2026 Research Projects
Projects are posted below; new projects will continue to be posted. To learn more about the type of research conducted by undergraduates, view the archived symposium booklets and search the past SURF projects.
This is a list of research projects that may have opportunities for undergraduate students. Please note that it is not a complete list of every SURF project. Undergraduates will discover other projects when talking directly to Purdue faculty.
You can browse all the projects on the list or view only projects in the following categories:
All Research Projects (257)
Characterization of Liquid Sprays in a Small-Scale Annular Gas Turbine
Description:
Liquid fuel injection plays a critical role in the performance of small-scale gas turbines. During ignition, the droplet size distribution of the injected fuel strongly influences combustion. This project focuses on quantifying and optimizing the spray distribution using a custom-built test rig designed to replicate realistic engine operating conditions. Experimental techniques such as backlit imaging and digital in-line holography are used to characterize the spray and particle size distributions.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
Desired experience:
• Be available to work in person over the summer.
• Have experience with MATLAB programming, including data post-processing and, ideally, parallel processing toolboxes.
• Be proficient with SolidWorks or similar CAD modeling and assembly creation software
• Have prior experience, or strong interest, in the design and construction and operation of fluid or experimental systems.
School/Dept.:
School of Mechanical Engineering
Professor:
Daniel
Guildenbecher
Experimental Solid Particle Erosion Testing
Description:
Sand and other particulate are unavoidably ingested into aeroengines operating in harsh environments. Within a compressor, Solid Particle Erosion increases tip losses and degrades aero performance. Current understanding of this erosion is derived from single particle impacts or rig-dependent empirical testing. The project will focus on assembling and running our ground-based test facility to study erosion at flight relevant conditions in Zucrow Labs Building 9. Possible research tasks include facility assembly, performance mapping, optical diagnostics, particle flow verification, CAD (NX), P&ID (Visio), component sourcing, coding (Python), etc.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
Desired experience:
• Possess a strong interest and desire for hands-on experimental activities
• Be available to work in person over the summer.
• Be proficient with SolidWorks or similar CAD modeling and assembly creation software
• Have prior experience, or strong interest, in the design and construction and operation of fluid or experimental systems.
School/Dept.:
School of Mechanical Engineering
Professor:
Daniel
Guildenbecher
A Foundation Model Approach to Crop Monitoring and Agroecosystem Analytics
Description:
The student will be involved in the following project tasks:
• Develop and evaluate a multimodal self-supervised representation learning pipeline for agricultural Earth observation.
• Integrate spectral (Sentinel-2), radar (Sentinel-1), and climate variables at the pixel level.
• Explore temporal prediction using next-embedding objectives and phenology-aware timing.
• Assess model performance under real-world agricultural conditions: sparse labels, irregular sampling, and sensor dropout.
• Demonstrate use for downstream agricultural tasks such as stress detection or yield-related proxies.
• Document technical work, results, and research outcomes in a final written report.
The selected student will work hands-on with multi-sensor satellite datasets to implement self-supervised learning methods and evaluate representation quality for agricultural applications. The student will experiment with spectral and radar data, integrate environmental variables, and assess generalization across temporal, spatial, and crop conditions. Additional activities include embedding visualization and interpretation, controlled experiments on sensor dropout and sparse temporal sampling, and communicating results through a poster /oral presentation and a technical report. The project is well-suited for students interested in machine learning, remote sensing, computer vision, geospatial analytics, or agricultural engineering.
• Develop and evaluate a multimodal self-supervised representation learning pipeline for agricultural Earth observation.
• Integrate spectral (Sentinel-2), radar (Sentinel-1), and climate variables at the pixel level.
• Explore temporal prediction using next-embedding objectives and phenology-aware timing.
• Assess model performance under real-world agricultural conditions: sparse labels, irregular sampling, and sensor dropout.
• Demonstrate use for downstream agricultural tasks such as stress detection or yield-related proxies.
• Document technical work, results, and research outcomes in a final written report.
The selected student will work hands-on with multi-sensor satellite datasets to implement self-supervised learning methods and evaluate representation quality for agricultural applications. The student will experiment with spectral and radar data, integrate environmental variables, and assess generalization across temporal, spatial, and crop conditions. Additional activities include embedding visualization and interpretation, controlled experiments on sensor dropout and sparse temporal sampling, and communicating results through a poster /oral presentation and a technical report. The project is well-suited for students interested in machine learning, remote sensing, computer vision, geospatial analytics, or agricultural engineering.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science and Artificial Intelligence, Computer Engineering
Desired experience:
▪ Exposure to deep learning concepts and experience with Python (NumPy, pandas, Pytorch etc.), HPC, Cuda and similar programming tools for data analysis or computational tasks.
▪ Be comfortable working with distributed systems and learning new software tools and working with structured/unstructured input/output data especially geospatial/remote sensing data.
▪ Be able to document results clearly and communicate findings effectively.
Applicants are expected to submit supporting evidence demonstrating alignment with the desirable skills listed above. Acceptable materials include coursework projects, GitHub repositories, technical reports, publications, or prior internship etc. that show experience with deep learning concepts, Python-based data analysis (e.g., NumPy, pandas, PyTorch), HPC or CUDA environments, and handling structured or unstructured data, particularly geospatial or remote sensing datasets. Submissions should also reflect the applicant’s ability to clearly document methods and results and communicate technical findings effectively.
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Dharmendra
Saraswat
AI and Human Factors in Healthcare
Description:
Student will research using real-time physical and cognitive load sensing to
develop semi-autonomous systems. These user-aware, adaptive
systems that can enhance human-human/ human-robot training and
performance
develop semi-autonomous systems. These user-aware, adaptive
systems that can enhance human-human/ human-robot training and
performance
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Human Factors, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Please review research project descriptions for relevant skills: https://engineering.purdue.edu/YuGroup
School/Dept.:
Edwardson School of Industrial Engineering
Professor:
Denny
Yu
More information: https://engineering.purdue.edu/IE/summer_intern./8
AI-Driven Multi-Scale Modeling for Advanced Electronic Packages
Description:
Project Description: Modern semiconductor packages are incredibly complex, containing tiny features (micrometers) inside much larger packages (millimeters). Simulating how these chips survive heat and physical stress using traditional physics-based models (FEA) is computationally slow and expensive. This project explores using Deep Learning computer vision models to predict stress and failure points much faster than traditional simulation methods.
Student Role & Responsibilities: Working alongside a graduate student mentor, the undergraduate researcher will:
1. Generate Simulation Data: Use MATLAB to run finite element simulations of chip structures to create a training dataset.
2. Develop AI Models: Assist in training and testing neural networks to interpret stress fields as images.
3. Analyze Results: Compare the AI predictions against ground-truth physics simulations to determine if AI can accurately predict mechanical failure in microelectronics.
Required Skills: Basic Python and MATLAB experience; interest in Finite Element Analysis and Machine Learning.
Student Role & Responsibilities: Working alongside a graduate student mentor, the undergraduate researcher will:
1. Generate Simulation Data: Use MATLAB to run finite element simulations of chip structures to create a training dataset.
2. Develop AI Models: Assist in training and testing neural networks to interpret stress fields as images.
3. Analyze Results: Compare the AI predictions against ground-truth physics simulations to determine if AI can accurately predict mechanical failure in microelectronics.
Required Skills: Basic Python and MATLAB experience; interest in Finite Element Analysis and Machine Learning.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Deep Learning, Material Modeling and Simulation, Microelectronics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Basic Python and MATLAB experience; interest in Finite Element Analysis and Machine Learning
School/Dept.:
School of Mechanical Engineering
Professor:
Ganesh
Subbarayan
AI-based detection of prognostic markers in malignant and benign laryngeal disease
Description:
Our team is offering SURF projects for 1-2 highly motivated students with an interest in clinical applications of AI in medicine and surgery and a strong background in either technical fields (i.e., Computer Science, Data Science, Electrical/Computer Engineering) and/or biomedical fields (i.e., Pre-medical Track, Biomedical Engineering).
Ideal candidates meet the following expectations:
* Basic programming skills (Python, pytorch, numpy)
* Interest in clinical applications of Artificial Intelligence
* Ability to work independently and in a team setting and communicate effectively
This SURF project will focus on the identification of predictive and prognostic biomarkers in patients with benign and malignant head and neck disease from a large clinical dataset encompassing tabular, imaging, audio, and video data. The SURF student(s) will work closely with a graduate mentor during the fellowship and will work on an independent/standalone sub-project.
General information about the lab:
The Translational Medical Image Computing (TMIC) Lab led by Dr. Kolbinger uses computational approaches to extract actionable knowledge from clinical datasets in the fields of surgery and interventional medicine. Our interdisciplinary research focuses on developing AI-based tools for surgery and evaluating these tools in early-phase clinical trials, thereby translating computational advancements into tangible patient benefits.
Ultimately, our goal is to translate technological innovations, such as Artificial Intelligence, into clinically valuable tools for medical decision support. By doing so, our research strives to personalize treatment approaches and make surgeries and medical care safer and more effective.
The lab's research addresses two key areas:
(1) Prediction of disease trajectories and outcomes: By predicting disease courses and outcomes from routinely available data acquired at earlier timepoints, we develop prediction models that help allocate the right treatment to the right patient at the right time to minimize complications and maximize patients' quality of life.
(2) Intraoperative decision support for increased safety and accuracy of minimally invasive surgeries: While computational image analysis is already clinically approved in fields like radiology or endoscopy, its application to surgical video has so far resulted in negligible patient benefit. Our team develops applications related to surgical safety and accuracy, for example visualization of vulnerable structures like blood vessels and nerves during surgeries and evaluates these systems in clinical routine. Our overarching goal is to integrate Artificial Intelligence-based image analysis into minimally invasive surgical systems and thereby set new standards for patient care.
The TMIC Lab currently comprises three graduate and five undergraduate researchers. Methodologies that all team members will interact with comprise advanced Artificial Intelligence methods for imaging and tabular data analysis, clinical imaging methods (i.e., MRI, CT, video data), advanced statistics, data visualization, conceptualization of and contribution to user studies and clinical research, and qualitative studies.
Ideal candidates meet the following expectations:
* Basic programming skills (Python, pytorch, numpy)
* Interest in clinical applications of Artificial Intelligence
* Ability to work independently and in a team setting and communicate effectively
This SURF project will focus on the identification of predictive and prognostic biomarkers in patients with benign and malignant head and neck disease from a large clinical dataset encompassing tabular, imaging, audio, and video data. The SURF student(s) will work closely with a graduate mentor during the fellowship and will work on an independent/standalone sub-project.
General information about the lab:
The Translational Medical Image Computing (TMIC) Lab led by Dr. Kolbinger uses computational approaches to extract actionable knowledge from clinical datasets in the fields of surgery and interventional medicine. Our interdisciplinary research focuses on developing AI-based tools for surgery and evaluating these tools in early-phase clinical trials, thereby translating computational advancements into tangible patient benefits.
Ultimately, our goal is to translate technological innovations, such as Artificial Intelligence, into clinically valuable tools for medical decision support. By doing so, our research strives to personalize treatment approaches and make surgeries and medical care safer and more effective.
The lab's research addresses two key areas:
(1) Prediction of disease trajectories and outcomes: By predicting disease courses and outcomes from routinely available data acquired at earlier timepoints, we develop prediction models that help allocate the right treatment to the right patient at the right time to minimize complications and maximize patients' quality of life.
(2) Intraoperative decision support for increased safety and accuracy of minimally invasive surgeries: While computational image analysis is already clinically approved in fields like radiology or endoscopy, its application to surgical video has so far resulted in negligible patient benefit. Our team develops applications related to surgical safety and accuracy, for example visualization of vulnerable structures like blood vessels and nerves during surgeries and evaluates these systems in clinical routine. Our overarching goal is to integrate Artificial Intelligence-based image analysis into minimally invasive surgical systems and thereby set new standards for patient care.
The TMIC Lab currently comprises three graduate and five undergraduate researchers. Methodologies that all team members will interact with comprise advanced Artificial Intelligence methods for imaging and tabular data analysis, clinical imaging methods (i.e., MRI, CT, video data), advanced statistics, data visualization, conceptualization of and contribution to user studies and clinical research, and qualitative studies.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biotechnology Data Insights, Deep Learning, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
We expect an interest in clinical applications of AI in medicine and surgery and a strong background in either technical fields (i.e., Computer Science, Data Science, Electrical/Computer Engineering) and/or biomedical fields (i.e., Pre-medical Track, Biomedical Engineering).
Ideal candidates meet the following expectations:
* Basic programming skills (Python, pytorch, numpy)
* Interest in clinical applications of Artificial Intelligence
* Ability to work independently and in a team setting and communicate effectively
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Fiona
Kolbinger
More information: https://fionakolbinger.github.io/
AI-driven smart monitoring and data analytics
Description:
This project is about sound-based and AI-driven smart monitoring of machine and systems. Students will learn about IoT and AI along with monitoring technology, robotics, and data analytics. Students will work with a graduate students for research.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Fabrication and Robotics, Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Industrial Engineering
- Mechanical Engineering
- Computer Engineering
- Computer Science
School/Dept.:
School of Mechanical Engineering
Professor:
Martin
Jun
More information: https://purduelamm.github.io/home/
AI-enabled Personalized Metabolic Digital Twin
Description:
The AI-enabled Personalized Metabolic Digital Twin represents a shift from reactive monitoring to proactive health management by creating a high-fidelity virtual replica of an individual’s unique metabolic processes. Unlike traditional fitness trackers that merely record past data, this platform leverages advanced AI to ingest complex, real-time physiological inputs and translate them into actionable, predictive guidance. By simulating the specific impacts of dietary, sleep, and exercise changes before they are implemented, the digital twin empowers both users and clinicians with the forward-looking insights necessary to intercept metabolic dysfunction and prevent chronic disease before it begins.
The student will participate in the AI model development, verification and validation of the developed model using the clinical datasets, and document the results into a journal publication.
The student will participate in the AI model development, verification and validation of the developed model using the clinical datasets, and document the results into a journal publication.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Desire to have taken machine learning, Python programming and linear algebra related courses.
School/Dept.:
School of Mechanical Engineering
Professor:
Guang
Lin
AI-guided Drug Development
Description:
The explosive growth of chemical data over the last two decades and the recent rise of artificial intelligence (AI) have converged, posing a revolution in drug discovery and development. One major hurdle, however, stems from the fact that most chemical data reside in a non-linear, high-dimensional manifold space, making it extremely difficult to extract chemical information mathematically and subsequently effectively utilize such information in machine learning and deep learning (ML/DL). Over the past few years, we have developed several novel concepts to effectively and accurately encode molecular information for predicting molecular properties. We have also developed generative ML/DL models to de novo-design drug molecules for achieving specific pharmacological effects. In particular, leveraging our unique interdisciplinary expertise in computational chemistry and mathematics, modeling and simulation, software development, and drug research, we have further expanded our research into drug development by integrating multi-omics information. In this SURF project, a student, ideally in chemistry, biochemistry, or biomedicinal engineering, with research experience in computational statistics and machine learning (Python, PyTorch, etc.), will work to implement manifold learning and utilize chemical data for designing drug molecules and products.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Preferred but not required: chemistry, biochemistry, or biomedicinal engineering majors with research experience in computational statistics and machine learning (Python, PyTorch, etc.)
School/Dept.:
Department of Industrial and Molecular Pharmaceutics
Professor:
Tonglei
Li
Accelerating RTL-to-GDSII Design Flows with Optimization and Active Learning on nanoHUB
Description:
Modern electronic design automation (EDA) workflows are essential for transforming high-level hardware descriptions (RTL) into manufacturable integrated circuit layouts (GDSII). However, these workflows are often slow and computationally expensive, requiring many design iterations to explore trade-offs between performance, power, and area. As a result, design space exploration remains a major bottleneck in chip design.
This project focuses on developing and evaluating an optimization loop around open EDA tools available on nanoHUB to accelerate RTL-to-GDSII workflows. The student will work with an end-to-end design flow deployed on nanoHUB and investigate how data-driven optimization and active learning techniques can reduce the number of required design iterations while maintaining or improving design quality.
The student will help construct an automated pipeline that runs EDA tools, collects design metrics (such as timing, power, and area), and uses those results to guide subsequent design choices. By leveraging nanoHUB’s computational infrastructure, data management capabilities, and openly available EDA tools, the project will demonstrate how iterative hardware design can be made faster, more systematic, and more reproducible.
This project focuses on developing and evaluating an optimization loop around open EDA tools available on nanoHUB to accelerate RTL-to-GDSII workflows. The student will work with an end-to-end design flow deployed on nanoHUB and investigate how data-driven optimization and active learning techniques can reduce the number of required design iterations while maintaining or improving design quality.
The student will help construct an automated pipeline that runs EDA tools, collects design metrics (such as timing, power, and area), and uses those results to guide subsequent design choices. By leveraging nanoHUB’s computational infrastructure, data management capabilities, and openly available EDA tools, the project will demonstrate how iterative hardware design can be made faster, more systematic, and more reproducible.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Microelectronics, System-on-a-Chip
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Electrical Engineering
- Computer Engineering
- Computer Science
- Engineering (First Year)
Desired experience:
▪ Experience with Python, MATLAB, or similar programming tools
▪ Interest in hardware design, EDA tools, or semiconductor workflows
▪ Interest in optimization, active learning, or data-driven design methods
▪ Comfort working with structured input/output data and computational pipelines
▪ Basic familiarity with Linux environments and version control (Git), or willingness to learn
▪ Ability to document results clearly and communicate findings effectively
School/Dept.:
School of Materials Engineering
Professor:
Alejandro
Strachan
Additive Manufacturing of Energetic Materials
Description:
The student will gain experience in additively manufacturing formulations relevant to energetic materials (i.e. propellants, pyrotechnics, explosives).
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
Desired experience:
Background in AM and/or polymer materials are beneficial.
School/Dept.:
School of Mechanical Engineering
Professor:
Monique
McClain
Additive Manufacturing to Fabricate Pharmaceutical Tablets
Description:
This project will demonstrate the use of Vibration Assisted Printing (VAP) to fabricate pharmaceutical tablets with tailored drug release profiles. VAP enables extrusion of extremely high-viscosity polymer–API formulations that are not accessible with conventional semi-solid extrusion methods, allowing greater formulation flexibility and geometric control. Purdue researchers will print proof-of-concept tablets with controlled infill, geometry, and multilayer compositions and evaluate their printing fidelity and release behavior. The results will establish VAP as a promising manufacturing approach for customizable modified-release pharmaceutical products.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Experience with additive manufacturing encouraged
School/Dept.:
School of Mechanical Engineering
Professor:
Steven
Son
More information: https://www.tandfonline.com/doi/full/10.3109/03639045.2015.1120743#d1e149
Adhesion Problems in Plant Cell Biomechanics
Description:
In the project a summer research project will contribute to a research group effort to advance the understanding of adhesion between plant cells in maintaining structural integrity of plant leaves. Such integrity is essential for plant survival.
Mechanical Engineering skills in mechanics of materials and finite element analysis are essential for the analysis of adhesion problems.
A student will use ImageJ for image processing and ABAQUS for model development and analysis.
Skills from this project will translate well to the mechanics of engineered cellular solids as common in packaging applications.
Mechanical Engineering skills in mechanics of materials and finite element analysis are essential for the analysis of adhesion problems.
A student will use ImageJ for image processing and ABAQUS for model development and analysis.
Skills from this project will translate well to the mechanics of engineered cellular solids as common in packaging applications.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
Desired experience:
Desired: ME 323, ME 489
School/Dept.:
School of Mechanical Engineering
Professor:
Thomas
Siegmund
More information: NA
Agentic AI in computational mechanics
Description:
The student will participate in the development of an Agentic AI system for computational mechanics that intends to automate finite element simulations.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Hector
Gomez
More information: https://engineering.purdue.edu/gomez/
Air Quality modeling: Incorporating stable isotopes into the US-EPA CMAQ air quality model
Description:
High performance computing is used to run sophisticated models, such as the EPA's CMAQ model, that predict air quality. These model, however, are difficult to evaluate because of limited observations. A growing set of observations of stable isotope abundances in atmospheric pollutants is available. However, current versions of CMAQ are not isotope enabled. The proposed research is to add stable isotopes in CMAQ and run simulations of the Negishi cluster at the Rosen Center for Advanced Computing (RCAC). Scripting language like bash or csh required. Some proficiency in R and/or python also helpful as well as use of ChatGPT.
Campus:
West Lafayette
Research categories:
Computer Architecture, Environmental Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
- Computer and Information Technology
- Mathematics - Computer Science
Desired experience:
Computer programming, UNIX/CMD line scripting, bash/csh, python, R
School/Dept.:
Earth, Atmospheric and Planetary Sciences
Professor:
Greg
Michalski
Application and impact of augmented reality on student learning and public engagement in engineering
Description:
A 30L Augment Reality (AR) virtual bioreactor developed in Unity needs to be optimized before implementing in the course. The student will test the AR virtual bioreactor using a Meta Quest 3 and improve it; assist literature review; locate and validate fermentation simulation data from literature and databases.
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology, Learning and Evaluation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
This project only opens to Purdue University students. Major/Minor includes STEM, education, psychology, or similar degrees, or high interest level in STEM education from other fields of study. The ideal student should be punctual, responsible, and responsive; strong at communication, writing, and data analysis skills; familiar with library database usage. Experience with literature review, biological process, and/or game development using Unity are preferred.
School/Dept.:
Division of Environmental and Ecological Engineering
Professor:
Xinyu
Zhang
Artificial Intelligence for Musicians
Description:
This project develops two mobile applications designed to support musicians:
(1) Music Score Follower and Error Detector
This app listens to live musical performance, tracks the performer’s location in the score in real time, and identifies performance errors such as incorrect notes, durations that are too long or too short, and other deviations. The development requires technologies including audio signal processing, user interface design, user experience evaluation, and mobile app engineering.
(2) Posture Recommender
This app analyzes video of musicians and provides personalized recommendations to improve playing posture. The development incorporates technologies such as computer vision, user interface design, user experience evaluation, and mobile app engineering.
(1) Music Score Follower and Error Detector
This app listens to live musical performance, tracks the performer’s location in the score in real time, and identifies performance errors such as incorrect notes, durations that are too long or too short, and other deviations. The development requires technologies including audio signal processing, user interface design, user experience evaluation, and mobile app engineering.
(2) Posture Recommender
This app analyzes video of musicians and provides personalized recommendations to improve playing posture. The development incorporates technologies such as computer vision, user interface design, user experience evaluation, and mobile app engineering.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Human Factors, Learning and Evaluation, Mobile Computing
Citizenship requirements:
No citizenship requirements, U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Computer Science
- Computer Engineering
- Computer and Information Technology
- Computer Engineering Technology
- Data Science
- Electrical Engineering
- Electrical Engineering Technology
Desired experience:
Programming, machine learning
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Yung-Hsiang
Lu
More information: https://ai4musicians.org/
Assessing Radiation-Drug Interactions
Description:
This project will evaluate how ionizing radiation interacts with various drugs with the intent to identify synergistic combinations Some drugs can augment the sensitivity of tissues to radiation, making them potent for combination with radiotherapy for treatment of cancer. Other drugs diminish the sensitivity of tissues to radiation, making them useful for protecting normal tissues from the harmful effects of radiation. This project will evaluate a variety of radiation regimens (including spatial and temporal fractionation) in combination with various drugs to determine the optimal combination for a given application. The student’s role on this project could include a variety of tasks, including quality assurance of the X-ray irradiator, measurements of delivered radiation dose in cells and tissues, and assessment of the biologic effects of radiation in combination with pharmaceuticals.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Health Sciences
Professor:
Matthew
Scarpelli
More information: https://hhs.purdue.edu/scarpellilab/
Atomistic modeling of radiation damage in semiconductors
Description:
Understanding how radiation interacts with materials is increasingly important for technologies operating in extreme environments such as space systems, nuclear energy, and radiation-hardened electronics. High-energy particles can create permanent atomic-scale damage that alters material performance in ways that are difficult to study experimentally. Molecular dynamics simulations provide a powerful approach for modeling these processes directly.
This project uses molecular dynamics simulations to study how high-energy nuclei interact with semiconductor and metallic materials, with a focus on the formation of permanent radiation damage. The work will examine how interfaces, defects, and other microstructural features influence damage creation and evolution, helping connect atomic-scale mechanisms to material behavior.
The student will play an active role in designing and running radiation damage simulations, defining simulation inputs, selecting material systems, and analyzing collision cascades and defect formation. Through this work, the student will develop practical skills in molecular dynamics, scientific programming, and data analysis while gaining experience working with modern computational research tools.
By the end of the project, the student will have hands-on experience in computational materials research and radiation damage modeling, contributing to efforts to design materials and electronic components that are more resilient to radiation.
This project uses molecular dynamics simulations to study how high-energy nuclei interact with semiconductor and metallic materials, with a focus on the formation of permanent radiation damage. The work will examine how interfaces, defects, and other microstructural features influence damage creation and evolution, helping connect atomic-scale mechanisms to material behavior.
The student will play an active role in designing and running radiation damage simulations, defining simulation inputs, selecting material systems, and analyzing collision cascades and defect formation. Through this work, the student will develop practical skills in molecular dynamics, scientific programming, and data analysis while gaining experience working with modern computational research tools.
By the end of the project, the student will have hands-on experience in computational materials research and radiation damage modeling, contributing to efforts to design materials and electronic components that are more resilient to radiation.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
- Aeronautical and Astronautical Engineering
- Mechanical Engineering
- Materials Engineering
Desired experience:
▪ Be available to work in person over the summer. ▪ Have experience with Python, or similar programming tools for data analysis or computational tasks. ▪ Be comfortable learning new software tools and working with structured input/output data. ▪ Have interest in workflow automation, scientific computing, or engineering simulations. ▪ Be able to document results clearly and communicate findings effectively. ▪ Experience with the LAMMPS software package is preferred.
School/Dept.:
School of Materials Engineering
Professor:
Alejandro
Strachan
More information: https://www.strachanlab.org/
Augmenting Manual Inspection Using Wearable VR/AR-Based Automated Visual Inspection
Description:
We will explore the potential of using Automated Visual Inspection (AVI) technologies to augment manual visual inspection. Emerging VR/AR eyewear, such as Meta’s Ray-Ban smart glasses, are lightweight and resemble conventional eyeglasses while enabling (1) real-time video streaming and recording from a perspective closely aligned with natural human vision, and (2) audio and visual feedback to the user. Because these devices closely resemble standard eyewear, they do not significantly interfere with inspectors’ normal workflow or visual performance.
Our objective is to investigate the feasibility of applying AVI algorithms to analyze video feeds captured by VR/AR glasses for defect detection. Locations of possible defects can then be communicated back to the human inspector in real time, thereby enhancing and augmenting existing manual inspection pipelines.
Detecting defects from human-captured video presents greater analysis challenges than from machine-captured imagery. Machine-acquired images are typically consistent in viewpoint, lighting, and positioning, whereas video captured by human inspectors can vary substantially due to natural movement and perspective changes. Furthermore, AVI processing must operate at sufficiently high speed to avoid slowing down the inspector’s workflow. Despite these challenges, successfully addressing these issues could significantly strengthen AVI capabilities while meaningfully enhancing human performance in manual inspection.
Activities will include recording data for positive and negative control samples during expert manual visual inspection under realistic workplace conditions, training and testing of agile machine learning architectures for defect identification, and development of augmented reality visualization architectures for supplementing manual human inspection.
Our objective is to investigate the feasibility of applying AVI algorithms to analyze video feeds captured by VR/AR glasses for defect detection. Locations of possible defects can then be communicated back to the human inspector in real time, thereby enhancing and augmenting existing manual inspection pipelines.
Detecting defects from human-captured video presents greater analysis challenges than from machine-captured imagery. Machine-acquired images are typically consistent in viewpoint, lighting, and positioning, whereas video captured by human inspectors can vary substantially due to natural movement and perspective changes. Furthermore, AVI processing must operate at sufficiently high speed to avoid slowing down the inspector’s workflow. Despite these challenges, successfully addressing these issues could significantly strengthen AVI capabilities while meaningfully enhancing human performance in manual inspection.
Activities will include recording data for positive and negative control samples during expert manual visual inspection under realistic workplace conditions, training and testing of agile machine learning architectures for defect identification, and development of augmented reality visualization architectures for supplementing manual human inspection.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
experience building augmented reality or VR systems, machine learning experience, computer programming
School/Dept.:
School of Materials Engineering
Professor:
Rahim
Rahimi
More information: https://younginstitute.research.purdue.edu/
Automated Characterization Platform for Beyond-Silicon "CMOS+X" AI Hardware Prototypes
Description:
I. Project Vision and Background
The rapid growth of artificial intelligence (AI), especially with large foundation models, is driving urgent demand for new, energy-efficient AI hardware built not only with advanced silicon CMOS, but also with next-generation transistor and memory device technologies in three-dimensional (3D) integrated "CMOS+X" systems, through monolithic 3D integration and advanced packaging. A key bottleneck in this exciting "lab-to-fab" transition to real-world impact is fast and robust characterization and testing of these CMOS+X dies, packaged chips, and multi-chip systems.
2. What Will You Do?
In Summer 2026, the SURF researcher will gain hands-on laboratory experience at Purdue’s Birck Nanotechnology Center, working with a National Instruments (NI) PXIe characterization system that integrates multiple modules (embedded FPGA, digital pattern instrument, and 10‑pA precision SMU). The student will help develop a comprehensive testing+analysis software framework to enable robust and high-performance testing and validation of AI hardware prototypes (silicon CMOS + emerging devices, with 3D integration).
3. Project Phases
Phase 1 (engineering foundation): Build an automated testing framework
- Develop a robust, reusable automation framework for chip characterization using the NI-PXIe characterization system.
- Contribute to PCB design/testing related tasks for the silicon and CMOS+X hardware prototypes from the team.
Phase 2 (exploratory): Build an LLM-powered AI copilot as the “Digital Twin”
- Explore APIs to build LLM-based agent/copilot with the automated testing workflow wrapped in its "sandbox".
- Explore this "digital twin" approach to accelerate testing, data analytics, and debugging.
The rapid growth of artificial intelligence (AI), especially with large foundation models, is driving urgent demand for new, energy-efficient AI hardware built not only with advanced silicon CMOS, but also with next-generation transistor and memory device technologies in three-dimensional (3D) integrated "CMOS+X" systems, through monolithic 3D integration and advanced packaging. A key bottleneck in this exciting "lab-to-fab" transition to real-world impact is fast and robust characterization and testing of these CMOS+X dies, packaged chips, and multi-chip systems.
2. What Will You Do?
In Summer 2026, the SURF researcher will gain hands-on laboratory experience at Purdue’s Birck Nanotechnology Center, working with a National Instruments (NI) PXIe characterization system that integrates multiple modules (embedded FPGA, digital pattern instrument, and 10‑pA precision SMU). The student will help develop a comprehensive testing+analysis software framework to enable robust and high-performance testing and validation of AI hardware prototypes (silicon CMOS + emerging devices, with 3D integration).
3. Project Phases
Phase 1 (engineering foundation): Build an automated testing framework
- Develop a robust, reusable automation framework for chip characterization using the NI-PXIe characterization system.
- Contribute to PCB design/testing related tasks for the silicon and CMOS+X hardware prototypes from the team.
Phase 2 (exploratory): Build an LLM-powered AI copilot as the “Digital Twin”
- Explore APIs to build LLM-based agent/copilot with the automated testing workflow wrapped in its "sandbox".
- Explore this "digital twin" approach to accelerate testing, data analytics, and debugging.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Big Data/Machine Learning, Computer Architecture, Deep Learning, Heterogeneous Integration, Microelectronics, Nanotechnology, System-on-a-Chip
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
1. Relevant curriculum and/or project training and background: semiconductors, VLSI, computer engineering, machine learning, automatic control.
2. Skillsets:
- Programming skills (e.g., Python, LabView)
- PCB board design and testing
- Prototyping experience with PCB/FPGA
- Software development
- LLM-based agentic workflow/framework development (with commercial tool API or open-source models)
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Haitong
Li
More information: https://engineering.purdue.edu/NanoX/
Automated Measurement System Developmnent for Chips and Wafers
Description:
In this project the student will be using python to create a platform for automated testing system of chips and wafer level device array. The goal is to control the probing instrument to move the prober to the right location of the chip and then control the measurement equipment (oscilloscope, parameter analyzer) to send the right signal and capture it, creating an interface that would show the data right away. The student will develop, debug and manage the code with some demonstration. They will develop automated electronic testing skills. They will learn to create large codebase with and without the help of standard coding LLM models.
Campus:
West Lafayette
Research categories:
Heterogeneous Integration, Microelectronics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering
- Computer Engineering
Desired experience:
Basic Electronic Circuits, some prior coding skills, some knowledge of semiconductor device is a plus
School/Dept.:
School of Materials Engineering
Professor:
Raisul
Islam
More information: https://engineering.purdue.edu/RISE-Lab
Automated Radio Evaluation Suite: Modular Testing Architecture
Description:
Next-generation communication systems demand precise, repeatable RF testing. Although commercial automation tools exist, they’re often out of reach for research labs, which end up using manual workflows that are slow and prone to error. The Automated Radio Evaluation Suite (ARES) addresses this gap with an open-source, MATLAB-based platform that automates power-amplifier and antenna characterization. The platform uses a set of DC power supplies, signal generators, signal analyzers, and vector network analyzers (VNA) to characterize devices. For amplifiers, it measures gain, efficiency, power, and linearity. For antennas, it supports return loss, absolute gain, and realized gain radiation-pattern measurements using a known reference or dual-antenna methods.
This work advances ARES from a function-oriented architecture where measurement logic was tightly coupled to specific instrument sets to an object-oriented design with abstract interfaces for instrument control. As VISA/SCPI commands are model-specific, using a modular architecture will allow for drop-in support of new instrument command sets without invasive code changes by encapsulating instruments in modular driver controllers. The new architecture potentially enables simulated instruments, bypassing VISA drivers when virtually testing and debugging new features. Collectively, these enhancements expand device compatibility and accelerate data collection and visualization.
This work advances ARES from a function-oriented architecture where measurement logic was tightly coupled to specific instrument sets to an object-oriented design with abstract interfaces for instrument control. As VISA/SCPI commands are model-specific, using a modular architecture will allow for drop-in support of new instrument command sets without invasive code changes by encapsulating instruments in modular driver controllers. The new architecture potentially enables simulated instruments, bypassing VISA drivers when virtually testing and debugging new features. Collectively, these enhancements expand device compatibility and accelerate data collection and visualization.
Campus:
West Lafayette
Research categories:
Microelectronics, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering
- Computer Engineering
Desired experience:
MATLAB, Python, RF engineering courses (optional), lab instrumentation
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Dimitrios
Peroulis
More information: https://aresapp.readthedocs.io/latest/home.html
Automation Methods for Gamma Ray Spectroscopy and Data Analysis
Description:
Gamma-ray spectroscopy is used by nuclear physicists to better understand the structure of the nucleus in excited states. By observing the decays from an excited nucleus, we can create level schemes that map allowed energy states and transitions. With this knowledge, we can identify specific energy states relevant to nuclear isomer battery applications. Despite their importance, level schemes are currently only constructed through manual analysis, limiting scalability and increased risk for systematic bias, which in turn limits ability for discovery. We present a fully automated framework that reframes level scheme construction as an image-to-graph translation problem. An end-to-end machine learning (ML) framework converts two-dimensional gamma-gamma coincidence matrices into structured nuclear level schemes with transitions, implemented using an autoregressive graph neural network (GRAN). The framework is supported by Monte Carlo simulation that inputs level transition data, benchmarking the ML solution. Hence, enabling generation of toy sample of level schemes to overcome limited availability of data for training and validation. The resulting level schemes serve as a benchmark for model performance prior to deployment on experimental data. Leveraging ML/AI allows increases in speed, accuracy and reach are bolstering the ability to discover nuclear isomer states of interest in transitions data.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Learning and Evaluation, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
- Physics
- Computer Science
- Nuclear Engineering
- Mathematics - Computer Science
School/Dept.:
Physics and Astronomy
Professor:
Andreas
Jung
More information: https://www.physics.purdue.edu/jung/
Battery safety for electrified aircraft
Description:
Battery fire safety research is critical for aviation electrification because aircraft operate under stringent safety, weight, and reliability constraints, where a single battery failure can have catastrophic consequences. High-energy-density batteries enable longer-range electric or hybrid-electric flight, but they also raise the risk of thermal runaway—a self-accelerating failure process in which heat, gas, and flammable materials are rapidly released. To study battery fire safety, our lab investigates thermal runaway mechanisms under various abuse conditions, such as mechanical damage (crush, puncture), electrical abuse (overcharge, over-discharge, short circuit), and thermal abuse (external heating, high ambient temperature). This involves controlled experiments on cells, modules, and packs to measure temperature, pressure, gas composition, and flame behavior, combined with high-speed imaging and diagnostic tools to capture failure progression. The experimental data are then used to develop and validate physics-based and computational models that predict when and how thermal runaway will occur, how it propagates between cells, and how effective different mitigation strategies are—such as improved cell design, thermal management, venting, fire suppression, and containment systems tailored for aircraft environments.
Campus:
West Lafayette
Research categories:
Energy and Environment
Citizenship requirements:
No citizenship requirements, U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
passional for aviation sustainability, hands-on in the lab is desired but not required, good team work spirit.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Li
Qiao
More information: qiaoresearchgroup.com
Behavior and analysis of structural connections under earthquake
Description:
Structural connections are a key component of buildings and bridges, responsible for safely transferring forces between structural elements. Their performance during extreme events such as earthquakes is critical to overall structural safety. This project offers undergraduate students a hands-on introduction to the design and evaluation of earthquake-resistant structural connections.
The student will work closely with graduate students, laboratory technicians, and faculty to develop experimental setups, fabricate test specimens, and conduct laboratory testing on small- and large-scale structural components. In parallel, the student will gain exposure to numerical modeling by assisting with finite element simulations to better understand load transfer and failure mechanisms.
The project emphasizes both technical and professional development. Students will present their work in a poster format and may have the opportunity to contribute as a co-author on a research publication, providing valuable preparation for graduate studies or industry careers.
The student will work closely with graduate students, laboratory technicians, and faculty to develop experimental setups, fabricate test specimens, and conduct laboratory testing on small- and large-scale structural components. In parallel, the student will gain exposure to numerical modeling by assisting with finite element simulations to better understand load transfer and failure mechanisms.
The project emphasizes both technical and professional development. Students will present their work in a poster format and may have the opportunity to contribute as a co-author on a research publication, providing valuable preparation for graduate studies or industry careers.
Campus:
West Lafayette
Research categories:
Engineering the Built Environment, Material Modeling and Simulation, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Lyles School of Civil Engineering
Professor:
Akanshu
Sharma
More information: https://www.akanshusharma.com/
Behavior of structural connections under earthquake
Description:
Structural connections are a key component of buildings and bridges, responsible for safely transferring forces between structural elements. Their performance during extreme events such as earthquakes is critical to overall structural safety. This project offers undergraduate students a hands-on introduction to the design and evaluation of earthquake-resistant structural connections.
The student will work closely with graduate students, laboratory technicians, and faculty to develop experimental setups, fabricate test specimens, and conduct laboratory testing on small- and large-scale structural components. In parallel, the student will gain exposure to numerical modeling by assisting with finite element simulations to better understand load transfer and failure mechanisms.
The project emphasizes both technical and professional development. Students will present their work in a poster format and may have the opportunity to contribute as a co-author on a research publication, providing valuable preparation for graduate studies or industry careers.
The student will work closely with graduate students, laboratory technicians, and faculty to develop experimental setups, fabricate test specimens, and conduct laboratory testing on small- and large-scale structural components. In parallel, the student will gain exposure to numerical modeling by assisting with finite element simulations to better understand load transfer and failure mechanisms.
The project emphasizes both technical and professional development. Students will present their work in a poster format and may have the opportunity to contribute as a co-author on a research publication, providing valuable preparation for graduate studies or industry careers.
Campus:
West Lafayette
Research categories:
Engineering the Built Environment, Material Modeling and Simulation, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Lyles School of Civil Engineering
Professor:
Akanshu
Sharma
More information: https://www.akanshusharma.com/
Beyond the Audiogram: Anatomical Changes Following Noise Exposure and Traumatic Brain Injury.
Description:
Hearing injuries (i.e., tinnitus and hearing loss) are the most common service-related
disabilities affecting over 4.5 million veterans. Service members often struggle to
understand speech in noisy environments despite having normal audiometric hearing
thresholds. Current diagnostics do not have the sensitivity required to detect subtle but
significant functional changes in the injured auditory system. Our project investigates
functional and structural changes in the peripheral and central auditory system following
noise exposure and traumatic brain injury (TBI) using a preclinical model (chinchilla
lanigera).
Functional auditory assessment has been previously performed in all animals. The
selected student is only expected to perform histological work. Histology may be
performed on brain and/or temporal bone (i.e., cochlea) that has been previously
collected. The selected student is expected to learn basic histological techniques—tissue
preparation, cryogenic sectioning, immunofluorescence staining, and multi-channel
confocal microscopy. Additionally, the student is expected to learn how to use stereology
software to quantify structural changes at the cellular level (e.g., cell death, synapse loss,
dendrite morphology, cell body, etc.). Overall, the selected student will help establish a
robust histological protocol that current and future students can easily implement to
support electrophysiological measures with histological markers. Combining functional
measures and histological markers will contribute to the development of more sensitive
diagnostics.
disabilities affecting over 4.5 million veterans. Service members often struggle to
understand speech in noisy environments despite having normal audiometric hearing
thresholds. Current diagnostics do not have the sensitivity required to detect subtle but
significant functional changes in the injured auditory system. Our project investigates
functional and structural changes in the peripheral and central auditory system following
noise exposure and traumatic brain injury (TBI) using a preclinical model (chinchilla
lanigera).
Functional auditory assessment has been previously performed in all animals. The
selected student is only expected to perform histological work. Histology may be
performed on brain and/or temporal bone (i.e., cochlea) that has been previously
collected. The selected student is expected to learn basic histological techniques—tissue
preparation, cryogenic sectioning, immunofluorescence staining, and multi-channel
confocal microscopy. Additionally, the student is expected to learn how to use stereology
software to quantify structural changes at the cellular level (e.g., cell death, synapse loss,
dendrite morphology, cell body, etc.). Overall, the selected student will help establish a
robust histological protocol that current and future students can easily implement to
support electrophysiological measures with histological markers. Combining functional
measures and histological markers will contribute to the development of more sensitive
diagnostics.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
1. Laboratory Basics: wet lab environments including pipetting and reagent
preparation.
2. Histology: tissue processing (fixing, embedding, sectioning, staining, mounting),
immunofluorescence, and confocal microscopy.
3. Analytical Skills: stereology and image analysis software (e.g., Neurolucida,
ImageJ), data management.
4. Communication: protocol development, written and oral communication.
5. Personal: self-motivated, open minded, and accountable
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Michael
Heinz
More information: https://engineering.purdue.edu/HeinzLab
Biofilm remediation using electric fields
Description:
We are building a quantitative, non-contact approach to reduce bacterial surface fouling in confined flow environments by applying carefully tuned, externally controlled physical cues. The project focuses on the earliest steps of surface attachment, i.e., before mature biofilms form, and asks a simple question: can we reliably bias bacteria away from “settle-and-stick” behavior using controllable stimuli, without relying on chemicals or coatings? We will develop repeatable assays, define operating regimes, and measure outcomes with straightforward metrics (attachment rate, coverage, and growth over time) in micro-scale test channels. The longer-term vision is a tunable, reagent-free anti-fouling strategy that can translate across biomedical and industrial settings.
Student role: The student will help design and run experiments, build/modify a small benchtop testbed, and develop analysis pipelines. They will gain skills in microfluidic handling, optical microscopy (time-lapse imaging), basic instrumentation/electronics integration, and quantitative data analysis in Python/Matlab (image processing, curve fitting, statistics), using standard lab tools plus automated acquisition and scripted analysis workflows.
Student role: The student will help design and run experiments, build/modify a small benchtop testbed, and develop analysis pipelines. They will gain skills in microfluidic handling, optical microscopy (time-lapse imaging), basic instrumentation/electronics integration, and quantitative data analysis in Python/Matlab (image processing, curve fitting, statistics), using standard lab tools plus automated acquisition and scripted analysis workflows.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology, Ecology and Sustainability, Fluid Modelling and Simulation, Medical Science and Technology, Nanotechnology, System-on-a-Chip
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Ashwin
Ramachandran
Biophysical and analytical characterization of ligand interactions with MYC Promoter G-quadruplex DNA for anti-cancer drug development.
Description:
The major focus of my research program is to characterize structures and functions of DNA G-quadruplexes. G-quadruplexes are non-canonical DNA secondary structures formed in specific regions of human genome with functional importance and have emerged as attractive anticancer drug targets. MYC, one of the most commonly deregulated genes in human cancers, has a DNA G4 motif in its promoter that functions as a transcriptional silencer. Compounds that bind to and stabilize the G-quadruplex formed in the MYC promoter have been shown to significantly lower MYC levels in cancer cells. Thus, the MYC promoter G-quadruplex (MycG4) represents a novel target for MYC inhibition by small molecules.
Project Description. Biophysical characterization of ligand interactions with MYC Promoter G-quadruplex DNA for anti-cancer drug development.
Expected Deliverables: 1) Measure binding affinity of G-quadruplex ligands to MycG4 DNA using fluorescence spectroscopy binding assay. 2) Measure effect of G-quadruplex ligands on MycG4 thermal stability using circular dichroism spectroscopy. 3) Record and organize the binding data and analyze the data in correlation with compound structures. Lorelei will work under the supervision of my graduate student, Jinho Jang and Rongxue Zhang.
Project Description. Biophysical characterization of ligand interactions with MYC Promoter G-quadruplex DNA for anti-cancer drug development.
Expected Deliverables: 1) Measure binding affinity of G-quadruplex ligands to MycG4 DNA using fluorescence spectroscopy binding assay. 2) Measure effect of G-quadruplex ligands on MycG4 thermal stability using circular dichroism spectroscopy. 3) Record and organize the binding data and analyze the data in correlation with compound structures. Lorelei will work under the supervision of my graduate student, Jinho Jang and Rongxue Zhang.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biotechnology Data Insights, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Chemistry major and related background and training; minimum 1 year lab experience.
School/Dept.:
Department of Medicinal Chemistry and Molecular Pharmacology
Professor:
Danzhou
Yang
Bladeless Mixing of Energetic Materials
Description:
This project focuses on the bladeless mixing of energetic materials, transitioning from a recipe-based framework to one centered on real-time process adjustment and quality by control. It emphasizes understanding the fundamental physics of mixing and addressing key mixing challenges. Additionally, it involves analyzing mixing progression and conducting performance testing.
Campus:
West Lafayette
Research categories:
Chemical Unit Operations, Material Processing and Characterization
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemical Engineering
- Chemistry
- Materials Engineering
- Mechanical Engineering
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Stephen
Beaudoin
Build the Future with AI: Robotics, Coding, and Security Projects
Description:
*** Desired experience: Strong coding skills and motivation in research are required. Background in deep learning, security, and natural language processing is not required but a plus.
*** Possible industry involvement: Some of these projects are funded by Meta/Facebook research awards and J.P.Morgan AI research awards.
*** We especially encourage applications from women, Aboriginal peoples, and other groups underrepresented in computing.
*** Project 1. Robotics Manipulation and Navigation, Vision-Language-Action and Reinforcement Learning
Vision-Language-Action (VLA) models have recently demonstrated strong capabilities in robotic manipulation by leveraging large-scale multimodal pretraining. However, applying reinforcement learning (RL) to improve these models remains computationally expensive.
In this project, we will develop a sample-efficient RL approach for VLA models, aiming to reduce training cost while improving policy robustness and generalization. Our ultimate goal is to enable scalable and efficient RL for VLA models in real-world robotic manipulation.
Early work and background can be found here:
SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models ICRA 2025 https://lt-asset.github.io/selp/
*** Project 2. Agents and LLMs for Sofware Engineering including Detecting and Fixing Software Bugs and Vulnerabilities
In this project, we will develop machine learning approaches including agents and large language models for the entire processes of software engineering, including requirements, design, code generation, test generation, code review, and detection and fixing of software bugs and security vulnerabilities. We will also build benchmarks for such coding and engineering tasks.
Early work and background can be found here:
Unified Software Engineering Agent as AI Software Engineer ICSE 2026: https://arxiv.org/pdf/2506.14683
Can Language Models Replace Programmers for Coding? 🐟 RepoCod Says 'Not Yet' ACL 2025 https://lt-asset.github.io/REPOCOD/
TENET: Leveraging Tests Beyond Validation for Code Generation https://arxiv.org/abs/2509.24148
Impact of Code Language Models on Automated Program Repair. ICSE 2023. https://www.cs.purdue.edu/homes/lintan/publications/clm-icse23.pdf
KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair. ICSE 2023. https://www.cs.purdue.edu/homes/lintan/publications/knod-icse23.pdf
*** Project 3. Binary Recovery and Foundation Models
Binary code analysis is the foundation of crucial security and development tasks, including legacy software maintenance, vulnerability detection, malware detection, and binary recovery. Combined with the sophistication of cybercrime that poses threats worldwide (e.g., cybercrime is predicted to cost $10.5 trillion annually by 2025), effective binary analysis techniques are in high demand. Existing models do not understand the syntax or semantics of binaries. The idea is to build binary foundation models considering syntaxes, compiler optimizations, hardware, etc. Our recent binary foundation model Nova is one of the first. Our CCS 2024 paper recovers data structures and identifier names from binaries, which could be useful for identifier recovery and renaming.
Our recent prior work and background can be found here:
https://www.cs.purdue.edu/homes/lintan/publications/nova-iclr25.pdf
https://www.cs.purdue.edu/homes/lintan/publications/resym-ccs24.pdf
https://www.cs.purdue.edu/homes/lintan/publications/gennm-ndss25.pdf
https://corebench.github.io/
*** Project 4. Autoformalization: Inferring Specifications from Software Text for Finding Bugs and Vulnerabilities
A fundamental challenge of detecting or preventing software bugs and vulnerabilities is understanding programmers’ intentions, formally called specifications. If we know the specification of a program (e.g., where a lock is needed, what input a deep learning model expects, etc.), a bug detection tool can check if the code matches the specification.
Building upon our expertise as being the first to extract specifications from code comments to automatically detect software bugs and bad comments, in this project, we will analyze various new sources of software textual information (such as API documents and StackOverflow Posts) to extract specifications for bug detection. For example, the API documents of deep learning libraries such as TensorFlow and PyTorch contain a lot of input constraint information about tensors. Language models may be explored.
Early work and background can be found here:
https://www.cs.purdue.edu/homes/lintan/projects.html
*** Possible industry involvement: Some of these projects are funded by Meta/Facebook research awards and J.P.Morgan AI research awards.
*** We especially encourage applications from women, Aboriginal peoples, and other groups underrepresented in computing.
*** Project 1. Robotics Manipulation and Navigation, Vision-Language-Action and Reinforcement Learning
Vision-Language-Action (VLA) models have recently demonstrated strong capabilities in robotic manipulation by leveraging large-scale multimodal pretraining. However, applying reinforcement learning (RL) to improve these models remains computationally expensive.
In this project, we will develop a sample-efficient RL approach for VLA models, aiming to reduce training cost while improving policy robustness and generalization. Our ultimate goal is to enable scalable and efficient RL for VLA models in real-world robotic manipulation.
Early work and background can be found here:
SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models ICRA 2025 https://lt-asset.github.io/selp/
*** Project 2. Agents and LLMs for Sofware Engineering including Detecting and Fixing Software Bugs and Vulnerabilities
In this project, we will develop machine learning approaches including agents and large language models for the entire processes of software engineering, including requirements, design, code generation, test generation, code review, and detection and fixing of software bugs and security vulnerabilities. We will also build benchmarks for such coding and engineering tasks.
Early work and background can be found here:
Unified Software Engineering Agent as AI Software Engineer ICSE 2026: https://arxiv.org/pdf/2506.14683
Can Language Models Replace Programmers for Coding? 🐟 RepoCod Says 'Not Yet' ACL 2025 https://lt-asset.github.io/REPOCOD/
TENET: Leveraging Tests Beyond Validation for Code Generation https://arxiv.org/abs/2509.24148
Impact of Code Language Models on Automated Program Repair. ICSE 2023. https://www.cs.purdue.edu/homes/lintan/publications/clm-icse23.pdf
KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair. ICSE 2023. https://www.cs.purdue.edu/homes/lintan/publications/knod-icse23.pdf
*** Project 3. Binary Recovery and Foundation Models
Binary code analysis is the foundation of crucial security and development tasks, including legacy software maintenance, vulnerability detection, malware detection, and binary recovery. Combined with the sophistication of cybercrime that poses threats worldwide (e.g., cybercrime is predicted to cost $10.5 trillion annually by 2025), effective binary analysis techniques are in high demand. Existing models do not understand the syntax or semantics of binaries. The idea is to build binary foundation models considering syntaxes, compiler optimizations, hardware, etc. Our recent binary foundation model Nova is one of the first. Our CCS 2024 paper recovers data structures and identifier names from binaries, which could be useful for identifier recovery and renaming.
Our recent prior work and background can be found here:
https://www.cs.purdue.edu/homes/lintan/publications/nova-iclr25.pdf
https://www.cs.purdue.edu/homes/lintan/publications/resym-ccs24.pdf
https://www.cs.purdue.edu/homes/lintan/publications/gennm-ndss25.pdf
https://corebench.github.io/
*** Project 4. Autoformalization: Inferring Specifications from Software Text for Finding Bugs and Vulnerabilities
A fundamental challenge of detecting or preventing software bugs and vulnerabilities is understanding programmers’ intentions, formally called specifications. If we know the specification of a program (e.g., where a lock is needed, what input a deep learning model expects, etc.), a bug detection tool can check if the code matches the specification.
Building upon our expertise as being the first to extract specifications from code comments to automatically detect software bugs and bad comments, in this project, we will analyze various new sources of software textual information (such as API documents and StackOverflow Posts) to extract specifications for bug detection. For example, the API documents of deep learning libraries such as TensorFlow and PyTorch contain a lot of input constraint information about tensors. Language models may be explored.
Early work and background can be found here:
https://www.cs.purdue.edu/homes/lintan/projects.html
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Cybersecurity, Deep Learning, Internet of Things (IoT), Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
- Computer Engineering
Desired experience:
*** Desired experience: Strong coding skills and motivation in research are required. Background in deep learning, security, and natural language processing is not required but a plus.
*** We especially encourage applications from women, Aboriginal peoples, and other groups underrepresented in computing.
School/Dept.:
Computer Science
Professor:
Lin
Tan
More information: https://lt-asset.github.io/
Building Agentic AI Systems for Mixed Reality Applications
Description:
This project explores an agentic AI system that integrates spatial computing with generative, multimodal interaction in mixed reality (MR) to enhance human capabilities in real-world contexts such as training, maintenance, and repair. The system enables users to interact with AI agents through language, gestures, and spatial context, allowing for more intuitive and adaptive assistance in complex physical tasks.
The undergraduate researcher will contribute to the design and development of MR applications that incorporate AI-driven interaction, including implementing interaction logic, prototyping user interfaces, and integrating AI models into real-time systems. The student will also assist in evaluating the system through user testing and iterative refinement.
Through this project, the student will gain hands-on experience in mixed reality development (e.g., Unity and Meta Quest), multimodal interaction design, and AI-powered systems, as well as exposure to research in human-computer interaction and intelligent interfaces.
The undergraduate researcher will contribute to the design and development of MR applications that incorporate AI-driven interaction, including implementing interaction logic, prototyping user interfaces, and integrating AI models into real-time systems. The student will also assist in evaluating the system through user testing and iterative refinement.
Through this project, the student will gain hands-on experience in mixed reality development (e.g., Unity and Meta Quest), multimodal interaction design, and AI-powered systems, as well as exposure to research in human-computer interaction and intelligent interfaces.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Human Factors, Mobile Computing
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Programming experience (e.g., Python, C#, or similar);
Familiarity with Unity or 3D development is a plus;
Interest in AR/VR, AI, or human-computer interaction.
School/Dept.:
School of Mechanical Engineering
Professor:
Karthik
Ramani
More information: https://engineering.purdue.edu/cdesign/wp/
Building Intelligent Composite Manufacturing Systems with Physics Models and Real-Time Data
Description:
The focus of the project is to develop physics-informed digital twin framework for advanced composites manufacturing by integrating high-fidelity physics-based simulations, data-driven models, and real-time sensing data. The student(s) will (1) Assist in developing simplified physics-based models to understand heat transfer and material behavior during composite manufacturing; (2) Help collect and analyze real-time process data (e.g., temperature, sensor signals) during manufacturing experiments; (3) Compare model predictions with experimental results to evaluate manufacturing quality; (4) Contribute to improving processing conditions for better material performance.
Skills the student will develop: (1) Fundamental understanding of composites manufacturing processes; (2) Basics of heat transfer and material behavior modeling; (3) Experience with data analysis and visualization; (4) Introduction to model–experiment integration and engineering problem solving.
Tools and technologies: (1) Programming tools such as MATLAB or Python for data analysis; (2) FEA and CFD simulation tools (e.g., Abaqus, ConvergeCFD)
(3) Experimental equipment and sensors (e.g., thermal cameras or in-situ monitoring systems); (4) Data processing and visualization tools
Skills the student will develop: (1) Fundamental understanding of composites manufacturing processes; (2) Basics of heat transfer and material behavior modeling; (3) Experience with data analysis and visualization; (4) Introduction to model–experiment integration and engineering problem solving.
Tools and technologies: (1) Programming tools such as MATLAB or Python for data analysis; (2) FEA and CFD simulation tools (e.g., Abaqus, ConvergeCFD)
(3) Experimental equipment and sensors (e.g., thermal cameras or in-situ monitoring systems); (4) Data processing and visualization tools
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Fabrication and Robotics, Fluid Modelling and Simulation, Material Modeling and Simulation, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Course work: AAE 352
Skills: Familiar with CAD modeling, CFD and FEA simulations
Experience in composites manufacturing and/or robotics is preferred
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Dianyun
Zhang
Building Ultra-Fast Edge AI Brains for Autonomous Robots
Description:
This project explores how autonomous robots can perform perception, decision-making, and control using distributed edge computing rather than cloud dependency. Students will build a modular edge AI stack integrating multiple sensors (e.g., RGB-D, IMU, LiDAR-lite, or UWB) with real-time fusion and local inference. The system will implement task scheduling, compute partitioning, and fail-safe autonomy under intermittent connectivity. Emphasis will be placed on deterministic latency, power-aware computation, and resilient autonomy pipelines. The final demonstration will showcase an autonomous robot performing navigation and task execution while dynamically adapting compute workloads across onboard processors and edge nodes. The project is explicitly designed to target publishable outcomes, with the goal of producing research suitable for submission to top-tier venues in computer vision and robotics (e.g., CVPR, ICCV, ECCV, ICRA, IROS).
Campus:
West Lafayette
Research categories:
Fabrication and Robotics, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Computer Engineering
- Computer Science
- Electrical Engineering
School/Dept.:
Computer Science
Professor:
Aniket
Bera
More information: https://ideas.cs.purdue.edu/
Building a novel database of 3D immune selection profiles of antigen surfaces from common pathogens
Description:
Immune selection plays a critical role in shaping the diversity structure of immunity-related genes in mammals, plants, and pathogens. Mammalian adaptive immunity dynamically diversifies receptor repertoires via V(D)J recombination to mount immediate reaction, while pathogens, in turn, employ antigenic variation for immune evasion without compromising protein fitness. This evolutionary tug-of-war leads to complex patterns where pathogens may simultaneously sustain multiple strains or, like influenza, cycle through haplotypes within antigenic sites in waves of dominant strains. The resulting evolutionary patterns of antigens thus reveal several distinctive signatures that can be used to identify immunodominant regions: 1) increased diversity around the target of selection, 2) alleles/haplotypes maintained at an intermediate frequency, 3) strong linkage disequilibrium among variant positions, and 4) trans-species polymorphism. Here, by testing and comparing different statistics, we plan to characterize the immunodominance patterns in human pathogens.
Undergraduate students are expected to help graduate students gather and curate antigen, taxon, and genetic resources for human pathogens, compute immune selection profiles across all antigen surfaces, and compare them with pathogens for which immunodominance information is known. They will develop skills in reading primary literature, data mining from protein and genetic databases, writing and running data analytical scripts. They will be using Pymol, R, and bash scripting.
Undergraduate students are expected to help graduate students gather and curate antigen, taxon, and genetic resources for human pathogens, compute immune selection profiles across all antigen surfaces, and compare them with pathogens for which immunodominance information is known. They will develop skills in reading primary literature, data mining from protein and genetic databases, writing and running data analytical scripts. They will be using Pymol, R, and bash scripting.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biology
Desired experience:
Trainings in immunology, genetics, basic programming capabilities
School/Dept.:
Biological Sciences
Professor:
Qixin
He
Building morphosyntactic networks for precision assessment and intervention
Description:
The goal of this project is to better understand the factors that contribute to difficulty using grammar in development language disorder (DLD). DLD is a language disorder that affects approximately 7% of the population and persists into adulthood, resulting in lifelong risks for poor biomedical, educational, and professional outcomes at tremendous cost to individuals and society. This multidisciplinary project aims to combine the latest advances in syntactic network science and natural language processing to build a new type of syntactic network for precision identification and intervention of grammatical impairments in DLD. This work will advance the field’s understanding of how children acquire and mentally represent grammar in both healthy and disordered language development. Student will participate in coding / development of tools that parse child language samples that will be used to build network models. Student will work with a senior member of the lab (PhD student).
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Medical Science and Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Preferred qualifications include: proficiency in R and/or Python, exposure to or interest in learning about network science, completion of at least one introductory level linguistics class, interest in grammar
School/Dept.:
Speech, Language, and Hearing Sciences
Professor:
Arielle
Borovsky
CISTAR: Deep Operator Networks for Secure and Efficient Chemical Engineering Application
Description:
Deep operator learning aims to replace repeated partial differential equations (PDE) and ordinary differential equations (ODE) solves with a learned solution operator, a map from input functions (for example initial and boundary conditions, forcing, material fields) to output functions (the resulting state field). DeepONet is a canonical architecture for this setting, pairing a branch network that encodes the input function via sensor evaluations with a trunk network that encodes the query location, enabling approximation of nonlinear operators with good generalization from relatively small training sets.
In chemical engineering, this is especially compelling for transport and reaction systems where classical simulation is accurate but too slow for many-query tasks (design, control, inference), and where stiffness is a dominant bottleneck. Recent work shows operator learning can surrogate stiff chemical kinetics propagators, using DeepONet variants to advance thermochemical states efficiently after offline training, with the explicit goal of accelerating reactive computational fluid dynamics (CFD) workflows.
Motivated by industrial-scale surrogate modeling efforts that emphasize efficiency on modern GPU stacks, this project will develop operator networks that are both computationally efficient and deployment-safe for chemical engineering models governed by PDEs.
The work plan centers on a rigorous data and evaluation pipeline, since recent editorial criticism has highlighted that claimed advantages of ML PDE surrogates depend strongly on realistic evaluation protocols and, often, much larger datasets. We will generate benchmark-quality training sets using established PDE data-generation practices and tooling and incorporate large, diverse simulation corpora where appropriate, including reaction–diffusion style systems. To improve “security” in the engineering sense of trustworthy decisions under uncertainty and limited high-fidelity data, we will pursue uncertainty-aware, multifidelity operator learning directions, and we will incorporate physics-inspired operator learning extensions to reduce sample complexity.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
In chemical engineering, this is especially compelling for transport and reaction systems where classical simulation is accurate but too slow for many-query tasks (design, control, inference), and where stiffness is a dominant bottleneck. Recent work shows operator learning can surrogate stiff chemical kinetics propagators, using DeepONet variants to advance thermochemical states efficiently after offline training, with the explicit goal of accelerating reactive computational fluid dynamics (CFD) workflows.
Motivated by industrial-scale surrogate modeling efforts that emphasize efficiency on modern GPU stacks, this project will develop operator networks that are both computationally efficient and deployment-safe for chemical engineering models governed by PDEs.
The work plan centers on a rigorous data and evaluation pipeline, since recent editorial criticism has highlighted that claimed advantages of ML PDE surrogates depend strongly on realistic evaluation protocols and, often, much larger datasets. We will generate benchmark-quality training sets using established PDE data-generation practices and tooling and incorporate large, diverse simulation corpora where appropriate, including reaction–diffusion style systems. To improve “security” in the engineering sense of trustworthy decisions under uncertainty and limited high-fidelity data, we will pursue uncertainty-aware, multifidelity operator learning directions, and we will incorporate physics-inspired operator learning extensions to reduce sample complexity.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Chemical Unit Operations, Fluid Modelling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Computer Science
- Chemical Engineering
- Computer Engineering
Desired experience:
Required:
•Experience with programming in the Python programming language or MATLAB
•Experience with machine learning libraries (e.g., scikit-learn, PyTorch, JAX)
•Course on differential equations
Preferred:
•Course on transport phenomena, fluid mechanics, or computational fluid dynamics
School/Dept.:
Davidson School of Chemical Engineering
Professor:
David
Bernal
More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=286478
CISTAR: Design of stable zeolite catalysts for fuel and chemical production
Description:
Zeolite catalysts are ubiquitous in the conversion of light hydrocarbons to useful chemical and transportation fuel products. However, protocols used to regenerate zeolite catalysts over repeated cycles leads to long-term structural changes that lead to irreversible catalyst deactivation. Recent work has shown that inorganic components and additives can be incorporated onto zeolite catalysts to stabilize them through the reaction and regeneration processes they encounter during operation. This project will work on developing post-synthetic modifications of zeolites to increase their structural stability, characterizing their surface and active site properties, and studying their catalytic reactivity and stability.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemical Engineering
- Chemistry
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Rajamani
Gounder
More information: https://sites.google.com/site/rgounder/
CISTAR: Examining how residual H2O (from CO2 conversion chemistry) influences methanol upgrading processes in zeolite catalysts
Description:
Recent collaborative work between the Gounder and Hibbitts groups showed how to synthesize zeolite catalysts that manipulate the location of the active site (an acid) to influence reaction rates, selectivities, and stabilities. This work focuses on methylating aromatics using methanol, which is an attractive platform chemical as it can be made from CO2 or biomass resources. The reaction produces water, and water is often a byproduct of methanol production (e.g., via CO2 hydrogenation), so understanding how water influences this reaction is critical to understanding how to leverage renewably sourced methanol. Students will learn how to synthesize zeolite catalysts and measure their performance for aromatic methylation with and without water co-feeds. Our goal is to determine how water alters the reaction mechanism and the structure-function relationships we have developed at water-free conditions. Optionally, students may also learn how to use density functional theory (DFT) approaches to study these reactions computationally by calculating activation barriers and reaction energies for mechanisms with and without water present.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemical Engineering
- Chemistry
Desired experience:
None, but reaction engineering is desirable.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
David
Hibbitts
More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=306454#research-interests-page
CISTAR: Gaining insights into polymer upcycling reactions using through model gas alkane hydrogenolysis
Description:
Plastics recycling suffers from the low demand of many common plastic wastes, such as polyethylene (grocery bags) and polypropylene (food containers), leading to plastics in landfills and waterways that can even breakdown into harmful microplastics that invade ecosystems. One method of polyolefin upcycling is hydrogenolysis: reacting polymer melts with H2 over a metal catalyst at high temperatures and pressures to produce a mixture of fuels and chemicals. Alkane hydrogenolysis has been extensively studied for small gaseous alkanes (e.g., ethane and isobutane), but differs from polymer hydrogenolysis as the latter takes place on different catalysts, at lower temperatures, and in a liquid hydrocarbon environment (the polymer melt) that will significantly alter the catalyst surface and the reaction behavior. In this work, we will “bridge the gap” between gaseous alkane model studies and liquid polymer studies by studying reactions using the same alkane in different environments (gas or liquid) by varying the alkane partial pressure above its saturation pressure (leading to condensation). Students interested in experimental catalysis will learn how to synthesize and characterize supported-metal catalysts (Ru, Ir, and Pt), and measure the rates of alkane hydrogenolysis at varying reaction conditions. These kinetic studies will answer key questions about polymer reactions that cannot be directly observed in polymer upcycling studies. Students interested in computational catalysis studies will develop kinetic Monte Carlo (KMC) simulations that will predict how polymers react, over time, during upcycling studies using input from model alkane kinetics and density functional theory (DFT) predictions.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemistry
- Chemical Engineering
Desired experience:
None, but reaction engineering is desirable.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
David
Hibbitts
More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=306454#research-interests-page
CISTAR: Modeling Palladium Speciation for Nanoparticle Synthesis and Hydrogen Sensor Development
Description:
Students will model the speciation of tetrachloropalladate in aqueous and ethanol-water solutions. They will learn how to use software and develop algorithms to solve systems of linear and nonlinear equations to follow the palladium species. The results from this model will be used to elucidate the reaction of palladium species during the formation of palladium nanoparticles. Subsequently, the palladium nanoparticles will be used in the development of robust hydrogen sensors.
Campus:
West Lafayette
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Michael
Harris
CISTAR: Modeling diffusion-limited reactions in zeolite crystals to advance crystallite development
Description:
Zeolite pores are so small that many molecules struggle to diffuse within zeolite crystals, leading to diffusion-limitations that can alter reaction rates, selectivities, and accelerate catalyst deactivation. To alleviate these limitations, zeolite crystals are often synthesized to be very small, or to be hierarchical such as “pillared” or “finned” structures, or to have large mesopores that cut across the crystal to facilitate diffusion. These methods of zeolite crystal design focus on optimizing the distances that molecules must diffuse within zeolite pores to reach catalytic active sites. However, few kinetic models can account for these or adequately predict these diffusion effects, because most kinetic models that we use are designed for isotropic materials with simple shapes (i.e., slab or cylindrical models) that do not resemble these zeolite crystals. Here, students will run kinetic Monte Carlo (KMC) simulations that predict catalyst performance, as a function of time, and can describe things like the concentrations of reactive intermediates within the pores. These KMC simulations can then be applied to zeolite crystals with varying size, shape, hierarchical features, and mesoporosity to optimize the crystal habit based on desired criteria (reaction rate, or selectivity, or stability). As a probe reaction of these tools, students will examine methanol-to-olefins reactions, a critically important reaction for producing olefins, potentially from non-fossil resources, as methanol can be produced from CO2 or biomass.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemical Engineering
- Chemistry
Desired experience:
None, but reaction engineering is desirable.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
David
Hibbitts
More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=306454#research-interests-page
CISTAR: Privacy-Preserving Machine Learning for Predicting Compressor Failures in Energy-Intensive Chemical Processes
Description:
Compressors are among the most energy-intensive pieces of equipment in the chemical industry, often accounting for a large fraction of total electricity consumption in refineries, petrochemical plants, and gas processing facilities. When compressors operate inefficiently or fail unexpectedly, they not only cause costly downtime and safety risks but also lead to significant energy waste and increased carbon emissions. Improving compressor reliability and efficiency is therefore critical for reducing the energy footprint of the chemical industry.
In this project, we propose using federated learning, a modern machine learning approach that enables collaborative model training without sharing raw industrial data, to predict compressor failures before they occur. Traditional machine learning methods require collecting large amounts of operational data in a centralized location, which is often impractical in industry due to data privacy, intellectual property concerns, and cybersecurity risks. Federated learning addresses this challenge by allowing each plant or company to train a local model on its own data and share only model updates rather than sensitive data itself.
By learning from diverse compressor operating conditions across multiple sites while preserving data privacy, the federated model can identify early warning signs of mechanical degradation, abnormal energy consumption, and impending failure. The outcome of this research will be a scalable, privacy-preserving framework for predictive maintenance that reduces unplanned shutdowns, improves energy efficiency, and supports CISTAR’s mission of advancing sustainable and reliable energy systems for the chemical industry.
Undergraduate researchers will gain hands-on experience at the intersection of energy systems, chemical engineering, and artificial intelligence, working on a real-world problem with direct impact on industrial sustainability and decarbonization.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
In this project, we propose using federated learning, a modern machine learning approach that enables collaborative model training without sharing raw industrial data, to predict compressor failures before they occur. Traditional machine learning methods require collecting large amounts of operational data in a centralized location, which is often impractical in industry due to data privacy, intellectual property concerns, and cybersecurity risks. Federated learning addresses this challenge by allowing each plant or company to train a local model on its own data and share only model updates rather than sensitive data itself.
By learning from diverse compressor operating conditions across multiple sites while preserving data privacy, the federated model can identify early warning signs of mechanical degradation, abnormal energy consumption, and impending failure. The outcome of this research will be a scalable, privacy-preserving framework for predictive maintenance that reduces unplanned shutdowns, improves energy efficiency, and supports CISTAR’s mission of advancing sustainable and reliable energy systems for the chemical industry.
Undergraduate researchers will gain hands-on experience at the intersection of energy systems, chemical engineering, and artificial intelligence, working on a real-world problem with direct impact on industrial sustainability and decarbonization.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Chemical Engineering
- Industrial Engineering
- Electrical Engineering
- Computer Science
Desired experience:
Some experience with programming in Python. Knowing machine learning, mathematical modeling, and optimization is a plus.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Can
Li
More information: https://cistar.us/
CISTAR: Synthesis of zeolite catalysts with tailored diffusion and reaction properties
Description:
Olefin oligomerization is a key step in light hydrocarbon gas upgrading routes to heavier molecular weight products. Acidic zeolites are an important class of materials to catalyze oligomerization reactions, but reaction rates and selectivities are influenced by coupled reaction-transport phenomena, and by the distribution of active sites within different pores of the material. This project will focus on synthesizing zeolite crystallites with tailored diffusion properties (e.g., crystal size and morphology, acid site distributions) to influence the rates and selectivities of olefin oligomerization.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
This project is offered by the NSF engineering research center CISTAR. Students working on this project will also participate in information sessions, tours, and informal mentoring with CISTAR's partner companies. Participants will join STEM teachers to offer two outreach events in Indianapolis in July, to help kids get excited about careers in STEM.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemical Engineering
- Chemistry
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Rajamani
Gounder
More information: https://sites.google.com/site/rgounder/
Catalyst Development for Asymmetric Hydroamination Reactions
Description:
This project focuses on the design and development of chiral cobalt-based catalysts for asymmetric hydroamination reactions, an important transformation for constructing enantioenriched amines that are widely found in pharmaceuticals, agrochemicals, and biologically active molecules.
Undergraduate researchers will play a central and hands-on role in this project. Students will be responsible for the design, synthesis, purification, and evaluation of chiral ligands used to generate enantioselective cobalt catalysts. Working closely with graduate mentors, students will first survey existing ligand frameworks and asymmetric catalyst precedents using chemical literature and structure databases (e.g., SciFinder and related chemical databases). Based on this analysis, they will propose new ligand structures aimed at improving catalytic activity and enantioselectivity.
Students will then develop synthetic routes to prepare enantiomerically pure ligands, carrying out multi-step organic synthesis, purification, and optimization. The prepared ligands will be coordinated to cobalt and evaluated in asymmetric hydroamination reactions, allowing students to directly connect molecular design with catalytic performance.
Throughout the project, students will gain extensive training in modern experimental techniques and data analysis, including:
• Reaction planning and retrosynthetic analysis
• Use of chemical literature and database searching tools
• Air- and moisture-sensitive synthesis (as appropriate)
• Purification techniques such as column chromatography and recrystallization
• Structural and purity characterization using NMR spectroscopy, UPLC–MS, HPLC, and GC–MS
• Analysis of enantioselectivity and reaction outcomes
By the end of the project, students will have developed strong technical proficiency in asymmetric synthesis and catalysis, as well as transferable skills in problem-solving, experimental design, data interpretation, and scientific communication. This experience will prepare them for advanced coursework, graduate research, or careers in the chemical sciences.
Undergraduate researchers will play a central and hands-on role in this project. Students will be responsible for the design, synthesis, purification, and evaluation of chiral ligands used to generate enantioselective cobalt catalysts. Working closely with graduate mentors, students will first survey existing ligand frameworks and asymmetric catalyst precedents using chemical literature and structure databases (e.g., SciFinder and related chemical databases). Based on this analysis, they will propose new ligand structures aimed at improving catalytic activity and enantioselectivity.
Students will then develop synthetic routes to prepare enantiomerically pure ligands, carrying out multi-step organic synthesis, purification, and optimization. The prepared ligands will be coordinated to cobalt and evaluated in asymmetric hydroamination reactions, allowing students to directly connect molecular design with catalytic performance.
Throughout the project, students will gain extensive training in modern experimental techniques and data analysis, including:
• Reaction planning and retrosynthetic analysis
• Use of chemical literature and database searching tools
• Air- and moisture-sensitive synthesis (as appropriate)
• Purification techniques such as column chromatography and recrystallization
• Structural and purity characterization using NMR spectroscopy, UPLC–MS, HPLC, and GC–MS
• Analysis of enantioselectivity and reaction outcomes
By the end of the project, students will have developed strong technical proficiency in asymmetric synthesis and catalysis, as well as transferable skills in problem-solving, experimental design, data interpretation, and scientific communication. This experience will prepare them for advanced coursework, graduate research, or careers in the chemical sciences.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Chemistry
Desired experience:
CHM26500, CHM26505, CHM26600, CHM26605
School/Dept.:
Chemistry
Professor:
Ming-Yu
Ngai
More information: https://www.ngaigroup.com/
Cellular basis for fibrotic remodeling after injury in skeletal muscle
Description:
In our lab, we investigate how various biological processes unfurl during wound healing after tissue injury, and how these processes can be improved using bioengineered therapies. For this research, we use a mouse model of mechanical injury to the skeletal muscle tissue, which results in either severe fibrosis if the injury is severe or scarless regeneration if the injury is minor.
The SURF project will involve identification of various cellular players involved in the wound healing process, through training and implementation of immunofluorescence staining, imaging, flow cytometry, and quantitative analysis of tissue structural remodeling. Through this project, the student will develop experience in regenerative therapies, cellular bioengineering, and extracellular matrix remodeling.
The SURF project will involve identification of various cellular players involved in the wound healing process, through training and implementation of immunofluorescence staining, imaging, flow cytometry, and quantitative analysis of tissue structural remodeling. Through this project, the student will develop experience in regenerative therapies, cellular bioengineering, and extracellular matrix remodeling.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
- Cell Molecular and Developmental Biology
- Biological Engineering - multiple concentrations
- Biochemistry (Biology)
Desired experience:
Strong background in biochemistry, bioengineering, cellular and molecular biology, tissue engineering, and /or developmental biology is preferred. Prior experience in a biomedical engineering or cell and molecular biology wet lab environment is preferred, but not required.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Taimoor
Qazi
More information: www.qazi-lab.com
Characterization of an effector involved in host-pathogen interaction
Description:
Description: The intracellular life cycle of the pathogenic bacteria Legionella pneumophila (L.p.) within a eukaryotic host is supported by the translocation of more than 330 effector proteins into the host cytosol. Modification of host targets via these effectors allows intracellular bacteria to remodel cellular processes relatively quickly and reversibly, when needed. This creates and maintains a replicative niche called the legionella-containing vacuole (LCV), essential for the survival of the pathogen. Legionella represents one of the most complex cases of cross talk between its eukaryotic host and the sheer number of effectors it translocates, making it a favoured system for studying the role of effectors in infection and the host signalling mechanism targeted by a given effector.
Adenosine Diphosphate (ADP) ribosylation is a reversible posttranslational modification involved in the regulation of numerous cellular processes. Enzymes that function as ADPribosyl (ADPr) transferases (ARTs) and/or ADPr glycohydrolases have been found in bacteria. These enzymes, through their coordinated activity of addition or removal of the ADPr group, are known to manipulate host target proteins. In silico analyses revealed the presence of another Legionella effector with putative ART activity. This effector, known as Lem26 (lpg2523), aligns to bacterial toxins like the cholera toxin and iota toxin. The project aims at characterizing the catalytic activity of this effector.
Campus: West Lafayette
Research Categories: Structure-function characterization
Preferred Major(s): Biochemistry (Department of Chemistry)
Desired experience: Be available to work in person in the lab over the summer
School/Dept.: Department of Chemistry
Professor: Chittaranjan Das
Adenosine Diphosphate (ADP) ribosylation is a reversible posttranslational modification involved in the regulation of numerous cellular processes. Enzymes that function as ADPribosyl (ADPr) transferases (ARTs) and/or ADPr glycohydrolases have been found in bacteria. These enzymes, through their coordinated activity of addition or removal of the ADPr group, are known to manipulate host target proteins. In silico analyses revealed the presence of another Legionella effector with putative ART activity. This effector, known as Lem26 (lpg2523), aligns to bacterial toxins like the cholera toxin and iota toxin. The project aims at characterizing the catalytic activity of this effector.
Campus: West Lafayette
Research Categories: Structure-function characterization
Preferred Major(s): Biochemistry (Department of Chemistry)
Desired experience: Be available to work in person in the lab over the summer
School/Dept.: Department of Chemistry
Professor: Chittaranjan Das
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Chittaranjan
Das
Characterize behavioral differences of infant Fmr1 KO rats across sleep and wake
Description:
This project will introduce an undergraduate student to quantitative behavioral analysis in a cutting-edge neuroscience context. The student will contribute to an ongoing Simons Foundation–funded study investigating how early sleep and movement patterns differ between Fmr1 knock-out (FXS model) and wild-type rats, and how these differences shape the development of cortical and brainstem motor circuits. A key component of this work involves analyzing high-speed video recordings of infant rats across sleep–wake states to characterize spontaneous twitches and wake movements—behaviors that provide crucial sensory input to the developing motor system.
The student’s primary role will be to use DeepLabCut, an open-source machine learning toolkit for markerless pose estimation, to track the kinematics of limb and body movements from neonatal rat videos. They will assist in generating training datasets, refining labeling accuracy, and running trained networks to extract movement trajectories. Through this process, the student will gain hands-on experience with machine learning pipelines, data curation, video analysis, and reproducible research practices in Python. They will also develop skills in behavioral quantification, dataset management, and interpreting movement features in the context of early brain development.
By producing high-quality movement-tracking datasets, the student will play an essential role in evaluating how knocking out Fmr1 alters the developmental organization of sleep-related and wake-related movements—key behavioral readouts that inform downstream neural analyses in motor cortex and red nucleus.
The student’s primary role will be to use DeepLabCut, an open-source machine learning toolkit for markerless pose estimation, to track the kinematics of limb and body movements from neonatal rat videos. They will assist in generating training datasets, refining labeling accuracy, and running trained networks to extract movement trajectories. Through this process, the student will gain hands-on experience with machine learning pipelines, data curation, video analysis, and reproducible research practices in Python. They will also develop skills in behavioral quantification, dataset management, and interpreting movement features in the context of early brain development.
By producing high-quality movement-tracking datasets, the student will play an essential role in evaluating how knocking out Fmr1 alters the developmental organization of sleep-related and wake-related movements—key behavioral readouts that inform downstream neural analyses in motor cortex and red nucleus.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology, Genetics, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Comfort working with and handling laboratory rats (infant and juvenile).
Basic experience with Python; familiarity with scientific libraries (NumPy, Pandas, Jupyter) is helpful.
Attention to detail and willingness to perform careful frame-by-frame behavioral scoring.
Interest in developmental neuroscience, sleep, or computational methods.
School/Dept.:
Biological Sciences
Professor:
James
Dooley
More information: www.dooleylab.com
Characterizing multi-scale viscoelasticity in de novo pericellular matrix of chondrocyte-seeded hydrogels
Description:
In this EMBRIO-related project, the participant will be analyzing load-displacement curves from different mechanical loading systems to evaluate the mechanical behavior and estimate the viscoelastic properties of cells within their pericellular environment. The participant will learn to culture chondrocytes, which are cells that derive from cartilage, and seed them into hydrogels for aseptic cell culture. The participant will learn about multiple mechanical testing systems including benchtop materials characterization using digital image correlation and atomic force microscopy. The participant will learn and apply mathematical concepts related to the relationship between stress and strain in a material, or the constitutive relationship, including for elastic and viscoelastic materials. They will use computational tools to estimate best fits for the parameters that define these relationships and enable comparison of different experimental conditions that may alter the biomechanical behavior of chondrocytes and the components that it synthesizes around them.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
- Mechanical Engineering
- Biological Engineering - multiple concentrations
Desired experience:
Engineering design, Mechanics of materials, Computing and coding for engineers, Differential equations and linear algebra
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Deva
Chan
More information: engineering.purdue.edu/ChanLab
Characterizing the injectability and rheological behavior of biomaterials for tissue repair
Description:
Injectable hydrogels permit minimally invasive delivery of therapies for tissue repair and regeneration. Granular hydrogels are an emerging class of injectable biomaterials that have many attractive features including inherent porosity, tunable mechanics, and modular design. Granular hydrogels are assembled through the packing of hydrogel microparticles. Microparticle properties play a consequential role in determining overall hydrogel properties.
In this SURF project, the goal is to use a microfluidics approach to create microparticles with varying features including size and shape, and investigating how changes in these features impact hydrogel porosity and rheological behavior.
The student will perform hydrogel fabrication using microfluidic devices and characterization of porosity, rheological behavior and mechanics. The student will learn how to use soft lithography to make microfluidic devices, how to operate these devices to make microparticles, and how to process and characterize the properties of these biomaterials.
In this SURF project, the goal is to use a microfluidics approach to create microparticles with varying features including size and shape, and investigating how changes in these features impact hydrogel porosity and rheological behavior.
The student will perform hydrogel fabrication using microfluidic devices and characterization of porosity, rheological behavior and mechanics. The student will learn how to use soft lithography to make microfluidic devices, how to operate these devices to make microparticles, and how to process and characterize the properties of these biomaterials.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cardiovascular Disease Research, Cellular Biology, Composite Materials and Alloys, Fabrication and Robotics, Material Modeling and Simulation, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Prior experience in materials characterization, fabrication, biological testing, and knowledge of biomechanics, mechanical properties of materials etc. is preferred but not required.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Taimoor
Qazi
More information: www.qazi-lab.com
Cochlea and brain histology to index plasticity of the lateral olivocochlear efferent system
Description:
This project aims to index sound-induced plasticity of the lateral olivocochlear efferent system by comprehensively characterizing neurotransmitter expression in neuron cell bodies and cochlear terminals. The student will learn to process cochlea and brain tissue samples using gross and microdissection, immunolabeling, and whole mount (cochlea) and tissue clearing (brain) techniques. The student will then obtain low magnification fluorescence images and high magnification confocal images of the tissue samples. The student will analyze images in FIJI (open source computer-based) and SyGlass (virtual reality) softwares to characterize lateral olivocochlear neuron neurotransmitter expression profiles before and after noise exposure. Pending interest and time, the student may also gain exposure to in vivo experimental components (sound exposures, auditory brainstem responses, behavioral testing).
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Coursework in Neuroscience, Biology, Biomedical Engineering, and/or Hearing Science recommended.
Experience with tissue processing, dissections, fluorescence and confocal imaging, and image analysis are desired, but not required.
School/Dept.:
Speech, Language, and Hearing Sciences
Professor:
Jane
Mondul
Collective Brownian motion under temperature fields
Description:
We are studying the Brownian motion of many kinds of micro and nanoparticles (polymer, metal, silicon, etc) in solution, with or without temperature fields. We will analyze the motion of these particles and how they interact with temperature gradients we created.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Material Processing and Characterization, Nanotechnology, Thermal Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Materials Engineering
- Electrical Engineering
School/Dept.:
School of Mechanical Engineering
Professor:
Jingang
Li
More information: https://www.jingangli.org/
Compliant Robots for Contact-Enabled Detumbling and Capturing of Space Objects
Description:
This project will develop and experimentally validate a contact-enabled detumbling approach that uses an origami-inspired soft manipulator with built-in energy absorption/compliance to safely interact with tumbling, non-cooperative space targets.
The student will contribute to the design, prototyping, and experimental testing of an origami-inspired compliant manipulator for safely interacting with tumbling, non-cooperative space objects. Expected tasks include designing manipulator concepts with targeted compliance and energy-absorption performance, building and refining prototypes, conducting experiments on an air-bearing low-friction table, and analyzing contact, detumbling, and capture behavior. Through the project, the student will develop skills in space robotics, compliant mechanism design, prototyping, dynamics and control, and experimental data analysis, while gaining experience with CAD tools, 3D printing, sensing and actuation hardware, motion tracking, and microgravity-inspired robotic testbeds.
The student will contribute to the design, prototyping, and experimental testing of an origami-inspired compliant manipulator for safely interacting with tumbling, non-cooperative space objects. Expected tasks include designing manipulator concepts with targeted compliance and energy-absorption performance, building and refining prototypes, conducting experiments on an air-bearing low-friction table, and analyzing contact, detumbling, and capture behavior. Through the project, the student will develop skills in space robotics, compliant mechanism design, prototyping, dynamics and control, and experimental data analysis, while gaining experience with CAD tools, 3D printing, sensing and actuation hardware, motion tracking, and microgravity-inspired robotic testbeds.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Robotics Engineering Technology
- Aeronautical and Astronautical Engineering
- Mechanical Engineering
Desired experience:
Experience with robotics design, system integration, and 3D printing. Applicants are expected to include a description of prior experience in robotics-relevant research and products/results.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Ran
Dai
More information: https://engineering.purdue.edu/AOL/research
Computational Investigation of the Actin Cytoskeleton in Plant Cells
Description:
The actin cytoskeleton consists of a network with highly dynamic actin filaments, playing diverse roles in both animal and plant cells. In particular, the actin cytoskeleton in plant cells is known to be rearranged on timescales of seconds to minutes to respond to biotic or abiotic stimuli. This rearrangement is mediated by various types of actin binding proteins that can modulate the dynamic behaviors of actin filaments. Due to the intrinsic complexity of the system, it is hard to illuminate the mechanism of dynamic rearrangement of the actin cytoskeleton observed in the plant actin cytoskeleton, only using experiments. In this project, we will use a combination of continuum and discrete models to find how the actin cytoskeleton in plant cells is rearranged dynamically and precisely.
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology, Cellular Biology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
Desired experience:
Coding skill, data analysis
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Taeyoon
Kim
More information: https://engineering.purdue.edu/mct
Computational Modeling and AI for Biological Systems
Description:
This project focuses on developing computational and AI-driven tools to analyze biological imaging data and model cellular signaling processes. Undergraduate researchers will work on projects such as image segmentation, calcium signaling analysis, geometric modeling of tissues, and parameter inference in biological systems. Students will gain hands-on experience in Python programming, machine learning, and scientific computing while working with real experimental datasets from zebrafish and other model systems.
Depending on their interests, students may develop graphical user interfaces (GUIs), evaluate and compare computational methods, or build models to simulate biological processes such as BMP–Smad signaling and tissue dynamics. The program emphasizes reproducible research, code development, and interdisciplinary collaboration. Students will present their work through reports, software tools, and a final presentation at the end of the program.
Depending on their interests, students may develop graphical user interfaces (GUIs), evaluate and compare computational methods, or build models to simulate biological processes such as BMP–Smad signaling and tissue dynamics. The program emphasizes reproducible research, code development, and interdisciplinary collaboration. Students will present their work through reports, software tools, and a final presentation at the end of the program.
Campus:
Indianapolis
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Cellular Biology, Deep Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
David
Umulis
Computational Modeling of Interface Usability
Description:
Human-computer interaction (HCI) practitioners and researchers study how to make computing interfaces easier to use, more accessible, and visually appealing. Assessing these factors often relies on tacit expert knowledge that is difficult to share and apply widely. Many emerging practices in software development (e.g., agent-based "vibe" coding) and training machine learning models (e.g., benchmarking and training objectives) require interface evaluation to be automated. Our project applies machine learning to build computational models to measure interface usability.
We are interested in a investigating several directions. These include: i) evaluating and training code generation models ii) evaluating and training computer use agents (CUA) models, and iii) building tools for designers and developers based on AI models.
Students will learn and apply skills in the scientific research process including project ideation, literature search, project discussions, experimental design/execution, data analysis, and results presentation. Different technologies that may be helpful, depending on eventual project direction may be useful: Python, JavaScript, React.js, coding agents, HuggingFace Transformers, PyTorch, and Docker. Prior experience with these is appreciated but not required, and students will develop their technical implementation skills. Students must have experience or interest in human-computer interaction and machine learning.
We are interested in a investigating several directions. These include: i) evaluating and training code generation models ii) evaluating and training computer use agents (CUA) models, and iii) building tools for designers and developers based on AI models.
Students will learn and apply skills in the scientific research process including project ideation, literature search, project discussions, experimental design/execution, data analysis, and results presentation. Different technologies that may be helpful, depending on eventual project direction may be useful: Python, JavaScript, React.js, coding agents, HuggingFace Transformers, PyTorch, and Docker. Prior experience with these is appreciated but not required, and students will develop their technical implementation skills. Students must have experience or interest in human-computer interaction and machine learning.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Human Factors
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Programming knowledge in Python is highly encouraged
Fundamental knowledge of machine learning concepts is highly encouraged
Experience or interest in human-computer interaction and machine learning
Experience or interest in conducting academic research
School/Dept.:
Computer Science
Professor:
Jason
Wu
More information: https://ciderlab.org/
Computer vision and image segmentation of erosive particle breakup behavior under dynamic impact conditions
Description:
Our lab has a highly specialized sub-millimeter gas gun for examining solid particle erosion behavior in aeroengines under Mach 1-2 impact conditions. We have collected high-speed image sequences of the breakup behavior, and we need to develop algorithms to reconstruct the breakup behavior.
Students will use ImageJ, Python, MATLAB, and image segmentation algorithms to generate displacement/velocity fields via high-speed images of ballistic impact. Understanding how to implement image registration may be necessary to generate 3D visualization of impact
Students will use ImageJ, Python, MATLAB, and image segmentation algorithms to generate displacement/velocity fields via high-speed images of ballistic impact. Understanding how to implement image registration may be necessary to generate 3D visualization of impact
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Composite Materials and Alloys, Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Computer Science
- Mathematics - Computer Science
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Zherui
Guo
Contractile Behaviors of the Actin Cytoskeleton
Description:
Cells need intracellular forces for their physiological functions, such as migration, cytokinesis, and morphogenesis. The actin cytoskeleton generates a large fraction of the forces via interactions between cytoskeletal components, such as actin filament, myosin, and cross-linking protein. Myosin II plays the most important role in cellular force generation. Myosin II molecules self-assemble into filaments with different structures depending on myosin II isoforms and other conditions such as pH and ionic concentration. It has remained elusive how the contractile behaviors of the actin cytoskeleton are affected by the architecture of myosin II filaments. In this study, we will employ a computational model to investigate the effects of the structural properties of myosin II filaments on the contractility of the actin cytoskeleton.
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology, Cellular Biology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
- Mechanical Engineering
Desired experience:
Coding skill and data analysis
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Taeyoon
Kim
More information: https://engineering.purdue.edu/mct
Contribution of roots vs. shoots to locally adaptive cold acclimation in Arabidopsis thaliana
Description:
The genetic basis of local adaptation (greater relative fitness of local genotypes compared to foreign genotypes) is a central focus of evolutionary biology. We have identified a genetic variant underlying differential freezing tolerance that contributes to local adaptation in natural populations of Arabidopsis thaliana from northern (Sweden) and southern (Italy) range edges in Europe. Cold acclimation, an adaptive plastic response that increases freezing tolerance, is impaired in the Italian population by a loss-of-function mutation in the transcription factor CBF2. The resulting loss of freezing tolerance reduces survival of the Italian population when grown in the Swedish environment. In the Italian environment, where cold acclimation is induced but freezing does not occur, a functional CBF2 in the Swedish population incurs a fitness cost of about 20%. The exact mechanisms behind these environmentally-dependent costs and benefits of CBF2 expression are not known, but recent research suggests that the CBF family of transcription factors is differentially expressed across plant tissues, with markedly stronger induction in roots than above-ground organs in response to cold. Differential induction of cold acclimation across tissues could be broadly relevant in the face of warming winters, as soil temperatures in northern latitudes are paradoxically projected to decrease due to loss of insulating snow cover.
This project seeks to understand the relative contributions of roots vs. shoots to benefits and costs associated with CBF2-mediated cold acclimation in an ecologically relevant study system. By measuring freezing tolerance and traits putatively linked to fitness effects associated with cold acclimation, the student will partition contributions of roots and shoots to fitness trade-offs in order to draw conclusions about future plant responses to fluctuating soil temperatures as winters warm. The student will learn protocols for root imaging (WinRHIZO root imaging system), measurement of osmotic potential using an osmometer, and the electrolyte leakage method for assaying freezing tolerance. Basic data collection and analysis techniques will also be taught using the statistical software JMP.
This project seeks to understand the relative contributions of roots vs. shoots to benefits and costs associated with CBF2-mediated cold acclimation in an ecologically relevant study system. By measuring freezing tolerance and traits putatively linked to fitness effects associated with cold acclimation, the student will partition contributions of roots and shoots to fitness trade-offs in order to draw conclusions about future plant responses to fluctuating soil temperatures as winters warm. The student will learn protocols for root imaging (WinRHIZO root imaging system), measurement of osmotic potential using an osmometer, and the electrolyte leakage method for assaying freezing tolerance. Basic data collection and analysis techniques will also be taught using the statistical software JMP.
Campus:
West Lafayette
Research categories:
Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Botany and Plant Pathology
Professor:
Christopher
Oakley
More information: https://btny.purdue.edu/labs/oakley/
Controlled stimulus delivery from hydrogels for tissue repair
Description:
This project will develop biomaterials for controlled delivery of physiological stimuli for tissue repair applications. The student will gain skills and learn techniques in hydrogel synthesis and fabrication, microfluidics, drug encapsulation and measurement of release kinetics, and testing of drug bioactivity using in vitro tests.
For more information, visit our website: www.qazi-lab.com
For more information, visit our website: www.qazi-lab.com
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Cardiovascular Disease Research, Cellular Biology, Composite Materials and Alloys, Material Processing and Characterization, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Prior wet lab experience in materials fabrication, testing, cell culture, or animal experimentation is preferred but not required.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Taimoor
Qazi
More information: www.qazi-lab.com
Convolutional Neural Network for Thermal Image Analysis
Description:
Combustion processes are central to energy production and propulsion systems, but their complex flame dynamics (governed by interactions of temperature, flow fields, and emissivity) make detailed analysis challenging. Advances in high-resolution thermal and optical imaging now enable precise measurements of flame characteristics, yet manual interpretation of this data remains slow, subjective, and limited in scalability. Deep learning offers a way to automate and accelerate this analysis by identifying key patterns and features that describe combustion behavior.
This project focuses on developing a Convolutional Neural Network (CNN) capable of analyzing both raw optical and thermal flame images to automatically extract and quantify combustion-relevant features. Building on recent work applying neural networks to flame diagnostics, the project aims to design and implement an image-analysis model that improves classification accuracy and physical interpretability.
In this project, the student will participate in dataset exploration, model architecture design, and training of CNNs using labeled experimental thermal imaging data. The student will evaluate model performance a testing dataset, visualize feature activations, and refine processing pipelines for thermal image analysis.
Through this work, the student will gain hands-on experience with machine learning for scientific imaging, including data handling, neural network implementation in Python, and performance benchmarking. They will also develop an understanding of how AI can support combustion diagnostics by reducing human effort and improving reliability.
By the end of the project, the student will have contributed to the development of an AI-enabled diagnostic tool for combustion research, while gaining practical skills in deep learning, image analysis, and scientific computing applicable across engineering and applied sciences.
This project focuses on developing a Convolutional Neural Network (CNN) capable of analyzing both raw optical and thermal flame images to automatically extract and quantify combustion-relevant features. Building on recent work applying neural networks to flame diagnostics, the project aims to design and implement an image-analysis model that improves classification accuracy and physical interpretability.
In this project, the student will participate in dataset exploration, model architecture design, and training of CNNs using labeled experimental thermal imaging data. The student will evaluate model performance a testing dataset, visualize feature activations, and refine processing pipelines for thermal image analysis.
Through this work, the student will gain hands-on experience with machine learning for scientific imaging, including data handling, neural network implementation in Python, and performance benchmarking. They will also develop an understanding of how AI can support combustion diagnostics by reducing human effort and improving reliability.
By the end of the project, the student will have contributed to the development of an AI-enabled diagnostic tool for combustion research, while gaining practical skills in deep learning, image analysis, and scientific computing applicable across engineering and applied sciences.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Material Processing and Characterization, Thermal Technology
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Computer Engineering
- Computer Science
- Materials Engineering
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
- Engineering (First Year)
Desired experience:
▪ Be available to work in person over the summer. ▪ Have experience with Python, or similar programming tools for data analysis or computational tasks. ▪ Be comfortable learning new software tools and working with structured input/output data. ▪ Have interest in workflow automation, scientific computing, or engineering simulations. ▪ Be able to document results clearly and communicate findings effectively.
Professor:
Alejandro
Strachan
More information: https://www.strachanlab.org/
Convolutional Neural Network for Thermal Image Analysis
Description:
Combustion processes are central to energy production and propulsion systems, but their complex flame dynamics (governed by interactions of temperature, flow fields, and emissivity) make detailed analysis challenging. Advances in high-resolution thermal and optical imaging now enable precise measurements of flame characteristics, yet manual interpretation of this data remains slow, subjective, and limited in scalability. Deep learning offers a way to automate and accelerate this analysis by identifying key patterns and features that describe combustion behavior.
This project focuses on developing a Convolutional Neural Network (CNN) capable of analyzing both raw optical and thermal flame images to automatically extract and quantify combustion-relevant features. Building on recent work applying neural networks to flame diagnostics, the project aims to design and implement an image-analysis model that improves classification accuracy and physical interpretability.
In this project, the student will participate in dataset exploration, model architecture design, and training of CNNs using labeled experimental thermal imaging data. The student will evaluate model performance a testing dataset, visualize feature activations, and refine processing pipelines for thermal image analysis.
Through this work, the student will gain hands-on experience with machine learning for scientific imaging, including data handling, neural network implementation in Python, and performance benchmarking. They will also develop an understanding of how AI can support combustion diagnostics by reducing human effort and improving reliability.
By the end of the project, the student will have contributed to the development of an AI-enabled diagnostic tool for combustion research, while gaining practical skills in deep learning, image analysis, and scientific computing applicable across engineering and applied sciences.
This project focuses on developing a Convolutional Neural Network (CNN) capable of analyzing both raw optical and thermal flame images to automatically extract and quantify combustion-relevant features. Building on recent work applying neural networks to flame diagnostics, the project aims to design and implement an image-analysis model that improves classification accuracy and physical interpretability.
In this project, the student will participate in dataset exploration, model architecture design, and training of CNNs using labeled experimental thermal imaging data. The student will evaluate model performance a testing dataset, visualize feature activations, and refine processing pipelines for thermal image analysis.
Through this work, the student will gain hands-on experience with machine learning for scientific imaging, including data handling, neural network implementation in Python, and performance benchmarking. They will also develop an understanding of how AI can support combustion diagnostics by reducing human effort and improving reliability.
By the end of the project, the student will have contributed to the development of an AI-enabled diagnostic tool for combustion research, while gaining practical skills in deep learning, image analysis, and scientific computing applicable across engineering and applied sciences.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Material Processing and Characterization, Thermal Technology
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Computer Science
- Materials Engineering
- Aeronautical and Astronautical Engineering
- Mechanical Engineering
- Engineering (First Year)
- Computer Engineering
Desired experience:
▪ Be available to work in person over the summer. ▪ Have experience with Python, or similar programming tools for data analysis or computational tasks. ▪ Be comfortable learning new software tools and working with structured input/output data. ▪ Have interest in workflow automation, scientific computing, or engineering simulations. ▪ Be able to document results clearly and communicate findings effectively.
School/Dept.:
School of Materials Engineering
Professor:
Alejandro
Strachan
More information: https://www.strachanlab.org/
Creating VR Experiences for Geoscience Informal and Formal Learning
Description:
Field trips are a core part of geological science education, offering hands-on learning. However, physical and financial constraints can make field trip sites less accessible. To address this, we are developing a VR experience program that will allow students in the geosciences to have access to field sites that otherwise would not be possible. An example of a location that we currently do not visit in our EAPS curriculum is cave settings: they are hard to navigate and require expensive equipment for exploration. Motivated by this, the first experience designed under our VR program will be focused on caves. Specifically, we plan on developing a VR experience that will allow students from EAPS 353 - Earth and Planetary Surface Processes to have an authentic field trip experience inside of VR cave without the difficulties associated with the exploring cave settings while reaching the learning objectives of the course. We also have an on-going partnership with the Indiana Department of Natural Resources to extend our current formal learning work to the informal space. Students in this project will help further develop this VR experience while integrating pedagogical elements into it.
Campus:
West Lafayette
Research categories:
Ecology and Sustainability, Learning and Evaluation, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Computer Science
- Planetary Sciences
- Geology and Geophysics
School/Dept.:
Department of Computer and Information Technology
Professor:
Anastasia
Kouvaras Ostrowski
Creating VR Experiences for Geoscience Informal and Formal Learning
Description:
Field trips are a core part of geological science education, offering hands-on learning. However, physical and financial constraints can make field trip sites less accessible. To address this, we are developing a VR experience program that will allow students in the geosciences to have access to field sites that otherwise would not be possible. An example of a location that we currently do not visit in our EAPS curriculum is cave settings: they are hard to navigate and require expensive equipment for exploration. Motivated by this, the first experience designed under our VR program will be focused on caves. Specifically, we plan on developing a VR experience that will allow students from EAPS 353 - Earth and Planetary Surface Processes to have an authentic field trip experience inside of VR cave without the difficulties associated with the exploring cave settings while reaching the learning objectives of the course. We also have an on-going partnership with the Indiana Department of Natural Resources to extend our current formal learning work to the informal space. Students in this project will help further develop this VR experience while integrating pedagogical elements into it.
Campus:
West Lafayette
Research categories:
Ecology and Sustainability, Learning and Evaluation, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Earth, Atmospheric and Planetary Sciences
Professor:
Caue
Borlina
Damping Characteristics of Additively Manufactured Tennis Racket Handle Pallets
Description:
Tennis players are very particular about their racket, spending time and energy customizing every aspect of it to best suit their needs. Previous literature has found vibration through the handle to be one of the key contributors to preferences amongst tennis players. One aspect of racket that can be altered is the size of the grip which is done by changing a part called the pallet. While pallets of varying sizes can be purchased from manufacturers, an additively manufactured pallet presents a unique opportunity to provide customized fit and tuned vibration at the same time.
Within this project you would work to create a model, perform simulations, manufacture and test prototypes. Throughout this project you will have the opportunity to use CAD software, simulation software, and multiple 3D printing technologies (FDM, SLA).
Within this project you would work to create a model, perform simulations, manufacture and test prototypes. Throughout this project you will have the opportunity to use CAD software, simulation software, and multiple 3D printing technologies (FDM, SLA).
Campus:
West Lafayette
Research categories:
Human Factors, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Materials Engineering
Desired experience:
MATLAB/Python, CAD
School/Dept.:
School of Materials Engineering
Professor:
Jan-Anders
Mansson
More information: https://engineering.purdue.edu/Engr/Ewry
Damping Characteristics of Additively Manufactured Tennis Racket Handle Pallets
Description:
Tennis players are very particular about their racket, spending time and energy customizing every aspect of it to best suit their needs. Previous literature has found vibration through the handle to be one of the key contributors to preferences amongst tennis players. One aspect of racket that can be altered is the size of the grip which is done by changing a part called the pallet. While pallets of varying sizes can be purchased from manufacturers, an additively manufactured pallet presents a unique opportunity to provide customized fit and tuned vibration at the same time.
Within this project you would work to create a model, perform simulations, manufacture and test prototypes. Throughout this project you will have the opportunity to use CAD software, simulation software, and multiple 3D printing technologies (FDM, SLA).
Within this project you would work to create a model, perform simulations, manufacture and test prototypes. Throughout this project you will have the opportunity to use CAD software, simulation software, and multiple 3D printing technologies (FDM, SLA).
Campus:
West Lafayette
Research categories:
Human Factors, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Mechanical Engineering
- Materials Engineering
Desired experience:
MATLAB/Python, CAD
School/Dept.:
School of Materials Engineering
Professor:
Jan-Anders
Mansson
More information: https://engineering.purdue.edu/Engr/Ewry
Data & Participant Engagement in Digital Phenotyping of Early Development in Neurogenetic Conditions
Description:
The undergraduate research assistant will play an active role in supporting ongoing human subjects research focused on behavioral and physiological data collection. The student will assist with participant communication, enrollment, scheduling, and compensation tracking, gaining experience in research coordination and ethical human subjects’ practices.
The student will prepare and manage assessment materials and contribute to data processing tasks, including coding behavioral and physiological data; segmenting videos; conducting fidelity coding; and cleaning legacy datasets.
Through this role, the student will develop skills in research methodology, human subjects compliance, data management, behavioral coding, attention to detail, and reproducible research practices. The student will gain hands-on experience with tools such as behavioral coding software, physiological data processing platforms, eye-tracking analysis systems, spreadsheet/database management software (e.g., Excel), and video editing software.
The student will prepare and manage assessment materials and contribute to data processing tasks, including coding behavioral and physiological data; segmenting videos; conducting fidelity coding; and cleaning legacy datasets.
Through this role, the student will develop skills in research methodology, human subjects compliance, data management, behavioral coding, attention to detail, and reproducible research practices. The student will gain hands-on experience with tools such as behavioral coding software, physiological data processing platforms, eye-tracking analysis systems, spreadsheet/database management software (e.g., Excel), and video editing software.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Psychological Sciences
Professor:
Bridgette
Kelleher
More information: https://kelleherlab.weebly.com/
Deformation in alloys and high entropy alloys
Description:
We invite participation in a research project focused on advancing predictive modeling of deformation mechanisms in next-generation Ni-based superalloys. This project leverages Phase Field Dislocation Dynamics (PFDD) to capture dislocation–precipitate interactions with significantly improved physical fidelity compared to traditional models.
Participants will investigate:
• The formation and evolution of extended dislocations in both matrix and precipitate phases
• Dislocation–precipitate interactions and precipitate shearing mechanisms
• The development of planar faults within precipitates
This work aims to deliver mechanistic insight and predictive capability for strengthening in complex alloy systems, supporting the design of higher-performance materials for extreme environments.
Participants will investigate:
• The formation and evolution of extended dislocations in both matrix and precipitate phases
• Dislocation–precipitate interactions and precipitate shearing mechanisms
• The development of planar faults within precipitates
This work aims to deliver mechanistic insight and predictive capability for strengthening in complex alloy systems, supporting the design of higher-performance materials for extreme environments.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Familiar with FEM and/or other computer simulations
School/Dept.:
School of Mechanical Engineering
Professor:
Marisol
Koslowski
More information: https://koslowskigroup.org
Design and Control of a Bipedal Walking Character
Description:
This project will involve the creation and deployment of a small bipedal robot. The student will be expected to assemble and design the mechanical structural components of the robot, interface with the electronics of the system, and deploy a reinforcement learning policy to control the robot to follow teleoperator commands. The student will need to know and learn CAD, electronic prototyping, basic robotics, and RL training and inference.
Campus:
West Lafayette
Research categories:
Deep Learning, Fabrication and Robotics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Computer Science
Professor:
Zachary
Kingston
More information: https://commalab.org
Design and Prototyping of a Portable Birdcage Antenna System for Non-Invasive Neuromodulation Applications in Alzheimers Disease
Description:
Undergraduate students will participate in the design, fabrication, and testing of a portable RF birdcage antenna system for non-invasive neuromodulation research with applications to Alzheimer’s disease. Students will work across the full engineering pipeline, including antenna construction and tuning, electronics and sensor integration, embedded system programming, and system-level testing. The project emphasizes hands-on prototyping and experimental validation in an interdisciplinary setting spanning electrical, biomedical, and mechanical engineering.
Through this project, students will develop skills in RF and antenna characterization, embedded systems and microcontrollers (C/C++, Python, MATLAB), data acquisition and feedback control, electromagnetic and thermal simulation (HFSS/COMSOL/ANSYS), and CAD-based mechanical design with 3D printing. All work will be conducted at the benchtop level using simulation, prototyping, and controlled testing only, with possible human or animal subjects involved.
Through this project, students will develop skills in RF and antenna characterization, embedded systems and microcontrollers (C/C++, Python, MATLAB), data acquisition and feedback control, electromagnetic and thermal simulation (HFSS/COMSOL/ANSYS), and CAD-based mechanical design with 3D printing. All work will be conducted at the benchtop level using simulation, prototyping, and controlled testing only, with possible human or animal subjects involved.
Campus:
Indianapolis
Research categories:
Biological Characterization and Imaging, Energy and Environment, Medical Science and Technology, Radiation Hardening
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering
- Mechanical Engineering
- Biomedical Engineering
Desired experience:
Students should have an interest in hands-on engineering research and be willing to learn across disciplines. Preferred (but not required) background includes basic coursework or experience in circuits and electronics, programming (C/C++, Python, or MATLAB), or signals and systems. Familiarity with CAD tools, electromagnetic or thermal simulation software (e.g., HFSS, COMSOL, ANSYS), or embedded systems/microcontrollers is a plus. Prior experience with prototyping, soldering, sensors, or 3D printing.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Haitham
Kanakri
More information: https://ieeexplore.ieee.org/abstract/document/10962220
Design and fabrication untethered soft robots
Description:
Soft robots have gained interest due to their ability to interact with their environment, adapt to external stimuli, and protect against external disturbances[1], [2], [3]. The inherent safety provided by the characteristic low modulus of materials used in soft robots allows them to perform tasks that are nearly impossible for their rigid counterparts[4]. The interplay between soft mechanics and controls intrinsic to soft robotics gives rise to innovative solutions for tasks ranging from simple grasping to complex manipulation and locomotion [5], [6], [7]. However, this interplay also poses challenges, complicating their modeling and control. These challenges include their infinite dimensionality, material nonlinearity, and large deformations that most soft robots exhibit [4]. As a result, sensory systems[8] and complex models/algorithms[9], [10] are required to implement closed-loop control, leading to computationally demanding models to represent the robot's behavior.
Physical nonlinearities in soft robotic architectures have provided a solution to address existing limitations by utilizing the robots' structure for physical control [11]. Elements such as snap-through instabilities, rate dependencies, and responsiveness to external stimuli have been integrated into soft robotics architectures to improve performance, functionality, and embodied control. By leveraging the system's mechanical response, various outputs can be encoded into the system through logic operations [11], [12], [13], alternating cycles [14], object size classification [15], [16], and an overall enhancement in robot performance [16]. However, such robotic architecture currently lacks autonomy and often demonstrate low performance when untethered. A significant factor contributing to this issue is the added mass from pneumatic systems, which typically constitutes the majority of the robot's structure. This work aims to explore, analyze, and design various pneumatic actuation systems to be integrated into soft robotic architectures, facilitating their untethered operation.
Among the existing architectures, we will analyze self-regulating oscillators [17], sequence-driven actuators [18], and pneumatic logic systems [12], [19], [20], offering insights into various soft logic and control devices and how to incorporate them into larger soft machines or systems.
References
[1] P. Polygerinos et al., “Soft Robotics: Review of Fluid-Driven Intrinsically Soft Devices; Manufacturing, Sensing, Control, and Applications in Human-Robot Interaction,” Adv. Eng. Mater., vol. 19, no. 12, p. 1700016, Dec. 2017, doi: 10.1002/ADEM.201700016.
[2] S. Kim, C. Laschi, and B. Trimmer, “Soft robotics: a bioinspired evolution in robotics,” Trends Biotechnol., vol. 31, no. 5, pp. 287–294, May 2013, doi: 10.1016/J.TIBTECH.2013.03.002.
[3] C. Majidi, “Soft-Matter Engineering for Soft Robotics,” Adv. Mater. Technol., vol. 4, no. 2, Feb. 2019, doi: 10.1002/ADMT.201800477.
[4] D. Rus and M. T. Tolley, “Design, fabrication and control of soft robots,” Nature, vol. 521, no. 7553, pp. 467–475, May 2015, doi: 10.1038/nature14543.
[5] C. Laschi, B. Mazzolai, and M. Cianchetti, “Soft robotics: Technologies and systems pushing the boundaries of robot abilities,” Sci. Robot., vol. 1, no. 1, p. eaah3690, Dec. 2016, doi: 10.1126/scirobotics.aah3690.
[6] R. Pfeifer, M. Lungarella, and F. Iida, “The challenges ahead for bio-inspired ‘soft’ robotics,” Commun. ACM, vol. 55, no. 11, pp. 76–87, Nov. 2012, doi: 10.1145/2366316.2366335.
[7] D. Trivedi, C. D. Rahn, W. M. Kier, and I. D. Walker, “Soft Robotics: Biological Inspiration, State of the Art, and Future Research,” Appl. Bionics Biomech., vol. 5, no. 3, pp. 99–117, 2008, doi: 10.1080/11762320802557865.
[8] R. L. Truby et al., “Soft Somatosensitive Actuators via Embedded 3D Printing,” Adv. Mater., vol. 30, no. 15, p. 1706383, Apr. 2018, doi: 10.1002/ADMA.201706383.
[9] K. Chin, T. Hellebrekers, and C. Majidi, “Machine Learning for Soft Robotic Sensing and Control,” Adv. Intell. Syst., vol. 2, no. 6, p. 1900171, June 2020, doi: 10.1002/AISY.201900171.
[10] T. G. Thuruthel, B. Shih, C. Laschi, and M. T. Tolley, “Soft robot perception using embedded soft sensors and recurrent neural networks,” Sci. Robot., vol. 4, no. 26, p. eaav1488, Jan. 2019, doi: 10.1126/scirobotics.aav1488.
[11] E. Milana, C. D. Santina, B. Gorissen, and P. Rothemund, “Physical control: A new avenue to achieve intelligence in soft robotics,” Sci. Robot., 2025.
[12] S. Conrad et al., “3D-printed digital pneumatic logic for the control of soft robotic actuators,” Sci. Robot., vol. 9, no. 86, p. eadh4060, Jan. 2024, doi: 10.1126/scirobotics.adh4060.
[13] Y. Zhai et al., “Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits,” Sci. Robot., vol. 8, no. 79, p. eadg3792, June 2023, doi: 10.1126/scirobotics.adg3792.
[14] D. Drotman, S. Jadhav, D. Sharp, C. Chan, and M. T. Tolley, “Electronics-free pneumatic circuits for controlling soft-legged robots,” Sci. Robot., vol. 6, no. 51, p. 2627, Feb. 2021, doi: 10.1126/SCIROBOTICS.AAY2627/SUPPL_FILE/AAY2627_SM.PDF.
[15] S. Zou, S. Picella, J. De Vries, V. G. Kortman, A. Sakes, and J. T. B. Overvelde, “A retrofit sensing strategy for soft fluidic robots,” Nat. Commun., vol. 15, no. 1, p. 539, Jan. 2024, doi: 10.1038/s41467-023-44517-z.
[16] J. C. Osorio, J. S. Rincon, H. Morgan, and A. F. Arrieta, “Embodying Control in Soft Multistable Robots from Morphofunctional Co‐design,” Adv. Sci., p. e03206, July 2025, doi: 10.1002/advs.202503206.
[17] L. C. Van Laake, J. De Vries, S. Malek Kani, and J. T. B. Overvelde, “A fluidic relaxation oscillator for reprogrammable sequential actuation in soft robots,” Matter, vol. 5, no. 9, pp. 2898–2917, Sept. 2022, doi: 10.1016/j.matt.2022.06.002.
[18] B. Van Raemdonck, E. Milana, M. De Volder, D. Reynaerts, and B. Gorissen, “Nonlinear Inflatable Actuators for Distributed Control in Soft Robots,” Adv. Mater., p. 2301487, July 2023, doi: 10.1002/adma.202301487.
[19] K. Boddapati, J. C. Osorio, and A. F. Arrieta, “On the Loss of Stability of Bistable Laminates due to Clamping”.
[20] P. Rothemund et al., “A soft, bistable valve for autonomous control of soft actuators,” Sci. Robot., vol. 3, no. 16, p. eaar7986, Mar. 2018, doi: 10.1126/scirobotics.aar7986.
Physical nonlinearities in soft robotic architectures have provided a solution to address existing limitations by utilizing the robots' structure for physical control [11]. Elements such as snap-through instabilities, rate dependencies, and responsiveness to external stimuli have been integrated into soft robotics architectures to improve performance, functionality, and embodied control. By leveraging the system's mechanical response, various outputs can be encoded into the system through logic operations [11], [12], [13], alternating cycles [14], object size classification [15], [16], and an overall enhancement in robot performance [16]. However, such robotic architecture currently lacks autonomy and often demonstrate low performance when untethered. A significant factor contributing to this issue is the added mass from pneumatic systems, which typically constitutes the majority of the robot's structure. This work aims to explore, analyze, and design various pneumatic actuation systems to be integrated into soft robotic architectures, facilitating their untethered operation.
Among the existing architectures, we will analyze self-regulating oscillators [17], sequence-driven actuators [18], and pneumatic logic systems [12], [19], [20], offering insights into various soft logic and control devices and how to incorporate them into larger soft machines or systems.
References
[1] P. Polygerinos et al., “Soft Robotics: Review of Fluid-Driven Intrinsically Soft Devices; Manufacturing, Sensing, Control, and Applications in Human-Robot Interaction,” Adv. Eng. Mater., vol. 19, no. 12, p. 1700016, Dec. 2017, doi: 10.1002/ADEM.201700016.
[2] S. Kim, C. Laschi, and B. Trimmer, “Soft robotics: a bioinspired evolution in robotics,” Trends Biotechnol., vol. 31, no. 5, pp. 287–294, May 2013, doi: 10.1016/J.TIBTECH.2013.03.002.
[3] C. Majidi, “Soft-Matter Engineering for Soft Robotics,” Adv. Mater. Technol., vol. 4, no. 2, Feb. 2019, doi: 10.1002/ADMT.201800477.
[4] D. Rus and M. T. Tolley, “Design, fabrication and control of soft robots,” Nature, vol. 521, no. 7553, pp. 467–475, May 2015, doi: 10.1038/nature14543.
[5] C. Laschi, B. Mazzolai, and M. Cianchetti, “Soft robotics: Technologies and systems pushing the boundaries of robot abilities,” Sci. Robot., vol. 1, no. 1, p. eaah3690, Dec. 2016, doi: 10.1126/scirobotics.aah3690.
[6] R. Pfeifer, M. Lungarella, and F. Iida, “The challenges ahead for bio-inspired ‘soft’ robotics,” Commun. ACM, vol. 55, no. 11, pp. 76–87, Nov. 2012, doi: 10.1145/2366316.2366335.
[7] D. Trivedi, C. D. Rahn, W. M. Kier, and I. D. Walker, “Soft Robotics: Biological Inspiration, State of the Art, and Future Research,” Appl. Bionics Biomech., vol. 5, no. 3, pp. 99–117, 2008, doi: 10.1080/11762320802557865.
[8] R. L. Truby et al., “Soft Somatosensitive Actuators via Embedded 3D Printing,” Adv. Mater., vol. 30, no. 15, p. 1706383, Apr. 2018, doi: 10.1002/ADMA.201706383.
[9] K. Chin, T. Hellebrekers, and C. Majidi, “Machine Learning for Soft Robotic Sensing and Control,” Adv. Intell. Syst., vol. 2, no. 6, p. 1900171, June 2020, doi: 10.1002/AISY.201900171.
[10] T. G. Thuruthel, B. Shih, C. Laschi, and M. T. Tolley, “Soft robot perception using embedded soft sensors and recurrent neural networks,” Sci. Robot., vol. 4, no. 26, p. eaav1488, Jan. 2019, doi: 10.1126/scirobotics.aav1488.
[11] E. Milana, C. D. Santina, B. Gorissen, and P. Rothemund, “Physical control: A new avenue to achieve intelligence in soft robotics,” Sci. Robot., 2025.
[12] S. Conrad et al., “3D-printed digital pneumatic logic for the control of soft robotic actuators,” Sci. Robot., vol. 9, no. 86, p. eadh4060, Jan. 2024, doi: 10.1126/scirobotics.adh4060.
[13] Y. Zhai et al., “Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits,” Sci. Robot., vol. 8, no. 79, p. eadg3792, June 2023, doi: 10.1126/scirobotics.adg3792.
[14] D. Drotman, S. Jadhav, D. Sharp, C. Chan, and M. T. Tolley, “Electronics-free pneumatic circuits for controlling soft-legged robots,” Sci. Robot., vol. 6, no. 51, p. 2627, Feb. 2021, doi: 10.1126/SCIROBOTICS.AAY2627/SUPPL_FILE/AAY2627_SM.PDF.
[15] S. Zou, S. Picella, J. De Vries, V. G. Kortman, A. Sakes, and J. T. B. Overvelde, “A retrofit sensing strategy for soft fluidic robots,” Nat. Commun., vol. 15, no. 1, p. 539, Jan. 2024, doi: 10.1038/s41467-023-44517-z.
[16] J. C. Osorio, J. S. Rincon, H. Morgan, and A. F. Arrieta, “Embodying Control in Soft Multistable Robots from Morphofunctional Co‐design,” Adv. Sci., p. e03206, July 2025, doi: 10.1002/advs.202503206.
[17] L. C. Van Laake, J. De Vries, S. Malek Kani, and J. T. B. Overvelde, “A fluidic relaxation oscillator for reprogrammable sequential actuation in soft robots,” Matter, vol. 5, no. 9, pp. 2898–2917, Sept. 2022, doi: 10.1016/j.matt.2022.06.002.
[18] B. Van Raemdonck, E. Milana, M. De Volder, D. Reynaerts, and B. Gorissen, “Nonlinear Inflatable Actuators for Distributed Control in Soft Robots,” Adv. Mater., p. 2301487, July 2023, doi: 10.1002/adma.202301487.
[19] K. Boddapati, J. C. Osorio, and A. F. Arrieta, “On the Loss of Stability of Bistable Laminates due to Clamping”.
[20] P. Rothemund et al., “A soft, bistable valve for autonomous control of soft actuators,” Sci. Robot., vol. 3, no. 16, p. eaar7986, Mar. 2018, doi: 10.1126/scirobotics.aar7986.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Modeling and fabrication of soft robots, control systems, robotic implementation, 3D printing
School/Dept.:
School of Mechanical Engineering
Professor:
Andres
Arrieta
More information: https://engineering.purdue.edu/ProgrammableStructures/
Design of Learning Modules in Physics Introductory Courses Aligned with Students Epistemic Profiles
Description:
Epistemology, the study of perceptions and justification of knowledge, is a key component of education research. In the evolving landscape of artificial intelligent systems, promoting personalized learning is evolving as a key area of research. This project seeks to facilitate students’ personalized learning through integration of AI in a large-enrolment physics course by tuning AI models to students’ epistemological beliefs. These beliefs correspond to students’ perceptions of what counts as “knowing” or “doing” physics. The idea is to develop learning modules in the physics course by integrating AI and simulations to facilitate students’ personalized learning of key physics concepts.
The student will work with a postdoctoral research scholar and fellow graduate students in designing the modules, collecting and analyzing data, along with publishing findings in leading peer-reviewed conference proceedings and journals.
The student will work with a postdoctoral research scholar and fellow graduate students in designing the modules, collecting and analyzing data, along with publishing findings in leading peer-reviewed conference proceedings and journals.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Learning and Evaluation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Experience with developing AI Modules.
School/Dept.:
Physics and Astronomy
Professor:
N. Sanjay
Rebello
Design, analysis, and fabrication of elastic light scattering system for airborne particulate matter
Description:
Student will work with Dr. Bae and his group to design, analysis, and fabricate a muti-angle light scattering measurement system for particulate matter detection and characterization. Design aspect will include concept design, design trade offs, and assembling bill of materials for make and buy. Analysis part will include estimating signal to noise ratio of the design choice and discussing the trade off between design choices. Fabrication will include designing a PCD board and if time permits, we will order them and do a surface mount soldering for testing.
Campus:
West Lafayette
Research categories:
Environmental Characterization, Microelectronics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Electrical Engineering
Desired experience:
Mechanical design experience, electronic/electric circuit experience and fabrication, micro-controller data acquisition
School/Dept.:
School of Mechanical Engineering
Professor:
Euiwon
Bae
Detection of protein biomarkers for disease diagnosis
Description:
: Nearly half of the world’s population is at risk for malaria according to the World Health Organization (WHO). However, the disease burden is unevenly distributed with approximately 75% of malaria cases and malaria-related deaths occurring in sub-Saharan Africa. Mothers and infants (i.e., pregnant/lactating women and children under 5) are at highest risk for severe malaria and death. To protect them, a population’s reservoir of infection needs to be addressed, including symptomatic and non-symptomatic carriers. Two of the largest barriers to a large-scale population screening is the invasiveness of blood draw or finger prick, and the vanishing low concentration of malaria biomarkers in non-invasive samples (e.g. saliva and urine).
To address these challenges, simultaneous multi-factor detection (detecting protein and nucleic acid biomarkers in the same sample) has the potential to increase specificity and sensitivity of disease detection by leveraging signal amplifications from both biomarker types. Ideally these testing platforms would have broad applications and have the potential to be manufactured in handheld and portable formats for use in low resource settings or at the point-of-need. Our group is poised to develop a technology platform that can address the barriers. We have previously achieved highly sensitive detection of malaria (3 parasite/µL of blood) from patient samples with 89% sensitivity and 100% specificity when compared to quantitative polymerase chain reaction (qPCR) (Colbert et al., 2021). In more recent work, we have used PD to detect protein-protein interactions for fundamental studies of protein binding kinetics (Ma et al., 2024, 2022), demonstrating the versatility of PD as a sensing modality. We have also demonstrated that PD-based pathogen detection can be performed on a hand-held portable system (Colbert et al., 2021).
In this application we propose to use particle diffusometry (PD) and optimize test conditions and PD detection algorithms for simultaneous multi-factor (nucleic acid and protein) pathogen detection. We will exploit relative changes in both particle size and solution viscosity in a dual use platform, which is expected to significantly increase the sensitivity of detection. We will demonstrate detection of Plasmodium falciparum (Pf) in blood, plasma, and saliva at levels consistent with asymptomatic carriers of the malarial pathogen. An advantage of our PD platform is that the principles that we establish in this work for malaria biomarker detection platform can be easily adapted for detection of other infectious diseases. Additionally, the protein detection technology can be extended into applications beyond rapid point-of-care (POC) diagnostics, such as sensing protein aggregation of biopharmaceuticals at the point-of-production, point-of-testing for produce freshness or point-of-injury for emergency care personnel.
To address these challenges, simultaneous multi-factor detection (detecting protein and nucleic acid biomarkers in the same sample) has the potential to increase specificity and sensitivity of disease detection by leveraging signal amplifications from both biomarker types. Ideally these testing platforms would have broad applications and have the potential to be manufactured in handheld and portable formats for use in low resource settings or at the point-of-need. Our group is poised to develop a technology platform that can address the barriers. We have previously achieved highly sensitive detection of malaria (3 parasite/µL of blood) from patient samples with 89% sensitivity and 100% specificity when compared to quantitative polymerase chain reaction (qPCR) (Colbert et al., 2021). In more recent work, we have used PD to detect protein-protein interactions for fundamental studies of protein binding kinetics (Ma et al., 2024, 2022), demonstrating the versatility of PD as a sensing modality. We have also demonstrated that PD-based pathogen detection can be performed on a hand-held portable system (Colbert et al., 2021).
In this application we propose to use particle diffusometry (PD) and optimize test conditions and PD detection algorithms for simultaneous multi-factor (nucleic acid and protein) pathogen detection. We will exploit relative changes in both particle size and solution viscosity in a dual use platform, which is expected to significantly increase the sensitivity of detection. We will demonstrate detection of Plasmodium falciparum (Pf) in blood, plasma, and saliva at levels consistent with asymptomatic carriers of the malarial pathogen. An advantage of our PD platform is that the principles that we establish in this work for malaria biomarker detection platform can be easily adapted for detection of other infectious diseases. Additionally, the protein detection technology can be extended into applications beyond rapid point-of-care (POC) diagnostics, such as sensing protein aggregation of biopharmaceuticals at the point-of-production, point-of-testing for produce freshness or point-of-injury for emergency care personnel.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology
Citizenship requirements:
No citizenship requirements
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Tamara
Kinzer-Ursem
Detection of protein biomarkers for disease diagnosis
Description:
: Nearly half of the world’s population is at risk for malaria according to the World Health Organization (WHO). However, the disease burden is unevenly distributed with approximately 75% of malaria cases and malaria-related deaths occurring in sub-Saharan Africa. Mothers and infants (i.e., pregnant/lactating women and children under 5) are at highest risk for severe malaria and death. To protect them, a population’s reservoir of infection needs to be addressed, including symptomatic and non-symptomatic carriers. Two of the largest barriers to a large-scale population screening is the invasiveness of blood draw or finger prick, and the vanishing low concentration of malaria biomarkers in non-invasive samples (e.g. saliva and urine).
To address these challenges, simultaneous multi-factor detection (detecting protein and nucleic acid biomarkers in the same sample) has the potential to increase specificity and sensitivity of disease detection by leveraging signal amplifications from both biomarker types. Ideally these testing platforms would have broad applications and have the potential to be manufactured in handheld and portable formats for use in low resource settings or at the point-of-need. Our group is poised to develop a technology platform that can address the barriers. We have previously achieved highly sensitive detection of malaria (3 parasite/µL of blood) from patient samples with 89% sensitivity and 100% specificity when compared to quantitative polymerase chain reaction (qPCR) (Colbert et al., 2021). In more recent work, we have used PD to detect protein-protein interactions for fundamental studies of protein binding kinetics (Ma et al., 2024, 2022), demonstrating the versatility of PD as a sensing modality. We have also demonstrated that PD-based pathogen detection can be performed on a hand-held portable system (Colbert et al., 2021).
In this application we propose to use particle diffusometry (PD) and optimize test conditions and PD detection algorithms for simultaneous multi-factor (nucleic acid and protein) pathogen detection. We will exploit relative changes in both particle size and solution viscosity in a dual use platform, which is expected to significantly increase the sensitivity of detection. We will demonstrate detection of Plasmodium falciparum (Pf) in blood, plasma, and saliva at levels consistent with asymptomatic carriers of the malarial pathogen. An advantage of our PD platform is that the principles that we establish in this work for malaria biomarker detection platform can be easily adapted for detection of other infectious diseases. Additionally, the protein detection technology can be extended into applications beyond rapid point-of-care (POC) diagnostics, such as sensing protein aggregation of biopharmaceuticals at the point-of-production, point-of-testing for produce freshness or point-of-injury for emergency care personnel.
To address these challenges, simultaneous multi-factor detection (detecting protein and nucleic acid biomarkers in the same sample) has the potential to increase specificity and sensitivity of disease detection by leveraging signal amplifications from both biomarker types. Ideally these testing platforms would have broad applications and have the potential to be manufactured in handheld and portable formats for use in low resource settings or at the point-of-need. Our group is poised to develop a technology platform that can address the barriers. We have previously achieved highly sensitive detection of malaria (3 parasite/µL of blood) from patient samples with 89% sensitivity and 100% specificity when compared to quantitative polymerase chain reaction (qPCR) (Colbert et al., 2021). In more recent work, we have used PD to detect protein-protein interactions for fundamental studies of protein binding kinetics (Ma et al., 2024, 2022), demonstrating the versatility of PD as a sensing modality. We have also demonstrated that PD-based pathogen detection can be performed on a hand-held portable system (Colbert et al., 2021).
In this application we propose to use particle diffusometry (PD) and optimize test conditions and PD detection algorithms for simultaneous multi-factor (nucleic acid and protein) pathogen detection. We will exploit relative changes in both particle size and solution viscosity in a dual use platform, which is expected to significantly increase the sensitivity of detection. We will demonstrate detection of Plasmodium falciparum (Pf) in blood, plasma, and saliva at levels consistent with asymptomatic carriers of the malarial pathogen. An advantage of our PD platform is that the principles that we establish in this work for malaria biomarker detection platform can be easily adapted for detection of other infectious diseases. Additionally, the protein detection technology can be extended into applications beyond rapid point-of-care (POC) diagnostics, such as sensing protein aggregation of biopharmaceuticals at the point-of-production, point-of-testing for produce freshness or point-of-injury for emergency care personnel.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Biochemistry (Chemistry)
- Biomedical Engineering
- Biological Engineering - multiple concentrations
- Chemical Engineering
- Biochemistry
- Biochemistry (Biology)
Desired experience:
Technical and laboratory skills: Student(s) chosen for this project will be working on assay development; optimizing assay design, testing various parameters (antibody, proteins, concentrations, wash conditions, etc); using a widefield microscope to capture microscopic images of prepared samples; and use ImageJ software for analyzing microscope images for quantification methods. Basic coding skills may be necessary as well.
Qualifications and performance expectations: Students(s) must demonstrate intellectual curiosity and persistence in experimental inquiry; have a strong foundation in cell and molecular biology; and exhibit attention to detail in organized notetaking, critical thinking, and quantitative reasoning in both experimental design and data interpretation. Attendance is expected for weekly laboratory meetings to discuss research progress and receive feedback for areas of improvement. Clear communication of program requirements, experiment handling, and mentoring needs are strongly encouraged.
Expected outcomes for student development: Through this project, student(s) will be able to:
Laboratory skills - (1) assay development and troubleshooting; (2) learn the concepts of protein-protein binding; (3) learn DNA amplification techniques; (4) obtain imaging data on a fluorescent microscope; (5) and perform image quantification and statistical analysis for gather generated
Individual development – (1) Technical writing skills; (2) critical thinking and results interpretation; (3) experimental design validation; (4) teamworking skills on collaborative project; and (5) professional presentation skills (oral and poster)
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Tamara
Kinzer-Ursem
Determining the Elastic Constants of Rock
Description:
Rock often exhibits anisotropic mechanical properties that arise from layering, foliation or preferred mineral orientation that develops during rock formation. Designing subsurface infrastructure in rock requires knowledge of the elastic constants of rock. In this study, research will determine how rock mineralogy and structure affect the mechanical properties of the rock. The student will (1) use 3D X-ray imaging to identify the mineralogy and structure of rock samples; (2) perform two types of ultrasonic measurements to measure elastic wave speeds; (3) determine the elastic constants of rock samples by fitting the arrival times to the Christoffel equation, and (4) examine the variability of elastic constants for rocks from different subsurface locations.
Campus:
West Lafayette
Research categories:
Energy and Environment, Environmental Characterization, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Previous laboratory experiments
School/Dept.:
Physics and Astronomy
Professor:
Laura
Pyrak-Nolte
Developing Soft Growing Robot Delivery of Insect Scale Robots for Non-Destructive Inspection
Description:
This project examines how soft growing robots (also known as vine robots) can be used to deliver and support insect scale robot sensor payloads. This project involves manufacturing and implementing a designed payload cap and testing it with the soft growing robot. Student will be expected to manufacture the mechanical and electrical components of the cap, integrate and test these components with existing soft robot and software platforms, and integrate and test with insect robot payloads. Student will develop skills with soft robot manufacturing and modeling, sensor integration, and non-destructive evaluation techniques.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Electrical Engineering
Desired experience:
Applicants should have some experience with microcontrollers (like arduino) and with 3D printing
School/Dept.:
School of Mechanical Engineering
Professor:
Laura
Blumenschein
More information: https://purdueraadlab.wixsite.com/website-1
Development and application of nuclear techniques and machine learning in human health
Description:
Dr. Nie’s group focuses on developing radiation-based instruments and methodologies for applications in human health. Students in her lab work on projects that advance neutron and X-ray technologies for measuring metals and trace elements in human bones and tissues in vivo. They also participate in high-resolution mapping of elemental concentrations and speciation in human and animal brains using state-of-the-art synchrotron facilities. These innovative approaches are applied to study metal exposure and its impact on health, the relationship between nutrition and health, and the links between metal exposure and neurodegeneration. As part of their research, students engage in Monte Carlo simulations, laboratory experiments, and data analysis. A new research direction involves leveraging artificial intelligence, particularly machine learning, to detect metal deposition patterns in healthy and neurodegenerative brains and to explore the connections between metal exposure, brain metal accumulation, and neurodegeneration.
Student can select to work on: make standards for elemental quantification, conduct Monte Carlo simulations on radiation transportation, radiation instrumentation design and development, perform experiments with the neutron generator, x-ray devices, and radiation detectors available in Dr. Nie’s lab, conduct experiments at the advanced photon source (APS) synchrotron facility at Argonne National Lab, x-ray and gamma ray spectroscopy, spectral fitting, statistical data analysis, and machine learning applications.
Student can select to work on: make standards for elemental quantification, conduct Monte Carlo simulations on radiation transportation, radiation instrumentation design and development, perform experiments with the neutron generator, x-ray devices, and radiation detectors available in Dr. Nie’s lab, conduct experiments at the advanced photon source (APS) synchrotron facility at Argonne National Lab, x-ray and gamma ray spectroscopy, spectral fitting, statistical data analysis, and machine learning applications.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Physics
- Radiological Health Sciences
- Computer Science
- Nuclear Engineering
- Biomedical Engineering
Desired experience:
Analytical skills; programming; basic knowledge on radiation sciences; interest in human health.
School/Dept.:
Health Sciences
Professor:
Linda
Nie
Development and characterization of low noise voltage and current amplifiers
Description:
The project involves development of ultra-low noise voltage and current amplifiers, and there integration into our low temperature experimental setup. As a starting point we will use an existing schematic of an amplifier which suppose to achieve < 0.5 nV/sqrt(Hz) noise level, build prototypes and perform bench and real world testing using real samples in a cryogenic system. Depending on student's background in physics I also expect active involvement of the SURF participant in one of the ongoing projects in close collaboration with a graduate student or a postdoctoral group member.
Campus:
West Lafayette
Research categories:
Microelectronics, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering
School/Dept.:
Department of Physics and Astronomy
Professor:
Leonid
Rokhinson
More information: http://www.physics.purdue.edu/leogroup
Development of Catalytic Methods for the Synthesis of Pharmaceutical Building Blocks
Description:
The student involved in this project will develop new organic reactions of interest in pharmaceutical synthesis using homogeneous catalysts. One key aspect of sustainability is that these catalysts will be exclusively based on earth-abundant elements as alternatives to precious metals such as palladium, platinum, rhodium, and iridium. Students will learn how to optimize reactions in order to obtain high yields, study mechanisms using a variety of experimental techniques, and carry out computational modeling studies using DFT methods. Students are expected to participate in meetings by presenting their research and assist in writing paper drafts. They will gain extensive technical expertise in conducting reactions under air-free conditions, monitoring reactions using a variety of analytical tools, and using physical organic methods to probe mechanisms.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Christopher
Uyeda
More information: https://www.chem.purdue.edu/uyeda/
Development of DNA Nanomaterials for miRNA Delivery across the Blood-Brain Barrier
Description:
This project is focused on addressing the challenge of treating and studying the brain-related diseases (Alzheimer's Disease, Parkinson's Disease) with DNA nano tetrahedra to deliver miRNA to control the protein expression level. DNA nanotechnology has a great promise in biomedical applications due to high biocompatibility, tunability, and relative stability. By now, our group has developed six nanostructures and started the in vitro delivery of miRNA with three different functionalization methods.
The students are expected to contribute to the work of characterization of individual nanostructures for miRNA in vitro delivery, the influence of functionalization methods on the delivery, or the delivery of miRNA in new cells lines. The students will perform experimental work to make the nanostructures, use them to deliver miRNA, evaluate the results, and process them. The techniques expected to be learned and be used are gel electrophoresis, DNA nanomaterials assembly, cell tissue culture, PCR confocal microscopy, image processing, and statistics. If preferred, coarse-grain and atomistic simulations of DNA nanomaterials in solution and potentially interacting with the cellular surface is possible.
The students are expected to contribute to the work of characterization of individual nanostructures for miRNA in vitro delivery, the influence of functionalization methods on the delivery, or the delivery of miRNA in new cells lines. The students will perform experimental work to make the nanostructures, use them to deliver miRNA, evaluate the results, and process them. The techniques expected to be learned and be used are gel electrophoresis, DNA nanomaterials assembly, cell tissue culture, PCR confocal microscopy, image processing, and statistics. If preferred, coarse-grain and atomistic simulations of DNA nanomaterials in solution and potentially interacting with the cellular surface is possible.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology, Material Modeling and Simulation, Nanotechnology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
- Biochemistry
- Chemistry
- Biology
- Biological Engineering - multiple concentrations
- Agricultural Engineering
Desired experience:
No prior experience is required; however former wet lab, gel electrophoresis, and cell culture experience are encouraged. If you prefer computational work, Python basic programming knowledge is encouraged
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Tamara
Kinzer-Ursem
More information: https://engineering.purdue.edu/ursemlab/index.html
Development of DNA Nanomaterials for miRNA Delivery across the Blood-Brain Barrier and Testing Cellular Pores
Description:
This project is focused on addressing the challenge of treating and studying the brain-related diseases (Alzheimer's Disease, Parkinson's Disease) with DNA nano tetrahedra to deliver miRNA to control the protein expression level. In addition, the students will help with testing DNA nanopores toxicity effects on cells. DNA nanotechnology has a great promise in biomedical applications due to high biocompatibility, tunability, and relative stability. By now, our group has developed six nanostructures and started the in vitro delivery of miRNA with three different functionalization methods.
The students are expected to contribute to the work of characterization of individual nanostructures for miRNA in vitro delivery, the influence of functionalization methods on the delivery, or the delivery of miRNA in new cells lines. The students will perform experimental work to make the nanostructures, use them to deliver miRNA, evaluate the results, and process them. The techniques expected to be learned and be used are gel electrophoresis, DNA nanomaterials assembly, cell tissue culture, PCR confocal microscopy, image processing, and statistics. If preferred, coarse-grain and atomistic simulations of DNA nanomaterials in solution and potentially interacting with the cellular surface is possible.
The students are expected to contribute to the work of characterization of individual nanostructures for miRNA in vitro delivery, the influence of functionalization methods on the delivery, or the delivery of miRNA in new cells lines. The students will perform experimental work to make the nanostructures, use them to deliver miRNA, evaluate the results, and process them. The techniques expected to be learned and be used are gel electrophoresis, DNA nanomaterials assembly, cell tissue culture, PCR confocal microscopy, image processing, and statistics. If preferred, coarse-grain and atomistic simulations of DNA nanomaterials in solution and potentially interacting with the cellular surface is possible.
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology, Cellular Biology, Medical Science and Technology, Nanotechnology
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Biomedical Engineering
- Biological Engineering - multiple concentrations
- Chemistry
- Pharmacy
- Biology
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Leopold
Green
More information: https://www.gar-nano.com/
Development of LENN Materials for Targeted mRNA Delivery
Description:
The SURF student will work closely with a graduate student to synthesize and characterize oligomers for condensing mRNA into nanoparticles. A library of eight compounds will be targeted in this project.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Chemical Catalysis and Synthesis, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
completion of two semesters of organic chemistry laboratory
School/Dept.:
Chemistry
Professor:
David
Thompson
More information: www.chem.purdue.edu/thompson
Development of Sustainably Sourced, High Performance Materials
Description:
The oceans are home to a diverse collection of animals producing intriguing materials. Mussels, barnacles, oysters, starfish, and kelp are examples of the organisms generating adhesive matrices for affixing themselves to the sea floor. Our laboratory is characterizing these biological materials, designing mimics, and developing applications. Mimics of these bioadhesives begin with the chemistry learned from characterization studies and incorporate the findings into new materials that can be produced on larger scales. A recent emphasis is sustainability. We are making new classes of sustainably sourced materials that are of high performance, low cost, and even can be carbon negative. Future efforts are planned in areas including: A) Using bulk proteins and inorganics to make mimics of the wet bonding cement from oysters and B) Using biobased and biomimetic adhesives for the basis of new plastic materials, such as systems like carbon fiber reinforced polymers, with all components sourced sustainably.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Composite Materials and Alloys, Ecology and Sustainability, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Jonathan
Wilker
More information: https://www.chem.purdue.edu/wilker/
Development of a Comfortable, Wearable, Cut-Resistant Hockey Neck Guard
Description:
Currently available neck guards for use in ice hockey fall short in combining suitable cut resistance with wearability and comfort. The aim of this work is to utilize high performance protective materials with human centered design co-prioritizing comfort, fit, and mobility.
The multilayer material stack necessary to achieve these targets is currently being developed. During this process samples of the material will be evaluated for cut resistance and basic material properties.
Students involved with this project over Summer ‘26 will be involved in any additional material characteization found to be necessary and initial 3D forming trials of the guard. This will provide students with an opportunity to gain familiarity with polymer-composite manufacturing techniques and material characterization
The multilayer material stack necessary to achieve these targets is currently being developed. During this process samples of the material will be evaluated for cut resistance and basic material properties.
Students involved with this project over Summer ‘26 will be involved in any additional material characteization found to be necessary and initial 3D forming trials of the guard. This will provide students with an opportunity to gain familiarity with polymer-composite manufacturing techniques and material characterization
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Human Factors, Material Processing and Characterization, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Manufacturing Methods, Material Characterization
School/Dept.:
School of Materials Engineering
Professor:
Jan-Anders
Mansson
More information: https://engineering.purdue.edu/Engr/Ewry
Development of a Drone-Based Synthetic Aperture Radar System for Agricultural Applications
Description:
DroneSAR is an innovative initiative that integrates synthetic aperture radar (SAR) technology with unmanned aerial vehicles (UAVs) to deliver high-resolution imaging and near real-time data collection for applications including soil moisture and biomass estimation. Our state-of-the-art S-Band radar is engineered to penetrate vegetation and the topsoil layer, enabling direct measurement of soil moisture beneath the canopy.
The radar system is fully integrated with a UAV platform, featuring an advanced positioning system and cutting-edge digital signal processing algorithms. We have developed robust calibration methods to ensure accurate quantitative surface measurements. During the summer, the project will focus on development and delivery data products for soil moisture and biomass estimation through extensive data collection, processing, and algorithm refinement.
This is a collaborative research project between the Laboratory for Applications of Remote Sensing and the Radio Navigation Lab at Purdue University, focusing on navigation and signals of opportunity for Earth remote sensing.
The radar system is fully integrated with a UAV platform, featuring an advanced positioning system and cutting-edge digital signal processing algorithms. We have developed robust calibration methods to ensure accurate quantitative surface measurements. During the summer, the project will focus on development and delivery data products for soil moisture and biomass estimation through extensive data collection, processing, and algorithm refinement.
This is a collaborative research project between the Laboratory for Applications of Remote Sensing and the Radio Navigation Lab at Purdue University, focusing on navigation and signals of opportunity for Earth remote sensing.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Environmental Characterization, Fabrication and Robotics, IoT for Precision Agriculture, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering
- Geomatics
- Aeronautical and Astronautical Engineering
- Agriculture and Biological Engineering
Desired experience:
Required; Matlab and/or python programming, willingness to work in the field to conduct data acquisition. Desirable: some background in GPS, radar or electromagnetics
School/Dept.:
Lyles School of Civil Engineering
Professor:
Melba
Crawford
More information: https://iot4ag.us/
Digital Twin Modeling of Capacitorless Power Converters for Reliable Electric Energy Systems
Description:
Power electronics converters are essential components in electric vehicles, renewable energy systems, and modern power grids. However, one of the most common causes of failure in these systems is the DC-link capacitor, which degrades over time due to temperature stress, electrical ripple, and aging.
This research project investigates capacitorless power converter topologies that eliminate the need for large capacitors while maintaining performance and reliability. The project will also develop a digital twin (digital clone) of the converter to predict failures and estimate converter lifetime using data-driven models.
The undergraduate researcher will assist in building experimental prototypes and collecting data from power converters operating under different environmental stresses such as temperature, vibration, humidity, and load cycling. The collected data will be used to train machine learning models that estimate time-to-failure and predict reliability under real operating conditions.
The student will participate in several research tasks including:
1. Simulating power converters in MATLAB/Simulink
2. Assisting with hardware testing of DC-DC converters
3. Collecting electrical measurements such as voltage, current, and temperature
4. Analyzing converter performance under different environmental conditions
5. Supporting the development of a digital twin model for reliability prediction
The research aims to create tools that allow engineers to predict failures before they occur, improving reliability in applications such as electric vehicles, aircraft power systems, and renewable energy installations.
Students will gain hands-on experience in:
1. Power electronics hardware
2. Embedded measurement systems
3. Data acquisition and experimental testing
4. Machine learning for engineering systems
5. Reliability analysis of electrical systems
This research project investigates capacitorless power converter topologies that eliminate the need for large capacitors while maintaining performance and reliability. The project will also develop a digital twin (digital clone) of the converter to predict failures and estimate converter lifetime using data-driven models.
The undergraduate researcher will assist in building experimental prototypes and collecting data from power converters operating under different environmental stresses such as temperature, vibration, humidity, and load cycling. The collected data will be used to train machine learning models that estimate time-to-failure and predict reliability under real operating conditions.
The student will participate in several research tasks including:
1. Simulating power converters in MATLAB/Simulink
2. Assisting with hardware testing of DC-DC converters
3. Collecting electrical measurements such as voltage, current, and temperature
4. Analyzing converter performance under different environmental conditions
5. Supporting the development of a digital twin model for reliability prediction
The research aims to create tools that allow engineers to predict failures before they occur, improving reliability in applications such as electric vehicles, aircraft power systems, and renewable energy installations.
Students will gain hands-on experience in:
1. Power electronics hardware
2. Embedded measurement systems
3. Data acquisition and experimental testing
4. Machine learning for engineering systems
5. Reliability analysis of electrical systems
Campus:
Indianapolis
Research categories:
Big Data/Machine Learning, Energy and Environment, Material Modeling and Simulation, Microelectronics, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Electrical Engineering or Computer Engineering
Desired experience:
ECE 20001
ECE 20002
ECE 43300
ECE 38200
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Euzeli
Dos Santos Jr.
More information: https://ieeexplore.ieee.org/abstract/document/10861665
Digital Twins and IoT design for product/equipment
Description:
This project is to design and develop digital twins and IoT platforms for physical products or equipment to link the physical and cyber systems. This is an entry point for managing factory operations and supply chains to optimize their cost efficiency, service level, and resilience. The student will work with Dr. Stephan Biller and Dr. Yuehwern Yih to identify the physical system and the data source to develop a digital twin for this system to monitor and predict its performance over its life cycle. The student will learn the functionality, performance measures, and existing sensors embedded in the physical system, to identify elements that are relevant for the digital twin and if there are any data gaps. This may involve learning multiple software systems or sensor technology embedded in the hardware, and conducting experiments for data collection in the lab. This project is meant to continue beyond the summer and potentially lead to a Master thesis or PhD dissertation.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Fabrication and Robotics, Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Requirements:
- Have curiosity and interest in digital twin, digital models, IoT, and manufacturing
- Understand fundamentals in manufacturing systems, data analysis (statistical, AI, etc.), and sensors
- Have experiences in digital models (including, but not limited to prescriptive, predictive, prescriptive, simulation, emulator, etc.)
- Have skills to learn new material quickly and have an open mind
- Have excellent communication (both writing and verbal) skills and interpersonal skills
- Experiences in digital twins are preferred, but not required.
School/Dept.:
Edwardson School of Industrial Engineering
Professor:
Yuehwern
Yih
More information: https://medium.com/purdue-engineering/digital-twins-smart-manufacturings-dna-for-a-bright-future-960882ab03ad
Digital Twins and IoT design for product/equipment
Description:
This project is to design and develop digital twins and IoT platforms for physical products or equipment to link the physical and cyber systems. This is an entry point for managing factory operations and supply chains to optimize their cost efficiency, service level, and resilience. The student will work with Dr. Stephan Biller and Dr. Yuehwern Yih to identify the physical system and the data source to develop a digital twin for this system to monitor and predict its performance over its life cycle. The student will learn the functionality, performance measures, and existing sensors embedded in the physical system, to identify elements that are relevant for the digital twin and if there are any data gaps. This may involve learning multiple software systems or sensor technology embedded in the hardware, and conducting experiments for data collection in the lab. This project is meant to continue beyond the summer and potentially lead to a Master thesis or PhD dissertation.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Fabrication and Robotics, Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Desired experience:
Requirements:
- Have curiosity and interest in digital twin, IoT, and manufacturing
- Understand fundamentals in manufacturing systems, data analysis (statistical, AI, etc.), and sensors
- Have experiences in digital models (including, but not limited to prescriptive, predictive, prescriptive, simulation, emulator, etc.)
- Have skills to learn new material quickly and have an open mind
- Have excellent communication (both writing and verbal) skills and interpersonal skills
- Experiences in digital twins are preferred, but not required.
School/Dept.:
School of Industrial Engineering
Professor:
Yuehwern
Yih
More information: https://medium.com/purdue-engineering/digital-twins-smart-manufacturings-dna-for-a-bright-future-960882ab03ad
Disease ecology in freshwater systems
Description:
The student will work on projects related to the ecology of infectious disease in freshwater systems. The exact project will be determined by the student and mentor, but could include field sampling of amphibian communities for fungal infection or laboratory experiments testing the impact of temperature on infection in zooplankton. The student will learn skills related to field sampling, experimental design, disease diagnostics, and data analysis.
Campus:
West Lafayette
Research categories:
Ecology and Sustainability
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Biological Sciences
Professor:
Catherine
Searle
More information: https://www.bio.purdue.edu/lab/searle/
Effects of colliding deflagration waves
Description:
The initiation of deflagration in energetic materials such as RDX is a multi-scale process that begins with the formation and coalescence of microscopic hotspots. Molecular simulations and mesoscale models show that when these hotspots deflagrate and collide, a localized temperature spike can occur at the interface, potentially accelerating energy release and driving rapid reaction propagation. Understanding this hotspot interaction is key to building predictive models of energetic material sensitivity and performance.
This project focuses on using molecular dynamics (MD) simulations to quantify the thermal and structural evolution of colliding hotspots in RDX. Building on recent theoretical work investigating ignition and growth mechanisms, the goal is to characterize how localized heating, stress, and chemical reactivity change during deflagration onset.
In this project, the student will design atomistic simulations that model hotspot formation and collision under controlled thermodynamic conditions. They will calculate local temperature distributions, track reaction progression using reactive force fields, and analyze the energetics of the interface region where collisions occur.
The student will gain hands-on experience with molecular dynamics simulation tools, data analysis of reaction processes, and visualization of temperature and energy fields. They will also develop an understanding of how microscopic reaction mechanisms connect to macroscopic ignition behavior in energetic materials.
By the end of the project, the student will have contributed to a quantitative, molecular-level understanding of hotspot coalescence and early-stage deflagration dynamics in RDX, gaining practical skills applicable to computational materials science, chemical physics, and energetic materials research.
This project focuses on using molecular dynamics (MD) simulations to quantify the thermal and structural evolution of colliding hotspots in RDX. Building on recent theoretical work investigating ignition and growth mechanisms, the goal is to characterize how localized heating, stress, and chemical reactivity change during deflagration onset.
In this project, the student will design atomistic simulations that model hotspot formation and collision under controlled thermodynamic conditions. They will calculate local temperature distributions, track reaction progression using reactive force fields, and analyze the energetics of the interface region where collisions occur.
The student will gain hands-on experience with molecular dynamics simulation tools, data analysis of reaction processes, and visualization of temperature and energy fields. They will also develop an understanding of how microscopic reaction mechanisms connect to macroscopic ignition behavior in energetic materials.
By the end of the project, the student will have contributed to a quantitative, molecular-level understanding of hotspot coalescence and early-stage deflagration dynamics in RDX, gaining practical skills applicable to computational materials science, chemical physics, and energetic materials research.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Mechanical Engineering
- Materials Engineering
- Aeronautical and Astronautical Engineering
- Computer Science
Desired experience:
▪ Be available to work in person over the summer. ▪ Have experience with Python, or similar programming tools for data analysis or computational tasks. ▪ Be comfortable learning new software tools and working with structured input/output data. ▪ Have interest in workflow automation, scientific computing, or engineering simulations. ▪ Be able to document results clearly and communicate findings effectively. ▪ Experience with the LAMMPS software package is preferred.
School/Dept.:
School of Materials Engineering
Professor:
Alejandro
Strachan
More information: https://www.strachanlab.org/
Efficient and sustainable water technology
Description:
Water and energy are tightly linked resources that must both become renewable for a successful future. However, today, water and energy resources are often in conflict with one another, especially related to impacts on electric grids. Further, advances in nanotechnology, material science and artificial intelligence allow for new avenues to improve the widespread implementation of desalination and water purification technology. Our lab’s project aims to explore nanofabricated membranes, light-driven reactions, artificial intelligence control algorithms, and thermodynamic optimization of systems. Our projects include hybrids of reverse osmosis desalination with renewable energy (solar, wind, and hydro), as well as other topics such as filtration, water treatment, and water vapor harvesting. The student(s) will be responsible for fabricating membranes, building hydraulic systems, modeling thermal fluid phenomenon, analyzing data, and/or implementing control strategies in novel system configurations. The lab also works on separation processes for water in air, including HVAC dehumidification and removing aerosols. More information here: www.warsinger.com
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Fluid Modelling and Simulation, Material Processing and Characterization, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Materials Engineering
- Environmental and Ecological Engineering
- Chemical Engineering
- Chemistry
Desired experience:
Applicants should have an interest in thermodynamics, water treatment, and sustainability. Applicants with experience in some (not all) of the following are preferred: experimental design and prototyping, manufacturing, materials science, Python, LabView, EES, MATLAB, 3D CAD Software, & Adobe Illustrator. Rising Juniors and Seniors are preferred.
School/Dept.:
School of Mechanical Engineering
Professor:
David
Warsinger
More information: www.warsinger.com
Electrochemistry for selective synthesis of biomolecules
Description:
Our lab is using a new method in electrochemical catalysis to enable the broader and more selective synthesis of molecules important to medicine. The student will work with a postdoc mentor to run organic chemical reactions, evaluate reaction results using various analytical methods, purify complex mixtures of products, and characterize them. The student will be trained in advanced synthetic organic techniques including air-free synthesis and electrochemical synthesis, operation of analytical instruments, and organic purification and characterization. They will gain experience and confidence in a bench chemistry environment, be given the opportunity to present their work in laboratory meetings, and gain confidence in reading and analyzing organic chemical literature to gain a greater fluency in the state of the art.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Chemistry
- Chemical Engineering
Desired experience:
General chemistry lecture and lab, organic chemistry lecture and lab
School/Dept.:
Chemistry
Professor:
Gabriel
Lovinger
Energy Quantification of High Strain Rate Impact Testing
Description:
The energy qualification of a reactive material under extreme loading condition has been a struggle. In recent work it has been shown that is there are way to account for most of the energy released based on theoretical calculations. The of this work is to utilize large, closed volumes chambers and simple ignition systems such as an e-match to experimentally measure the potential energy of the lose reactive materials before consolidation. Once consolidated, micro CT of the consolidated materials will allow for modeling. Finally launching the materials into a test chamber with an array of diagnostics will examine the consolidated materials full circle. The full aim of this work is to understand the fragment behavior of consolidated materials on both an experimental and computational level.
Campus:
West Lafayette
Research categories:
Material Processing and Characterization, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Steven
Son
More information: https://www.sciencedirect.com/science/article/pii/S0263224125024078
Energy Quantification of High Strain Rate Impact Testing
Description:
The energy qualification of a reactive material under extreme loading condition has been a struggle. In recent work it has been shown that is there are way to account for most of the energy released based on theoretical calculations. The of this work is to utilize large, closed volumes chambers and simple ignition systems such as an e-match to experimentally measure the potential energy of the lose reactive materials before consolidation. Once consolidated, micro CT of the consolidated materials will allow for modeling. Finally launching the materials into a test chamber with an array of diagnostics will examine the consolidated materials full circle. The full aim of this work is to understand the fragment behavior of consolidated materials on both an eperimental and computational level.
Campus:
West Lafayette
Research categories:
Energy and Environment, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Desired experience:
Sophomore, Junior or Senior in ME, AAE, or MSE preferred.
School/Dept.:
School of Mechanical Engineering
Professor:
Steven
Son
More information: https://www.sciencedirect.com/science/article/pii/S0263224125024078
Energy Quantification of High Strain Rate Impact Testing
Description:
The energy qualification of a reactive material under extreme loading condition has been a struggle. In recent work it has been shown that is there are way to account for most of the energy released based on theoretical calculations. The of this work is to utilize large, closed volumes chambers and simple ignition systems such as an e-match to experimentally measure the potential energy of the lose reactive materials before consolidation. Once consolidated, micro CT of the consolidated materials will allow for modeling. Finally launching the materials into a test chamber with an array of diagnostics will examine the consolidated materials full circle. The full aim of this work is to understand the fragment behavior of consolidated materials on both an eperimental and computational level.
Campus:
West Lafayette
Research categories:
Energy and Environment, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Desired experience:
Sophomore, Junior or Senior in ME, AAE, or MSE preferred.
Professor:
Steven
Son
More information: https://www.sciencedirect.com/science/article/pii/S0263224125024078
Energy Storage Analytics
Description:
Lithium ion (Li-ion) batteries are ubiquitous. Thermal safety and degradation characteristics of these systems and advanced battery chemistries are critical to the development of safer, high-performance batteries for electric vehicles. As part of this research, data-driven analytics of experimental and simulated performance under normal and anomalous operating conditions of Li-ion cells and metal electrodes will be performed.
Campus:
West Lafayette
Research categories:
Energy and Environment
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Chemical Engineering
Desired experience:
Strong analytical skills and a desire to learn new experimental and modeling & analysis tools. The final deliverable will be one research report (based on weekly progress presentations and updates) and one final presentation.
School/Dept.:
School of Mechanical Engineering
Professor:
Partha
Mukherjee
More information: https://engineering.purdue.edu/ETSL/
Engineered Energetic Fuel Particles
Description:
High density fuels, typically metals, are commonly added to propellants and explosives to improve their performance, as well as other factors such as sensitivity and toxicity. This research topic explores the development, small-scale manufacturing, and characterization of high-density fuels in energetic materials. Emphasis is placed on emergent material systems, such as aluminum-lithium alloys, oxide-free coated nano-aluminum, and mechanically activated (MA) fuels. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
Campus:
West Lafayette
Research categories:
Energy and Environment, Other
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
Desired experience:
Strong interest in defense research..
School/Dept.:
School of Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/Energetics
Engineering Dual Inhibitors of HDAC3 and HIV Protease Towards a Cure for HIV
Description:
The student will work closely with a graduate student to design and synthesize inhibitors of two enzymes: HDAC3 and HIV protease. The will purify the synthetic agents and use NMR spectroscopy to validate that the designed agent has been prepared.
Campus:
West Lafayette
Research categories:
Medical Science and Technology
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemistry
Desired experience:
organic chemistry course work and lab, prior research lab experience in organic synthesis required.
School/Dept.:
Chemistry
Professor:
Jean
Chmielewski
Enhancing AI-Based Interaction with the Materials Project
Description:
Understanding how researchers interact with large materials databases is increasingly important for data-driven materials discovery. Resources such as the Materials Project provide access to a vast collection of computed materials properties, but effectively querying and interpreting this information can be challenging, particularly for complex or open-ended research questions. Recent advances in agentic artificial intelligence provide new opportunities to improve how these databases are accessed and used.
This project focuses on the enhancement and extension of an AI agent within the Autonomous Universal Research Assistant (AURA) framework that can interact with the Materials Project in more flexible and expressive ways than current implementations. The work will explore how improved prompting strategies, tool design, and agent logic can enable richer queries, including multi-criteria searches, property comparisons, and more complex materials exploration workflows.
AURA Paper: https://chemrxiv.org/doi/full/10.26434/chemrxiv-2025-rs07k
The student will play an active role in designing prompts, developing and testing tools, and evaluating agent behavior when interacting with the Materials Project. This includes identifying limitations in existing query capabilities, extending tool interfaces to expose additional information, and systematically testing the agent’s responses for correctness, robustness, and usability. Through this work, the student will gain hands-on experience with large language models, scientific APIs, and modern software development practices. The project supports broader goals of making data-driven materials research more accessible and effective.
This project focuses on the enhancement and extension of an AI agent within the Autonomous Universal Research Assistant (AURA) framework that can interact with the Materials Project in more flexible and expressive ways than current implementations. The work will explore how improved prompting strategies, tool design, and agent logic can enable richer queries, including multi-criteria searches, property comparisons, and more complex materials exploration workflows.
AURA Paper: https://chemrxiv.org/doi/full/10.26434/chemrxiv-2025-rs07k
The student will play an active role in designing prompts, developing and testing tools, and evaluating agent behavior when interacting with the Materials Project. This includes identifying limitations in existing query capabilities, extending tool interfaces to expose additional information, and systematically testing the agent’s responses for correctness, robustness, and usability. Through this work, the student will gain hands-on experience with large language models, scientific APIs, and modern software development practices. The project supports broader goals of making data-driven materials research more accessible and effective.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Material Modeling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Materials Engineering
- Mechanical Engineering
- Computer Science
- Engineering (First Year)
- Computer Engineering
Desired experience:
▪ Be available to work in person over the summer. ▪ Have experience with Python, or similar programming tools for data analysis or computational tasks. ▪ Have interest in LLMs, AI agents, or autonomous scientific systems. ▪ Be comfortable learning new software tools and working with structured input/output data. ▪ Have interest in workflow automation, scientific computing, or engineering simulations. ▪ Have basic familiarity with version control (Git) or be willing to learn. ▪ Have prior experience, or strong interest, in building small computational workflows or automating analysis tasks. ▪ Be able to document results clearly and communicate findings effectively.
School/Dept.:
School of Materials Engineering
Professor:
Alejandro
Strachan
More information: https://www.strachanlab.org/
Epitaxial growth of wafer scale semiconductor thin films
Description:
In this project, the student will use the molecular beam epitaxy (NBE) system in the PI's lab and develop the growth of single crystal, high quality, wafer scale semiconductor films. The student will have the opportunity to learn about MBE and other thin film and semiconductor characterization techniques.
Campus:
West Lafayette
Research categories:
Material Processing and Characterization, Microelectronics, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Physics
- Materials Engineering
- Electrical Engineering
School/Dept.:
Physics and Astronomy
Professor:
Tiancong
Zhu
More information: https://sites.google.com/view/zhulab/home
Evaluating Microvascular and Oxygenation Responses of the Plantar Foot to Heat-Based Therapeutic Strategies in Type 2 Diabetes and Peripheral Artery Disease
Description:
Problems with blood flow in the feet are very common in people with type 2 diabetes (T2D) and peripheral artery disease (PAD). When the small blood vessels in the foot do not work properly, the tissues receive less oxygen. Over time, this can lead to numbness, weakness, slow healing, and, in severe cases, foot ulcers. These complications greatly reduce mobility and quality of life. Because of this, researchers are interested in simple, non-drug treatments—such as controlled heat therapy—that may improve circulation in the lower leg and foot.
Our research group recently completed several studies measuring how the small blood vessels in the foot respond to heating and changes in blood flow. We examined how fast oxygen returns to the foot after briefly stopping circulation and how the body reacts when heat and gentle compression are applied to the lower leg. In these studies, individuals with PAD showed much slower and smaller improvements in blood flow and oxygenation, while people with diabetes showed intermediate responses.
The goal of this SURF project is to build on these findings by studying how heat-based treatments influence foot circulation in the short term. The student will help measure changes in skin blood flow, tissue oxygen levels, and leg blood flow while participants undergo controlled heating or brief blood flow occlusion. To do this, we will use noninvasive tools such as near-infrared light sensors, laser-based blood flow monitors, and ultrasound. The results of this project will help us understand whether simple warming strategies could support foot health in people who are at risk for poor circulation.
Our research group recently completed several studies measuring how the small blood vessels in the foot respond to heating and changes in blood flow. We examined how fast oxygen returns to the foot after briefly stopping circulation and how the body reacts when heat and gentle compression are applied to the lower leg. In these studies, individuals with PAD showed much slower and smaller improvements in blood flow and oxygenation, while people with diabetes showed intermediate responses.
The goal of this SURF project is to build on these findings by studying how heat-based treatments influence foot circulation in the short term. The student will help measure changes in skin blood flow, tissue oxygen levels, and leg blood flow while participants undergo controlled heating or brief blood flow occlusion. To do this, we will use noninvasive tools such as near-infrared light sensors, laser-based blood flow monitors, and ultrasound. The results of this project will help us understand whether simple warming strategies could support foot health in people who are at risk for poor circulation.
Campus:
West Lafayette
Research categories:
Cardiovascular Disease Research
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Health and Kinesiology
Professor:
Bruno
Roseguini
Evaluating the Metabolic and Skeletal Outcomes in a Novel Murine Model of Alcohol Use Disorder and Type 2 Diabetes
Description:
Alcohol use disorder (AUD) has been diagnosed in 1 in 10 Americans. Type 2 Diabetes (T2D) has been diagnosed in 1 in 9 Americans. Both AUD and T2D have been found to increase fracture risk. The exact source of increased fracture risk is not fully understood in either disease. Furthermore, AUD and T2D often impact the same patients, leading to worse health outcomes and more elevated fracture risk than either disease alone. Despite this, research focused on the bone outcomes in these patients are limited. This is partly driven by a lack of animal models. Prior work in our lab has established a combined murine model but noticed significant areas of improvement we hope to address through continued model iterations. Through these iterations, we will improve the ability of researchers to model AUD and T2D while bridging the gap in assessing skeletal outcomes in these models. In doing this, we will contribute to characterizing the skeletal impacts of these diseases with a goal of reducing fracture risk and improving overall health outcomes in patients with these diseases.
Campus:
Indianapolis
Research categories:
Biological Characterization and Imaging, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
Desired experience:
Applicants must have taken an introductory course in biomechanics.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Joseph
Wallace
More information: https://engineering.purdue.edu/BBML/
Evalutation of novel radiotherapeutic agents in dogs.
Description:
Ongoing dog osteosarcoma study in collaboration with the vet school. This is a large-scale project, and our lab is responsible for multiple analyses, including CBC, chemistry panels, and PK studies. Students primary role would be to conduct in vitro assays using samples collected at the vet school.
Excellent organizational skills and reliability in following protocols.
Excellent organizational skills and reliability in following protocols.
Campus:
West Lafayette
Research categories:
Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Madduri
Srinivasarao
More information: https://www.chem.purdue.edu/low/index.html
Expanding and Evaluating Autonomous Scientific Workflows on nanoHUB
Description:
Autonomous AI systems are becoming increasingly important in scientific computing because they can plan and carry out complex simulation and data analysis tasks automatically. nanoHUB’s new agent-based platform, AURA, has shown that AI can complete multi-step scientific workflows in minutes rather than the hours typically required by manual work (10.26434/chemrxiv-2025-rs07k). This project builds on that capability by focusing on how new computational workflows can be designed so that AI systems can use them reliably and efficiently.
In this project, the student will play an active role in developing new computational workflows that expand the range of scientific tasks AURA can perform. The student will help design these workflows, define their required inputs and outputs, and deploy them on the nanoHUB platform. This work will give the student hands-on experience translating scientific ideas into usable computational tools.
The student will develop practical skills in Python programming, scientific computing, and workflow automation, as well as experience working with modern research software platforms. They will test how AURA uses the new workflows autonomously and compare AI-driven execution with manual execution to evaluate accuracy, speed, and reliability.
By the end of the project, the student will have gained experience in AI-enabled research, reproducible computational workflows, and evaluation of automated systems. Their contributions will directly support ongoing research infrastructure development on nanoHUB while preparing them for future work in computational science, data science, or engineering research.
In this project, the student will play an active role in developing new computational workflows that expand the range of scientific tasks AURA can perform. The student will help design these workflows, define their required inputs and outputs, and deploy them on the nanoHUB platform. This work will give the student hands-on experience translating scientific ideas into usable computational tools.
The student will develop practical skills in Python programming, scientific computing, and workflow automation, as well as experience working with modern research software platforms. They will test how AURA uses the new workflows autonomously and compare AI-driven execution with manual execution to evaluate accuracy, speed, and reliability.
By the end of the project, the student will have gained experience in AI-enabled research, reproducible computational workflows, and evaluation of automated systems. Their contributions will directly support ongoing research infrastructure development on nanoHUB while preparing them for future work in computational science, data science, or engineering research.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Materials Engineering
- Mechanical Engineering
- Computer Engineering
- Computer Science
- Aeronautical and Astronautical Engineering
- Engineering (First Year)
Desired experience:
▪ Be available to work in person over the summer.
▪ Have experience with MATLAB, Python, or similar programming tools for data analysis or computational tasks.
▪ Have interest in LLMs, AI agents, or autonomous scientific systems.
▪ Be comfortable learning new software tools and working with structured input/output data.
▪ Have interest in workflow automation, scientific computing, or engineering simulations.
▪ Have basic familiarity with version control (Git) or be willing to learn.
▪ Have prior experience, or strong interest, in building small computational workflows or automating analysis tasks.
▪ Be able to document results clearly and communicate findings effectively.
School/Dept.:
School of Materials Engineering
Professor:
Alejandro
Strachan
More information: https://www.strachanlab.org/
Experimental Analysis of Granular Flow in Hoppers
Description:
The undergraduate researcher will design a set of experiments to characterize the flow behavior in hoppers operating under different conditions. The student will also work with others on the project team to help gather data to validate new computational models. The data will also be shared and presented to industry sponsors.
Campus:
West Lafayette
Research categories:
Fluid Modelling and Simulation, Material Modeling and Simulation, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Chemical Engineering
Desired experience:
Fluid mechanics
School/Dept.:
School of Mechanical Engineering
Professor:
Aaron
Morris
Experimental and numerical characterization of low-GWP working fluids for thermal systems
Description:
Experimental and numerical characterization of thermo-physical properties of low-global warming potential (GWP) refrigerants and lubricants.
- Testing support
- Test stand modification
- Post-processing of experimental results
- Development of semi-empirical correlations of results
- Testing support
- Test stand modification
- Post-processing of experimental results
- Development of semi-empirical correlations of results
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Energy and Environment
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Riley
Barta
Exploring Ultrasound Contrast Agents
Description:
UCSAs are typically made by encapsulating a hydrophobic gas in a lipid monolayer [17, 21]. They can also be created via emulsification of a high density, hydrophobic liquid. One limitation of the emulsion-based agents is that they have lower contrast than gas filled agents and typically do not provide sufficient contrast for use in cardiovascular applications. Dr. Solorio’s lab has focused on developing liquid emulsion contrast agents composed of a protein shell that becomes highly echogenic after a thermocycling process in which the emulsion is heated and then cooled. After thermal cycling, they provide sufficient signal to allow detection in circulation and have shown enhanced echogenecity for at least 12 hours. Due to the stable nature of the nanoemulsion, the echogenic particles can be modified with targeting ligands to accumulate in inflamed tissues. These USCAs can be designed to deliver therapeutics in response to an external stimulus such as focused low frequency ultrasound. The research objective is to apply the novel contrast agents to study vascular pathology. USCAs will be used to 1) detect superficial plaque abnormalities such as ulcerations, 2) measure neovascularization of plaques, and 3) detect aneurysms. Fellows will gain research skills related to 1) understanding basic principles of ultrasound, 2) developing an understanding of the current limitations of contrast agents, 3) learning how to apply physical theory to clinically relevant systems, and 4) developing an understanding of contrast agent design.
Campus:
West Lafayette
Research categories:
Medical Science and Technology
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Luis
Solorio
FDM 3D Printing of Energetics
Description:
This project will utilize propellant and pyrotechnic formulations to 3D print various samples. Filament formulations will be varied to explore printability and burn rate. 3D printed samples will be evaluated to determine explosive and combustion characteristics such as jetting, penetration, burn rate, fireball, and impulse. This project will consist of material formulation, filament production, 3D printing solids, 3D modeling, charge preparation, test setup, high-speed camera operation, and data analysis.
Campus:
West Lafayette
Research categories:
Heterogeneous Integration, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/Energetics
Fabrication of atomic clean 2D heterostructures
Description:
In this project, the student will learn how to use mechanical exfoliation to isolate individual two-dimensional material flakes, and use them to create designed heterostructures with atomic clean surface. The student will also have opportunity to learn characterization techniques such as atomic force microscopy and scanning tunneling microscopy
Campus:
West Lafayette
Research categories:
Material Processing and Characterization, Microelectronics, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Physics
School/Dept.:
Physics and Astronomy
Professor:
Tiancong
Zhu
More information: https://sites.google.com/view/zhulab/home
Fracture Networks Under Stress
Description:
This project uses 3D X-ray tomography to examine the interactions among multiple topological elements in response to physical processes that affect fracture networks connectivity, fluid mixing, and elastic wave energy partitioning. The goal is to determine which portions of a fracture network and which intersections are affected by displacement fields that are generated by different global versus local stress configurations.
A SURF student working on this project would learn 3D printing to create fracture networks with multiple topological elements, 3D X-ray imaging and image analysis to extract the change in the topological elements that compose the network. An understanding of X-ray imaging and fracture interactions would be acquired during the project. No prior knowledge is needed.
A SURF student working on this project would learn 3D printing to create fracture networks with multiple topological elements, 3D X-ray imaging and image analysis to extract the change in the topological elements that compose the network. An understanding of X-ray imaging and fracture interactions would be acquired during the project. No prior knowledge is needed.
Campus:
West Lafayette
Research categories:
Energy and Environment
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Physics and Astronomy
Professor:
Laura
Pyrak-Nolte
From fracture to flow: how calving ice sheet affect ocean dynamics
Description:
When glaciers in Greenland calve, they release icebergs that mix with sea ice to form a dense granular material known as ice mélange. This mélange plays a critical role in fjord dynamics by transmitting stress to glacier fronts and driving ocean circulation during calving events. In this SURF project, an undergraduate student will design and conduct controlled hydraulic flume experiments to study the drag forces between floating ice mélange and the underlying ocean flow. Experimental results will be used to calibrate and validate computational fluid dynamics (CFD) simulations in OpenFOAM, leading to a new parameterization of mélange–ocean drag for large-scale ocean models. Students will gain hands-on experience with laboratory instrumentation (PIV), fluid dynamics, data analysis, and numerical modeling, contributing to our understanding of how Greenland’s glaciers interact with the ocean in a warming climate.
Campus:
West Lafayette
Research categories:
Ecology and Sustainability, Energy and Environment, Environmental Characterization, Fluid Modelling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Lyles School of Civil Engineering
Professor:
yue
meng
More information: https://olivmeng.github.io/
Functional genomic screening to define PFAS neurotoxic mechanism conferring AD risk
Description:
The project aims aims to elucidate sub-cellular compartment homeostasis that is disrupted by developmental PFAS exposure, including PFOA, PFBA, and GenX, that collectively contribute to neurotoxicity later in life by increasing cellular vulnerability to established neuro-risk factors associated with neurodegeneration. Participating students will perform stem cell culturing and carry out transcriptomic and epigenomic analysis after harvested cells. The student will learn stem cell biology and bioinformatic analysis.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biotechnology Data Insights, Cellular Biology
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Chongli
Yuan
Generative AI for the discovery of new drugs
Description:
Recent advancements in generative AI, e.g., transformers, diffusion, have found tremendous success in language and image generation. The same technologies have started being applied successfully for designing molecules with desired properties. These approaches hold the promise of accelerating drug discovery by orders of magnitude since they replace a more or less random search with a more informative guided search of the space of all possible molecules. In this project, the team will investigate two approaches, (1) equivariant 3D-conditional diffusion and (2) generative flow networks , and their application to the design of new drugs.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Arezoo
Ardekani
Gradual Verification: Assuring Programs Incrementally
Description:
Description and Significance:
Software verification is the process of ensuring that a piece of software does what it is intended to do (i.e., ensuring the code adheres to its specification). Software verification is important for all software systems, but particularly so, for critical systems such as control systems for aircraft and nuclear power plants. However, static verification---the technique often applied to such systems---cannot support incrementality. It involves writing detailed specifications (often in a formal logic) on system components, such as pre- and postconditions for functions. To prove that a function adheres to its postcondition (e.g. that a findMax function returns the maximal element of a list), tools demand many additional specifications. Worse even, is that a single specification cannot be checked for correctness without providing all the required specifications.
The idea of Gradual verification [1,2,3,4] was introduced to solve this problem and make static verification more useful in practice. Gradual verification supports the incremental specification and verification of programs through the sound and principled application of static (at compile-time) and dynamic (at run-time) techniques---static techniques are applied where possible and dynamic ones where necessary. As a result, a gradual verifier allows developers to pick and choose which properties and components of their system to specify without any unnecessary effort and receive immediate verification feedback. Since the introduction of gradual verification [1], we have extended the approach to support more practical programs, such as those containing recursive heap data structures (trees, graphs, lists, etc.) [2,3,4].
Student Involvement:
Students who work with me this summer will have the opportunity to work on a gradual verification project of their choosing. I have several interesting new projects for gradual verification that involve theory, building tools, and/or studying human behavior. For example, I am interested in how machine learning and large language models can be used to generate specifications supported by gradual verification. I am also interested in building a gradual verifier for the C programming language and exploring the educational impact of gradual verification through user studies. See the projects page on my website for detailed project information: https://jennalwise.github.io/projects/. Each of these endeavors will allow students to gain experience with programming language design, software development, theory, formal reasoning, logic, and empirical studies. Additionally, my goal is for students who work with me this summer to present and/or publish their work at conferences (as many of my students have done in the past).
[1] Johannes Bader, Jonathan Aldrich, and Éric Tanter. "Gradual Program Verification." In International Conference on Verification, Model Checking, and Abstract Interpretation, pp. 25-46. Springer, Cham, 2018.
[2] Jenna Wise, Johannes Bader, Cameron Wong, Jonathan Aldrich, Éric Tanter, and Joshua Sunshine. "Gradual verification of recursive heap data structures." Proceedings of the ACM on Programming Languages 4, no. OOPSLA (2020): 1-28.
[3] Jenna DiVincenzo, Ian McCormack, Conrad Zimmerman, Hemant Gouni, Jacob Gorenburg, Jan-Paul Ramos-Dávila, Mona Zhang, Joshua Sunshine, Éric Tanter, and Jonathan Aldrich. “Gradual C0: Symbolic Execution for Gradual Verification.” ACM Trans. Program. Lang. Syst. 46, 4, Article 14 (December 2024), 57 pages. https://doi.org/10.1145/3704808
[4] Conrad Zimmerman, Jenna DiVincenzo, and Jonathan Aldrich. "Sound Gradual Verification with Symbolic Execution." Proceedings of the ACM on Programming Languages 8, no. POPL (2024): 2547-2576.
Software verification is the process of ensuring that a piece of software does what it is intended to do (i.e., ensuring the code adheres to its specification). Software verification is important for all software systems, but particularly so, for critical systems such as control systems for aircraft and nuclear power plants. However, static verification---the technique often applied to such systems---cannot support incrementality. It involves writing detailed specifications (often in a formal logic) on system components, such as pre- and postconditions for functions. To prove that a function adheres to its postcondition (e.g. that a findMax function returns the maximal element of a list), tools demand many additional specifications. Worse even, is that a single specification cannot be checked for correctness without providing all the required specifications.
The idea of Gradual verification [1,2,3,4] was introduced to solve this problem and make static verification more useful in practice. Gradual verification supports the incremental specification and verification of programs through the sound and principled application of static (at compile-time) and dynamic (at run-time) techniques---static techniques are applied where possible and dynamic ones where necessary. As a result, a gradual verifier allows developers to pick and choose which properties and components of their system to specify without any unnecessary effort and receive immediate verification feedback. Since the introduction of gradual verification [1], we have extended the approach to support more practical programs, such as those containing recursive heap data structures (trees, graphs, lists, etc.) [2,3,4].
Student Involvement:
Students who work with me this summer will have the opportunity to work on a gradual verification project of their choosing. I have several interesting new projects for gradual verification that involve theory, building tools, and/or studying human behavior. For example, I am interested in how machine learning and large language models can be used to generate specifications supported by gradual verification. I am also interested in building a gradual verifier for the C programming language and exploring the educational impact of gradual verification through user studies. See the projects page on my website for detailed project information: https://jennalwise.github.io/projects/. Each of these endeavors will allow students to gain experience with programming language design, software development, theory, formal reasoning, logic, and empirical studies. Additionally, my goal is for students who work with me this summer to present and/or publish their work at conferences (as many of my students have done in the past).
[1] Johannes Bader, Jonathan Aldrich, and Éric Tanter. "Gradual Program Verification." In International Conference on Verification, Model Checking, and Abstract Interpretation, pp. 25-46. Springer, Cham, 2018.
[2] Jenna Wise, Johannes Bader, Cameron Wong, Jonathan Aldrich, Éric Tanter, and Joshua Sunshine. "Gradual verification of recursive heap data structures." Proceedings of the ACM on Programming Languages 4, no. OOPSLA (2020): 1-28.
[3] Jenna DiVincenzo, Ian McCormack, Conrad Zimmerman, Hemant Gouni, Jacob Gorenburg, Jan-Paul Ramos-Dávila, Mona Zhang, Joshua Sunshine, Éric Tanter, and Jonathan Aldrich. “Gradual C0: Symbolic Execution for Gradual Verification.” ACM Trans. Program. Lang. Syst. 46, 4, Article 14 (December 2024), 57 pages. https://doi.org/10.1145/3704808
[4] Conrad Zimmerman, Jenna DiVincenzo, and Jonathan Aldrich. "Sound Gradual Verification with Symbolic Execution." Proceedings of the ACM on Programming Languages 8, no. POPL (2024): 2547-2576.
Campus:
West Lafayette
Research categories:
Cybersecurity, Human Factors, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
- Computer Engineering
- Mathematics - Computer Science
Desired experience:
Students must have taken at least two programming courses or have equivalent programming background.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Jenna
DiVincenzo
More information: https://jennalwise.github.io/projects/
Heat-shock in Colombian Phureja Potato
Description:
It is anticipated that global warming will greatly reduce the yield of potatoes in the Andes regions. In collaboration with Dr. Esperanza Torres at UNAL, we are using transcriptome data (RNASeq) to identify the mechanism of heat-shock resistance in 3 lines of phureja potato. This analysis will identify differentially expressed genes between resistant lines and a temperature-sensitive line, and will identify genes that may be useful for breeding programs that can improve
thermotolerance in traditional potato varieties. The student will gain experience in high-performance computing, big-data analysis, differential expression analysis, gene annotation, and analysis of the causal mechanisms underlying the observed thermotolerance.
thermotolerance in traditional potato varieties. The student will gain experience in high-performance computing, big-data analysis, differential expression analysis, gene annotation, and analysis of the causal mechanisms underlying the observed thermotolerance.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biotechnology Data Insights, Cellular Biology, Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Biological Sciences
Professor:
Michael
Gribskov
High efficiency HVAC membranes
Description:
Convective drying processes consume a substantial amount of energy within the manufacturing sector and often rely on inefficient processes. A novel closed-cycle architecture which combines water vapor selective membranes (which allow water vapor transport while blocking air) is proposed, with a unique heat pump design for efficient thermal management (also dubbed as MemDry). Closed-cycle heat pump-driven convective drying processes have been implemented in the past; however, such systems suffer from two major drawbacks: (1) they cool post-process air to induce condensation, and condensing water vapor requires a significant amount of energy and (2) the cooled air must be reheated before being used in a drying process again. Our proposed MemDry cycle can provide upwards of 80% energy savings over conventional heat pump convective drying cycles by combining novel nanomaterials with clever system design to avoid to need for energy expenditures associated with cooling/reheating and water vapor condensation. Furthermore, the cycle can achieve upwards of 30-40% second law efficiency, further improving upon conventional technologies. Additionally, providing heat via an electric-powered heat pump cuts back on CO2 emissions compared to a combustion-driven drying process.
The student who joins this lab will be expected to attend weekly lab meetings and sub-group meetings. They will be focusing on prototype development and evaluation. Contributions will include 3D CAD modeling, framework manufacturing, system control and data acquisition design, membrane material fabrication and testing, and ample opportunities to assist with technical writing efforts (journal articles, conference papers, grant submissions, and patent application). Once the prototype device is developed and optimized, the student will be able to assist with experiments and will be taught to run some of the experiments on their own as well. The student will be expected to give weekly progress updates and will have the opportunity to present their projects at different symposiums and conferences. The student should also be eager to collaborate with other researchers outside of mechanical engineering to draw from a broad range of expertise in pursuing our research goals.
The student who joins this lab will be expected to attend weekly lab meetings and sub-group meetings. They will be focusing on prototype development and evaluation. Contributions will include 3D CAD modeling, framework manufacturing, system control and data acquisition design, membrane material fabrication and testing, and ample opportunities to assist with technical writing efforts (journal articles, conference papers, grant submissions, and patent application). Once the prototype device is developed and optimized, the student will be able to assist with experiments and will be taught to run some of the experiments on their own as well. The student will be expected to give weekly progress updates and will have the opportunity to present their projects at different symposiums and conferences. The student should also be eager to collaborate with other researchers outside of mechanical engineering to draw from a broad range of expertise in pursuing our research goals.
Campus:
West Lafayette
Research categories:
Fluid Modelling and Simulation, Material Modeling and Simulation, Thermal Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Enthusiasm for energy systems, and long-term dedication to undergraduate research. We need a mix of skills including for prototyping, modeling, graphic design, business/economic calculations, and education outreach. Most recruits have good GPA's, but we may make exceptions for especially dedicated applicants. Some specific needed skills include:
• Experience with CAD modeling (CATIA, SOLIDWORKS, etc.) is necessary.
• Some background in prototype development/manufacturing or membrane materials/fabrication will be useful.
• Experience with LabVIEW control and data acquisition is beneficial. Course-related experience is also very helpful.
• Experience with MATLAB and Engineering Equation Solver will be useful for assisting inside projects related to the technology we are developing.
• Student should have a strong interest in thermodynamics and controls, along with a general motivation
School/Dept.:
School of Mechanical Engineering
Professor:
Davide
Ziviani
More information: https://www.warsinger.com/
High-throughput Electrocatalysis with Metal Oxides
Description:
This project is part of a broader Center for Chemical Innovation (CCI) focused on high-throughput and high-resolution characterization of single nanocrystals. In this project, undergraduate students will develop knowledge and skills in materials synthesis and characterization, electrocatalysis, and high-throughput experimentation.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Chemistry
- Chemical Engineering
Desired experience:
completed coursework in general chemistry, organic chemistry
School/Dept.:
Chemistry
Professor:
Christina
Li
More information: https://www.chem.purdue.edu/li/
HumanAI Collaboration for Physical Task Support
Description:
Physical machine repair and maintenance require an accurate understanding of a device’s structure, state, and operating logic. While modern AI systems have strong reasoning capabilities, they often lack the task-specific context needed to provide precise suggestions, ask useful follow-up questions, or support troubleshooting. This is especially challenging in repair, where critical evidence is often hidden and must be obtained through physical inspection and manipulation. We propose a context-aware human-AI collaboration paradigm for physical task support. The AI proactively guides users to discover and gather the knowledge needed for diagnosis and repair. In this collaboration, the human contributes embodied access to the physical world through manipulation, inspection, and experimentation, while the AI contributes reasoning, information seeking, and adaptive guidance.
Undergraduate students will contribute primarily to system development, including building AR applications and implementing computer vision features for object recognition and state tracking. They will use tools and technologies such as Unity, AR frameworks, and vision models for real-time interaction. Through this work, they will develop skills in interactive system prototyping, human-centered AI design, and HCI research methods, including user study design and evaluation.
Undergraduate students will contribute primarily to system development, including building AR applications and implementing computer vision features for object recognition and state tracking. They will use tools and technologies such as Unity, AR frameworks, and vision models for real-time interaction. Through this work, they will develop skills in interactive system prototyping, human-centered AI design, and HCI research methods, including user study design and evaluation.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Internet of Things (IoT), Mobile Computing
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Karthik
Ramani
Identifying drug treatments for functional recovery after spinal cord injury
Description:
Spinal cord injury is a significant human health problem affecting about 300,000 people in the US. Better treatment options are needed to overcome the limited regeneration potential of the human spinal cord. Zebrafish larvae are an emerging model system for drug screening for several reasons including large number of embryos per breeding, genetics, and availability of behavioral assays for drug testing. Our lab has conducted a large-scale drug screen with an FDA-approved library to identify novel compounds that enhance functional recovery following injury as assessed by a swimming assay. We are currently characterizing the effects of some of these drugs in more detail with the goal of identifying compounds of high efficacy and specificity. The student will be involved with fish breeding, spinal cord injury, drug treatment, and behavioral assay. We hope that this work will identify new compounds with translational potential for treatment of human spinal cord injury.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Cell Biology, Biochemistry, Neurobiology
School/Dept.:
Biological Sciences
Professor:
Daniel
Suter
More information: https://suterlab.bio.purdue.edu
Image-Based Modeling of Blood Flow and Transport in the Brain
Description:
Blood flow plays a critical role in brain function in both health and disease. Abnormal blood flow dynamics are linked to a wide range of vascular and neurological disorders, including cerebral aneurysms, atherosclerosis, vessel wall inflammation, and neurodegenerative diseases. Medical imaging can be combined with computational modeling to study blood flow in patient-specific vascular geometries. These image-based models allow researchers to quantify biomechanical forces generated by blood flow and to investigate how these forces influence the transport of contrast agents and drugs used for diagnostics and therapeutics, as well as their interaction with surrounding brain tissues.
Work in this project will involve developing patient-specific computational models of blood flow and transport using medical images of cerebral blood vessels. Using Magnetic Resonance Imaging (MRI) data, vascular geometries will be segmented and used to construct three-dimensional models of the cerebral vasculature. These models will then be used to simulate blood flow and the coupled transport of contrast agents and drugs through cerebral blood vessels.
The student will gain hands-on experience in medical image processing, vascular segmentation, three-dimensional modeling, and computational simulations of coupled flow and transport processes. The project will provide exposure to interdisciplinary research at the interface of biomedical engineering, medical imaging, and computational flow modeling.
Work in this project will involve developing patient-specific computational models of blood flow and transport using medical images of cerebral blood vessels. Using Magnetic Resonance Imaging (MRI) data, vascular geometries will be segmented and used to construct three-dimensional models of the cerebral vasculature. These models will then be used to simulate blood flow and the coupled transport of contrast agents and drugs through cerebral blood vessels.
The student will gain hands-on experience in medical image processing, vascular segmentation, three-dimensional modeling, and computational simulations of coupled flow and transport processes. The project will provide exposure to interdisciplinary research at the interface of biomedical engineering, medical imaging, and computational flow modeling.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cardiovascular Disease Research, Fluid Modelling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Interest in biomedical engineering, fluid dynamics, or computational modeling. Helpful background includes basic mathematics and programming (e.g., MATLAB or Python). No prior experience with medical imaging or modeling is required.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Vitaliy
Rayz
More information: https://engineering.purdue.edu/CFML
Immunoengineering for next-generation immunotherapies
Description:
This project focuses on advancing next-generation immunotherapies for challenging diseases—including cancer and neurological disorders—by integrating immunoengineering with high-level genetic editing of immune cells both ex vivo and in vivo. By designing and studying modified immune cell populations such as NK cells, the research aims to better understand and enhance their therapeutic potential. In parallel, the project develops organoid systems, including brain-like tissues, to create physiologically relevant platforms for examining how engineered immune cells interact with complex disease environments. Together, these approaches provide a powerful framework for uncovering disease mechanisms, optimizing immune-based interventions, and accelerating the translation of innovative therapies. The student will be involved in experimental aspects of the project and carry out studies in one or multiple areas involving the evaluation of immune engineering approaches to improve immune cell function, development and assembly of organoid systems and functional and analytical evaluation of the molecular, engineering and therapeutic aspects of immune-disease settings. In addition to collecting data, the student will analyze data, perform visualizations and present their research findings.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Industrial and Physical Pharmacy
Professor:
Sandro
Matosevic
More information: www.matoseviclab.com
In-vivo assessment of low frequency alternating current modulation of peripheral nerve activity using dual electrode stimulation
Description:
Direct application of Low Frequency Alternating Current stimulation on peripheral nerves using cuff electrodes has been shown to modulate nerve conduction and activation, blocking nerve fibers at lower amplitudes, and activating nerve fibers in a sizewise order with larger amplitudes. In-silico modeling of the phenomenon points to inactivation of the low threshold sodium channels, which differentially presents itself as a spacially as a function of the distance of the nerve fiber. These models further predict that complete temporal nerve conduction block, and spatially selective activation is possible when two electrodes are used. The aim of this project is to develop and perform experiments to search for evidence of these predictions in-vivo. The student will translate the in-silico experiments to the in-vivo case, conduct the pilot studies, and analyze the data produced. Both physical and software tools will need to be developed and/or modified. The skill sets to be developed include 3D design and printing of physical tools (Fusion360, Formlabs), development, testing and use of software analysis tools (Python, Matlab), and analysis of the results with respect to the exant in-silico experimental results.
Campus:
Indianapolis
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Biomedical Engineering
Desired experience:
3D CAD experience, Matlab experience
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Ken
Yoshida
Independent Component Analysis of fMRI time-series
Description:
This project will use, and implement as needed, reliable subject-level independent component analysis (ICA), to improving subject-level network estimation beyond standard dual regression, to characterize large-scale brain networks during rest, rest–task or rest-task fMRI paradigms in clinical and healthy populations. Subject-specific ICA spatial maps, time courses, and inter-component functional network connectivity will be extracted for each condition to (i) identify discriminative biomarkers distinguishing clinical participants from controls, (ii) quantify task-induced network reconfiguration and post-task recovery signatures relative to baseline rest, and (iii) evaluate individual “fingerprinting” accuracy based on ICA features within and across conditions. By integrating template-guided ICA, rigorous cross-validated predictive modeling, and fingerprinting analyses, this project will determine whether ICA-derived network representations provide reliable, interpretable, and clinically meaningful biomarkers of brain dysfunction and individual identity, advancing the use of network-level fMRI signatures for mechanistic understanding and precision neuroscience.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
- Industrial Engineering
- Electrical Engineering
- Computer Engineering
Desired experience:
imaging, time-series analyses, data-mining, critical thinking.
School/Dept.:
Edwardson School of Industrial Engineering
Professor:
Joaquin
Goni Cortes
More information: https://engineering.purdue.edu/ConnplexityLab
Inertial Microfluidics: Analyzing Flow Visualization Data from the NIST Cytometer
Description:
Microfluidic devices are increasingly used for lab-on-a-chip technology for medical diagnostics. “Microfluidics” refers to fluid mechanics at the micron scale (10^-6 m). In this project, we focus on inertial microfluidics, where small geometries combine with high flow speed to produce non-intuitive inertial effects. One such phenomenon is inertial migration: rigid particles suspended inflow drift toward a small number of stable focusing positions, a behavior that emerges from the nonlinear Navier-Stokes equations.
A key open question is: at what spatial scale does the particle’s inertial migration influence the surrounding flow field? To investigate this, and in collaboration with NIST Gaithersburg, we have collected fluorescence video data of particles moving through a microfluidic channel. The images reveal a characteristic U-shaped depletion zone around the particle.
The goal of this project is to analyze the experimental video data, compare it to existing finite-element simulations, and to develop a reduced-order MATLAB model to better understand the underlying flow physics. Students will gain experience in image processing, computational modeling, and interpreting fluid-mechanical experiments.
A key open question is: at what spatial scale does the particle’s inertial migration influence the surrounding flow field? To investigate this, and in collaboration with NIST Gaithersburg, we have collected fluorescence video data of particles moving through a microfluidic channel. The images reveal a characteristic U-shaped depletion zone around the particle.
The goal of this project is to analyze the experimental video data, compare it to existing finite-element simulations, and to develop a reduced-order MATLAB model to better understand the underlying flow physics. Students will gain experience in image processing, computational modeling, and interpreting fluid-mechanical experiments.
Campus:
West Lafayette
Research categories:
Fluid Modelling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
Desired experience:
Linear Algebra, Differential Equations. Programming in MATLAB, Critical thinking, Good Communication Skills. Fluid mechanics or image processing is a bonus.
School/Dept.:
Mathematics
Professor:
Kaitlyn
Hood
Inertial Microfluidics: Analyzing Flow Visualization Data from the NIST Cytometer
Description:
Microfluidic devices are increasingly used for lab-on-a-chip technology for medical diagnostics. “Microfluidics” refers to fluid mechanics at the micron scale (10^-6 m). In this project, we focus on inertial microfluidics, where small geometries combine with high flow speed to produce non-intuitive inertial effects. One such phenomenon is inertial migration: rigid particles suspended inflow drift toward a small number of stable focusing positions, a behavior that emerges from the nonlinear Navier-Stokes equations.
A key open question is: at what spatial scale does the particle’s inertial migration influence the surrounding flow field? To investigate this, and in collaboration with NIST Gaithersburg, we have collected fluorescence video data of particles moving through a microfluidic channel. The images reveal a characteristic U-shaped depletion zone around the particle.
The goal of this project is to analyze the experimental video data, compare it to existing finite-element simulations, and to develop a reduced-order MATLAB model to better understand the underlying flow physics. Students will gain experience in image processing, computational modeling, and interpreting fluid-mechanical experiments.
A key open question is: at what spatial scale does the particle’s inertial migration influence the surrounding flow field? To investigate this, and in collaboration with NIST Gaithersburg, we have collected fluorescence video data of particles moving through a microfluidic channel. The images reveal a characteristic U-shaped depletion zone around the particle.
The goal of this project is to analyze the experimental video data, compare it to existing finite-element simulations, and to develop a reduced-order MATLAB model to better understand the underlying flow physics. Students will gain experience in image processing, computational modeling, and interpreting fluid-mechanical experiments.
Campus:
West Lafayette
Research categories:
Fluid Modelling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
Desired experience:
Linear Algebra, Differential Equations. Programming in MATLAB, Critical thinking, Good Communication Skills. Fluid mechanics or image processing is a bonus.
School/Dept.:
Mathematics
Professor:
Kaitlyn
Hood
Investigating Bolted Connections for SpeedCore Steel Coupling Beams
Description:
As part of the American Institute of Steel Construction's Need for Speed initiative, this project investigates bolted connections to use steel coupling beams in the SpeedCore system. SpeedCore is an innovative composite wall system in which two exterior steel faceplates are connected by tie bars and filled with concrete—much like a structural “sandwich.” This system allows for an estimated 43% reduction in construction time, enhanced ductility, and increased strength when compared to traditional reinforced concrete core construction in high-rise structures.
The student will work at Robert L. and Terry L. Bowen Laboratory supporting ongoing research aimed at further improving the SpeedCore system. Responsibilities will include assisting graduate research assistants in constructing large‑scale test specimens for destructive testing, helping set up instrumentation, contributing to data collection and interpretation, and engaging in research discussions throughout the project. This role provides hands‑on experience with experimental structural engineering and exposure to cutting‑edge innovations in steel‑concrete composite systems.
The student will work at Robert L. and Terry L. Bowen Laboratory supporting ongoing research aimed at further improving the SpeedCore system. Responsibilities will include assisting graduate research assistants in constructing large‑scale test specimens for destructive testing, helping set up instrumentation, contributing to data collection and interpretation, and engaging in research discussions throughout the project. This role provides hands‑on experience with experimental structural engineering and exposure to cutting‑edge innovations in steel‑concrete composite systems.
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Engineering the Built Environment, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Civil Engineering
School/Dept.:
Lyles School of Civil Engineering
Professor:
Amit
Varma
More information: https://engineering.purdue.edu/Bowen
Investigating Calcium-dependence signaling in a protein pathway and their effect on actin remodeling in the egg-to-embryo transition of mammalian eggs
Description:
Calcium (Ca2+) signaling is a fundamental element of life, being involved in intracellular signaling pathways that are known for maintaining cellular homeostasis. In mammalian eggs, Ca2+ signaling mediates the egg-to-embryo transition, a developmental event composed of a series of events that transform a mature oocyte (egg) into a developing embryo following fertilization. Increased cytosolic Ca2+ induces key developmental events including the completion of meiosis, progression to embryonic mitosis, and the establishment of the egg's preventative mechanisms that block fertilization by more than one sperm (known as blocks to polyspermy). Calcium ions exert their effects through calcium-dependent effector molecules, and Ca2+/Calmodulin-dependent kinase II (CaMKII) is directly involved with exit from metaphase II stage and mediating events in early embryogenesis. It is known that the gamma isoform of CaMKII (CaMKIIy) mediates multiple events of the egg-to-embryo transition, as evidence from studies that CaMKIIy knockout female mice are infertile due to egg activation defects. Furthermore, CaMKII has numerous downstream effectors, including T-cell lymphoma invasion and metastasis 1 (Tiam1), a Rac1 guanine nucleotide exchange factor (GEF) phosphorylated by CaMKII in other biological systems such as neurons. Tiam1 activates small G-proteins Rac1 and Cdc42, which places Tiam1 as a potential mediator of membrane block establishment for actin cytoskeleton remodeling. Despite Tiam1 transcript being enriched in mammalian eggs and testes in mice, there are no known roles of Tiam1 in the mammalian egg-to-embryo transition. Here, we investigate the Ca2+-dependent activation of Tiam1 and its association to CaMKIIy throughout meiosis and early egg activation stages. Oocytes from CF-1 female mice were used for collection and superovulation was achieved through injections with pregnant mare serum gonadotropin (PMSG) and human chorionic gonadotropin (hCG) for the in vitro fertilization (IVF) experiments. Expression of Tiam1 protein in oocytes was evaluated using immunoblotting and immunofluorescence; and Tiam1/CaMKIIy co-localization was analyzed through proximity ligation assays (PLA). We detected both Tiam1 and CaMKIIy (control) protein presence in Prophase I, Metaphase II, and fertilized egg lysate through immunoblotting. Immunofluorescence images show Tiam1 presence in both cytoplasm and cortex in Prophase I, Metaphase II, and fertilized eggs. PLA results for Tiam1 and CaMKIIy in Metaphase II and fertilized eggs indicate a promising mechanism for studying the co-localization of both Tiam1 and CaMKIIy under varying Ca2+ manipulations. Future studies include: (1) investigating the potential phosphorylation of Tiam1 upon Ca2+ and CaMKII-activation using a PhosTag acrylamide assay for differential separation of proteins based on their phospho-state, followed by immunoblotting, (2) a GTPase-linked immunosorbent assay (GLISA) to measure the Ca2+-dependent activation state of a small GTPase, Cdc42, and its co-dependency on Tiam1/CaMKIIy, and (3) characterizing the impact on sperm-induced actin remodeling from manipulations to Tiam1 with a reverse genetics approach known as Trim-Away. These studies will elucidate the factors and pathways of Tiam1 and CaMKIIy that contribute to the Ca2+ -dependent signaling events in the egg-to-embryo transition.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Technical and laboratory skills: Student(s) chosen for this project will be working with animals (mice); collecting oocytes (mammalian eggs) from animals through dissection; performing oocyte collection and media prep for oocyte culture; perform in-vitro fertilization (IVF) for collecting early embryos; using a widefield microscope to capture microscopic images of prepared samples; and use ImageJ software for analyzing microscope images for quantification methods. Basic coding skills may be necessary as well.
Qualifications and performance expectations: Students(s) must demonstrate intellectual curiosity and persistence in experimental inquiry; have a strong foundation in cell and molecular biology; and exhibit attention to detail in organized notetaking, critical thinking, and quantitative reasoning in both experimental design and data interpretation. Attendance is expected for weekly laboratory meetings to discuss research progress and receive feedback for areas of improvement. Clear communication of program requirements, experiment handling, and mentoring needs are strongly encouraged.
Expected outcomes for student development: Through this project, student(s) will be able to:
Laboratory skills - (1) handle working with rodents; (2) learn the concepts of oocyte handling; (3) perform immunofluorescence experiments using microscopy; (4) perform IVF; (5) and perform image quantification and statistical analysis for gather generated
Individual development – (1) Technical writing skills; (2) critical thinking and results interpretation; (3) experimental design validation; (4) teamworking skills on collaborative project; and (5) professional presentation skills (oral and poster) .
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Tamara
Kinzer-Ursem
More information: https://engineering.purdue.edu/ursemlab
Investigating SLGT-2i Efficacy in Treating Cardiomyopathy in Duchenne Muscular Dystrophy
Description:
This project explores the potential of SGLT-2i treatment in preventing cardiomyopathy in a murine model of Duchenne muscular dystrophy. Students will assist with echocardiographic and histopathologic image acquisition and analysis to quantify functional changes in the heart and determine whether SGLT-2i therapy prevents a decline in function loss. Students will develop skills in rodent handling, ultrasound image acquistion and analysis, animal dissection, histological analysis, and descriptive and analytical statistics. Students will use a high frequency ultrasound machine, anesthesia machines, blood pressure monitoring equipment, ultrasound image analysis software, histology image analysis software, and may need to code in MATLab, Python, and/or R.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cardiovascular Disease Research
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Introductory coursework in anatomy and physiology, statistics, biology, and chemistry. Desired skills include coding experience in R, Python, MATLab. Previous experience in animal/rodent handling is preferred, but not required.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Craig
Goergen
More information: https://engineering.purdue.edu/cvirl
Investigating the Anti-cancer Role of Vitamin C
Description:
The aim of this project is to explore the novel anti-cancer role of vitamin C through inhibiting the enzymatic activity of a key oncoprotein identified by our lab. The candidate will be trained for basic skills of cell culture, protein purification, biochemical assay development, etc. A co-authored paper of this project is expected to be submitted in this fall.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology, Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Medicinal Chemistry and Molecular Pharmacology
Professor:
Qingfei
Zheng
More information: https://www.mcmp.purdue.edu/faculty/zhengqf
Investigating the Expansion of Thermally Expandable Materials
Description:
This project involves utilizing thermally expandable microspheres (TEM) to create a “foaming” polymer/epoxy material. A TEM is a gas filled polymer capsule that expands as the gas is heated. This expansion allows for polymer formulations to grow in volume, giving the appearance of a foaming process like spray foam insulation. Students will be responsible for rheological, thermal expansion, and curing rate experiments. Students will gain competency in rheology, expanding materials, and testing thermal and mechanical properties. By the end of the project, the students will have characterized the expansion properties of polymer formulations.
Campus:
West Lafayette
Research categories:
Material Processing and Characterization
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Stephen
Beaudoin
Investigating the Oncogenic Function of a Neurotransmitter
Description:
Our lab recently discovered a novel post-translational modification (PTM) on an essential amino acid of a key oncoprotein, KRAS, which is induced by a neurotransmitter, serotonin. In this project, we aim to understand the oncogenic functions of this unique PTM on KRAS and develop drug leads to target it for cancer therapy. The candidate will be trained for the basic knowledge and skills of protein/peptide chemistry, cell biology, and drug discovery.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology, Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Medicinal Chemistry and Molecular Pharmacology
Professor:
Qingfei
Zheng
More information: https://www.mcmp.purdue.edu/faculty/zhengqf
Investigation of Undergraduate Problem-Solving Approaches in Interdisciplinary Contexts
Description:
Project Summary: Assignments from a bioanalytical chemistry course and think aloud interviews conducted with various life science majors have been collected to understand how students process problems in interdisciplinary contexts. Through qualitative analysis of these data pieces this project aims to characterize the knowledge students use in interdisciplinary contexts when problem solving and what problem features cue them to integrate knowledge from multiple disciplines.
Student role/expectations: By participating in this project the student will build foundational qualitative research skills. This includes how to deductively and inductively analyze qualitative data via the QDATA software NVIVO, how to properly handle human subjects research data, and how to properly record qualitative analysis/data interpretations. They will also assist in the cleaning of think aloud interview transcripts prior to analysis.
Student role/expectations: By participating in this project the student will build foundational qualitative research skills. This includes how to deductively and inductively analyze qualitative data via the QDATA software NVIVO, how to properly handle human subjects research data, and how to properly record qualitative analysis/data interpretations. They will also assist in the cleaning of think aloud interview transcripts prior to analysis.
Campus:
West Lafayette
Research categories:
Learning and Evaluation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Some biology and chemistry background is helpful. We will teach you the qualitative methods for the research!
School/Dept.:
Biological Sciences
Professor:
Stephanie
Gardner
More information: https://www.bio.purdue.edu/lab/gardner/
IoT4Ag: AgBot Field Operations
Description:
The goal of this project is to support the field operations of a small fleet of autonomous mobile agricultural ground robots, AgBots. The AgBot can be configured for IoT sensor deployment, sensor reading, and in-row navigation, monitoring, and physical sampling. A robotic trailer for transporting the team of AgBots to the field also houses a charging station for the robots and serve as a communication base station for the team. The trailer is attached to an autonomous Polaris Ranger utility task vehicle (UTV). The AgBot team will be deployed multiple times a week over the growing season to perform sensor deployment, reading, crop monitoring, and physical sampling tasks. Students on this project will work with project mentors on mechanical design improvements to the robotic trailer system, sensor deployment system, and the physical sampling storage system; incorporating capabilities for AgBot autonomous sensor readings in the field; autonomous approaches for AgBot fleet deployment from the trailer; and path planning and implementation of in-field AgBot rendezvous with the Polaris Ranger UTV for battery recharging. Field tests will be conducted at the Purdue University Agronomy Center for Research and Education (ACRE) facility.
Campus:
West Lafayette
Research categories:
Deep Learning, IoT for Precision Agriculture, Other
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Mechanical Engineering
- Electrical Engineering
- Computer Engineering
- Computer Science
- Agricultural Engineering
School/Dept.:
School of Mechanical Engineering
Professor:
David
Cappelleri
More information: https://iot4ag.us/
Iron deficiency and quality of life in perimenopausal women
Description:
The goal of this randomized controlled trial (RCT) is to understand the effects of iron status and supplementation on perimenopausal women’s cognitive performance, family relationships, and overall quality of life. Perimenopause, or the menopause transition, is associated with significant hormonal and reproductive changes in women. Evidence documents interindividual differences in the symptoms associated with perimenopause. One of the most common symptoms is perimenopausal menorrhagia (PM), defined as abnormally heavy bleeding, which may place a woman at risk for iron deficiency (ID) and iron deficiency anemia (IDA). Women with PM frequently complain of fatigue and impaired work performance, which align with symptoms resulting from ID among other symptoms. While these symptoms, along with changes in cognitive performance and affect, have been shown to occur with ID and IDA in women of reproductive age, the underlying causes of many symptoms experienced during perimenopause remain ill-defined. Given the prevalence of PM and the known association between iron status, cognition, and affect in young women of reproductive age, we propose to measure specific biomarkers of iron status and to assess women’s cognitive performance, mood, and quality of family relationships as well as life during perimenopause. Women will be categorized based on iron status and groups will be compared within timepoint to assess the relationship between varying levels of iron status and the outcome variables and across time points to assess the effects of supplementation. This study will be the first to examine the effects of iron status on cognition, mood, and quality of life during this important life transition and the data will be instrumental when determining the dose and duration of iron supplementation needed for optimal improvement in these women. Findings from this study will begin to fill critical gaps in our knowledge and serve to inform policy relating to the surveillance and treatment of iron deficiency in women during perimenopause.
Student role in the project:
The undergraduate mentee will be expected to actively contribute to various aspects of the project, gaining exposure to micronutrient research. Key responsibilities include: 1. Engagement in development of research protocols: The mentee will be introduced to study design, randomization, and regulatory aspects, including ethical considerations. 2. Interacting with participant and data collection: The mentee will assist in participant recruitment, scheduling, and data collection, including the informed consent process, collection of health history, demographic information, assessment of mood and quality of family relationships, and measurements of cognitive abilities. 3. Data processing (laboratory techniques): The mentee will gain hands-on experience with blood processing and measurement of iron biomarkers via standard laboratory procedures, including ELISAs. 4. Data management and analysis: Mentee will be involved with data entry, cleaning, and preliminary analysis and will be expected to contribute to the interpretation of outcomes. 5. Collaboration and dissemination of findings: Mentee is expected to actively participate in lab meetings, present findings, collaborate with the research team, and aid in the write-up and publication of the findings.
Student role in the project:
The undergraduate mentee will be expected to actively contribute to various aspects of the project, gaining exposure to micronutrient research. Key responsibilities include: 1. Engagement in development of research protocols: The mentee will be introduced to study design, randomization, and regulatory aspects, including ethical considerations. 2. Interacting with participant and data collection: The mentee will assist in participant recruitment, scheduling, and data collection, including the informed consent process, collection of health history, demographic information, assessment of mood and quality of family relationships, and measurements of cognitive abilities. 3. Data processing (laboratory techniques): The mentee will gain hands-on experience with blood processing and measurement of iron biomarkers via standard laboratory procedures, including ELISAs. 4. Data management and analysis: Mentee will be involved with data entry, cleaning, and preliminary analysis and will be expected to contribute to the interpretation of outcomes. 5. Collaboration and dissemination of findings: Mentee is expected to actively participate in lab meetings, present findings, collaborate with the research team, and aid in the write-up and publication of the findings.
Campus:
West Lafayette
Research categories:
Medical Science and Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Background/interest in biomedical sciences preferred but not required
School/Dept.:
Nutrition Science
Professor:
Laura
Murray-Kolb
More information: https://hhs.purdue.edu/directory/laura-murray-kolb/
Isolation of a Monoester from other Esterification Products
Description:
The goal of this project is to separate the products of an esterification reaction. Students will learn the fundamentals of wiped-film evaporation and chromatography to be able to isolate one. They will gain skills in one of those two systems, HPLC, and NMR analysis. By the end of the project, students will have obtained the operating parameters necessary to successfully isolate one of the reaction products.
Campus:
West Lafayette
Research categories:
Chemical Unit Operations, Material Processing and Characterization
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
Desired experience:
Some previous lab experience, and a good attitude.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Stephen
Beaudoin
Joint solar energy harvesting and wireless communications
Description:
This project involves the design of small solar-powered wireless sensors that can be used to monitor soil health and environmental conditions.
Campus:
West Lafayette
Research categories:
Energy and Environment, IoT for Precision Agriculture, Microelectronics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering Technology
- Electrical Engineering
- computer engineering
Desired experience:
electronic circuits
digital logic design
electronic prototyping
analog circuit design
troubleshooting
School/Dept.:
School of Engineering Technology
Professor:
Walter
Leon Salas
Large Language Models (LLMs) Fine-Tuning for Biological Engineering Education
Description:
Objective: Enhance an existing LLM-based Tutor to personalize students’ learning.
Key Tasks:
1. Perform a literature search and extract meaningful action points for Socratic learning using LLMs
2. Enhance an existing LLM using a curriculum learning approach
3. Evaluate model performance on advanced evaluation tasks
4. Compare with general-purpose LLMs and traditional teaching methods
Outcome: Specialized LLM for improved Biological Engineering education. This internship offers hands-on experience in AI and Biological Engineering, which is ideal for engineering students interested in EdTech and Biological Engineering and Sciences.
Preferable majors: Biological Engineering, Agricultural Engineering, Computer Science.
Key Tasks:
1. Perform a literature search and extract meaningful action points for Socratic learning using LLMs
2. Enhance an existing LLM using a curriculum learning approach
3. Evaluate model performance on advanced evaluation tasks
4. Compare with general-purpose LLMs and traditional teaching methods
Outcome: Specialized LLM for improved Biological Engineering education. This internship offers hands-on experience in AI and Biological Engineering, which is ideal for engineering students interested in EdTech and Biological Engineering and Sciences.
Preferable majors: Biological Engineering, Agricultural Engineering, Computer Science.
Campus:
West Lafayette
Research categories:
Learning and Evaluation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Skills Required: Machine Learning, Natural Language Processing
Desired: An exposure to LLMs and advance its applications in educational settings
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Martin
Okos
Large Language Models (LLMs) for Personalized Tutoring
Description:
This project provides undergraduate students with hands-on research and development experience in large language model (LLM)–based educational systems. The intern will contribute to enhancing an existing AI tutor by developing features informed by the literature, conducting basic evaluation, integrating the frontend, deploying on a GPU-enabled platform, and producing formal research reporting. Mentorship and technical guidance will be provided throughout the internship.
Key tasks:
1. Conduct a focused literature review on LLM-based AI tutoring systems and identify feasible enhancement opportunities.
2. Design and implement research-informed feature enhancements to an existing LLM tutor.
3. Develop and execute a basic evaluation protocol to assess functionality and usability.
4. Contribute to frontend development using React and assist with GPU-enabled deployment on Purdue’s Anvil platform.
5. Document technical work, evaluation results, and research outcomes in a final written report.
Key tasks:
1. Conduct a focused literature review on LLM-based AI tutoring systems and identify feasible enhancement opportunities.
2. Design and implement research-informed feature enhancements to an existing LLM tutor.
3. Develop and execute a basic evaluation protocol to assess functionality and usability.
4. Contribute to frontend development using React and assist with GPU-enabled deployment on Purdue’s Anvil platform.
5. Document technical work, evaluation results, and research outcomes in a final written report.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Successful candidates should demonstrate foundational programming experience (e.g., Python and/or JavaScript), familiarity with AI or machine learning concepts, and the ability to read and synthesize technical literature. Experience with web development frameworks (preferably React), version control, and basic software testing is desirable. Candidates must show strong written communication skills, the ability to work independently under mentorship, and a commitment to ethical and responsible use of AI systems.
Interested students should submit supporting documents (e.g., prior coursework, research papers/GitHub repositories, deployed applications, or technical reports) demonstrating relevant skills. Each submission must briefly explain relevance to the project tasks. Evidence may span multiple skill areas. Selection prioritizes demonstrated capability, clarity of communication, and readiness to contribute independently.
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Dharmendra
Saraswat
Laser Ignition of Boron Doped Particle
Description:
The ignition behavior of single particles has been studied for a variety of alloying materials, and it is known that doped and alloy particles have enhanced burning affects in propellants. The single particle ignition of these dopped particles would provide insight into reason the particles have these effects. It is expected that the doping and alloying materials would reduce the energy necessary for a single particle to burn. Using single particle ignition of boron, a material with a high potential energy, we aim to quantify the effect of the doping and alloying materials for the minimal energy required for ignition.
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Energy and Environment, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Prefer Sophomore, Junior or Senior in ME, AAE, MSE, or ChE.
School/Dept.:
School of Mechanical Engineering
Professor:
Steven
Son
More information: https://www.sciencedirect.com/science/article/pii/S0010218025000732
Learning Optimization Through Play: Designing Decision-Driven Educational Games for STEM Education
Description:
Project Overview:
This undergraduate research project explores how digital games can be used to teach core Mathematical Optimization concepts through interactive decision-making. Students will design and develop playable game modules where players implicitly solve optimization problems—such as inventory control, routing, or competitive decision-making—through gameplay mechanics rather than traditional equations.
The project blends computer science, game design, and applied mathematics, allowing students to translate abstract optimization principles into engaging, intuitive player experiences.
________________________________________
Research Objectives:
1. Translate optimization problems into gameplay mechanics
Investigate how classical optimization topics (e.g., inventory management, routing, Stackelberg games) can be represented as meaningful player decisions.
2. Design and implement educational game modules
Develop one or more digital game prototypes that embed optimization logic within gameplay.
3. Evaluate learning and engagement outcomes
Study whether players improve their understanding of optimization concepts through gameplay.
________________________________________
Core Research Questions:
• How can mathematical optimization principles be embedded into game mechanics without requiring formal math notation?
• Which types of gameplay (turn-based, real-time, competitive, collaborative) best convey optimization trade-offs?
• Does gameplay-based learning improve conceptual understanding and decision-making intuition in STEM topics?
________________________________________
Technical Implementation:
Students will:
• Use Unity, Godot, Unreal Engine, or Blender for game development.
• Implement optimization logic using object-oriented programming (e.g., C#, Python, GDScript, C++).
• Develop AI agents using rule-based or heuristic optimization approaches.
• Integrate data tracking to log player decisions and outcomes.
________________________________________
Skills Students Will Develop:
• Game design and prototyping
• Object-oriented and logic programming
• Applied optimization modeling
• User experience (UX) and learning assessment
• Interdisciplinary research communication
________________________________________
Suitable For:
• Junior and senior undergraduates
• Majors in Computer Science, Computer Engineering, Software Engineering, or Applied Math
• Students interested in game development, AI, simulation, or educational technology
This undergraduate research project explores how digital games can be used to teach core Mathematical Optimization concepts through interactive decision-making. Students will design and develop playable game modules where players implicitly solve optimization problems—such as inventory control, routing, or competitive decision-making—through gameplay mechanics rather than traditional equations.
The project blends computer science, game design, and applied mathematics, allowing students to translate abstract optimization principles into engaging, intuitive player experiences.
________________________________________
Research Objectives:
1. Translate optimization problems into gameplay mechanics
Investigate how classical optimization topics (e.g., inventory management, routing, Stackelberg games) can be represented as meaningful player decisions.
2. Design and implement educational game modules
Develop one or more digital game prototypes that embed optimization logic within gameplay.
3. Evaluate learning and engagement outcomes
Study whether players improve their understanding of optimization concepts through gameplay.
________________________________________
Core Research Questions:
• How can mathematical optimization principles be embedded into game mechanics without requiring formal math notation?
• Which types of gameplay (turn-based, real-time, competitive, collaborative) best convey optimization trade-offs?
• Does gameplay-based learning improve conceptual understanding and decision-making intuition in STEM topics?
________________________________________
Technical Implementation:
Students will:
• Use Unity, Godot, Unreal Engine, or Blender for game development.
• Implement optimization logic using object-oriented programming (e.g., C#, Python, GDScript, C++).
• Develop AI agents using rule-based or heuristic optimization approaches.
• Integrate data tracking to log player decisions and outcomes.
________________________________________
Skills Students Will Develop:
• Game design and prototyping
• Object-oriented and logic programming
• Applied optimization modeling
• User experience (UX) and learning assessment
• Interdisciplinary research communication
________________________________________
Suitable For:
• Junior and senior undergraduates
• Majors in Computer Science, Computer Engineering, Software Engineering, or Applied Math
• Students interested in game development, AI, simulation, or educational technology
Campus:
West Lafayette
Research categories:
Learning and Evaluation, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Desired Technical Skills:
Programming & Development
• Proficiency in at least one object-oriented language (e.g., C#, C++, Java, Python)
• Experience with logic-based or rule-based programming
• Familiarity with version control systems (e.g., Git/GitHub)
Game Development:
• Experience with at least one game engine or development tool:
o Unity
o Godot
o Unreal Engine
o Blender (for modeling and assets)
• Understanding of game mechanics, physics, and player interaction
Optimization & Simulation:
• Ability to implement algorithms for:
o Shortest path / routing
o Scheduling
o Inventory control
• Experience with simulations or agent-based modeling is a plus
________________________________________
Desired Special Skills:
• Strong problem-solving and analytical thinking
• Ability to translate abstract mathematical ideas into interactive systems
• Creativity in game design and storytelling
• Debugging and testing complex systems
• Basic UI/UX design principles
________________________________________
Training & Experience (Preferred, Not Required):
• Prior experience developing a complete game or simulation project
• Experience working on team-based software projects
• Exposure to educational or “serious games”
• Experience with AI agents, heuristics, or decision-making models
• Participation in hackathons, game jams, or research projects
School/Dept.:
Edwardson School of Industrial Engineering
Professor:
Danial
Davarnia
Learning for humanoid robot whole-body control
Description:
Humanoid robots have the potential to assist humans in daily life, factories, and disaster response. For robots to work safely and effectively alongside people, they must be able to understand and respond appropriately to human actions and preferences. Although modern robots can already walk, balance, and navigate complex environments, making them interact naturally and safely with humans remains a major challenge. This summer project will focus on studying how humans interact behaviorally with humanoid robots and exploring simple methods to make this interaction more intuitive and trustworthy. The student will help design and conduct experiments using Purdue’s Digit humanoid robot, observing how humans respond to different robot behaviors (e.g., speed, distance, or gesture style). Using collected data, the student will analyze patterns in human comfort, trust, and cooperation. Depending on the student’s background, additional tasks may include: (1) Implementing simple control or perception algorithms for the robot in simulation or hardware, (2) Analyzing human-robot interaction data using Python or MATLAB, and (3) Assisting with demonstrations or visualization of robot behaviors. Learning Outcomes: By the end of the project, the student will gain experience in: (1) Fundamental principles of human–robot interaction, (2) Hands-on work with a humanoid robot (Digit), (3) Experimental design and data analysis, and (4) Basic programming for robot control or behavioral analysis.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Electrical Engineering
- Computer Science
- Computer Engineering
- Mathematics - Computer Science
Desired experience:
Python, C++
Dynamics modeling, controls, optimization
Data collection
School/Dept.:
School of Mechanical Engineering
Professor:
Yan
Gu
More information: https://www.thetracelab.com/
Light-weight dual-use detector support structure for Particle Physics
Description:
The project is situated in the field of detector mechanics which is dealing with the design and construction of detector support structures that are light-weight and radiation hard, as well as able to efficiently cool electronic circuitry, aka silicon sensors and other detectors for charged particles. Detectors are for future colliders such as the electron-ion collider or the future circular collider, high-luminosity Large Hadron Collider or even the muon Collider.
Students will execute and implement finite element analysis of stave/dee structures able to cool detectors and as a first step also verify deflection remain minimal under load. Software such as Ansys will be provided, access to lab space for eventual prototypes and cooling labs, mechanical test setup all exists as part of the Jung lab.
Students will execute and implement finite element analysis of stave/dee structures able to cool detectors and as a first step also verify deflection remain minimal under load. Software such as Ansys will be provided, access to lab space for eventual prototypes and cooling labs, mechanical test setup all exists as part of the Jung lab.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Big Data/Machine Learning, Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Physics
Desired experience:
Experience in FEA work and thermal aspects of integrated circuitry, preferential composite engineering experience or aerospace engineering.
School/Dept.:
Physics and Astronomy
Professor:
Andy
Jung
More information: https://www.physics.purdue.edu/jung/
Liquid Level Meter for a Dark Matter R&D Detector
Description:
The student will design and build a capacitor and use a high-precision capacitance-to-digital converter to develop a liquid level meter. They will learn how to install and operate the capacitance meter’s GUI, acquire and analyze the data, and construct a model that relates capacitance to liquid height. The resulting model should have predictive capability for different liquids. This device will ultimately be used in an R&D liquid noble-gas detector for dark matter detection.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Physics
School/Dept.:
Physics and Astronomy
Professor:
Qing
Xia
Lithographic Processing of Ferroelectric Oxides
Description:
Research Topic, Ferroelectric Memory: The microelectronics game remains the same as it has for decades. Do more, faster, under more extreme conditions and in a smaller footprint. For memory, demands have especially intensified. The push for near-, or in-memory, compute along with application drivers for systems that operate in ever more severe environments requires alternative approaches. In response, emergent ferroelectric memory technologies based on wurtzite and fluorite crystal structures are being increasingly pursued. Fluorite ferroelectrics based on hafnia (e.g. Hf(Zr)O2), meanwhile, exhibit requisite scaling and are CMOS compatible suggesting a way to shrink the process-memory gap. Despite this promise, each technology remains unable to meet endurance targets. It is therefore necessary to “find and fix” the defect(s) limiting lifetime. To do so, methods of processing these materials must be developed that “help” them work more than “hurt” them.
What’ You’ll Do: Team members will be responsible for developing lithographic processing recipes for optimizing the performance of both planar and non-planar ferroelectric hafnia devices. This will require demonstrated lithographic ability to be employed in Birck Nanotechnology Center. In addition, Specifically, you will use photoluminescence (fancy for glow in the dark) to assess defects induced during the processing. Direct mentoring from Dr. B will build your skills in advanced spectroscopic tools (Raman, photoluminescence), coding, technical communication and professional development. In addition, you will have the chance to participate in writing journal articles and pursuing patents based on your work.
Who we are… Specere is a latin word that means “to look or behold.” That’s what we do. We look, explore, and examine different ways to: (1) move energy with light and (2) get information from light. More specifically, we are a light lab employing physics to create spectroscopic, thermal, and sensing solutions.
Campus:
West Lafayette
Research categories:
Heterogeneous Integration, Material Processing and Characterization, Microelectronics, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Industrial Engineering
- Materials Engineering
- Mechanical Engineering
Desired experience:
Lithographic processing and clean room experience is central to this position.
School/Dept.:
School of Mechanical Engineering
Professor:
Thomas
Beechem
More information: www.specere.org
Machine Learning for Semiconductor Packaging Automation
Description:
The student will collect training data for machine learning applications that can be used to automate measurements of component orientations using machine vision. Validation techniques will be developed and the performance of these algorithms will then be quantified. These algorithms will then be used to automate some of the robotic processes used to package semiconductors with associated flex circuits needed for building high-speed pixelated radiation sensors. Benefits over manual operations will be quantified in terms of speed, accuracy, and robustness.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Fabrication and Robotics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Physics and Astronomy
Professor:
Matthew
Jones
Machine LearningDriven Reconstruction of Cardiac Flow MRI: Harmonization, Training, and Quantification
Description:
This project develops an end-to-end pipeline for transforming raw 4D Flow MRI data into high-quality reconstructions and clinically relevant hemodynamic metrics, with a focus on pediatric patients with congenital heart defects. Students will contribute to machine learning–based image reconstruction, data harmonization, and flow quantification to improve the accuracy and usability of cardiac MRI. Work will include cleaning and organizing MRI datasets, training and evaluating deep learning models, and extracting flow features such as wall shear stress, regurgitant fraction, and energy loss. The project bridges machine learning, fluid dynamics, and medical imaging for direct clinical impact.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
- Computer Science
- Mechanical Engineering
Desired experience:
Proficiency in Python and NumPy (experience with PyTorch or JAX is a plus)
Familiarity with basic fluid mechanics or vector calculus
Interest in medical imaging, computational modeling, or machine learning
Bonus: Experience with Linux, scientific visualization (ParaView, VTK, or Plotly), or GPU computing
School/Dept.:
School of Mechanical Engineering
Professor:
Ilias
Bilionis
More information: https://predictivesciencelab.org/
Making Invisible Sleep Signals Visible: Multisensory State Estimation Using Smart Textiles
Description:
This project investigates how soft, fabric-embedded sensors respond to physical conditions associated with sleep, and how multiple sensor signals can be combined to infer physiological states. The work establishes engineering foundations for noninvasive developmental monitoring systems.
Campus:
West Lafayette
Research categories:
Heterogeneous Integration, Internet of Things (IoT), Microelectronics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Background in electrical engineering, biomedical engineering, physics, or a related field. Coursework or experience in circuits, sensors, signal processing, or embedded systems is preferred. Familiarity with MATLAB or Python, experimental data analysis, or hands-on lab/robotics work is a plus. Students should be comfortable working with quantitative data and interested in experimental research.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
MUHAMMAD
HUSSAIN
Mass spectrometry of biomolecules and nanoclusters
Description:
We are using mass spectrometry to study the localization of lipids, drugs, and proteins in biological tissues and to prepare novel functional interfaces using well-defined polyatomic ions. The student will work with a graduate student mentor to either perform nanocluster synthesis and characterization using mass spectrometry and electrochemical measurements or to develop new analytical approaches for quantitative analysis of biomolecules in biological samples. We are also developing computational approaches for connecting mass spectrometry imaging data with biochemical pathways. In both projects, the student will be trained to operate state-of-the-art mass spectrometers and perform independent data acquisition and analysis. The student will also work with scientific literature to obtain a broader understanding of the field.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Chemical Catalysis and Synthesis
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
General chemistry, analytical chemistry and labs
School/Dept.:
Chemistry
Professor:
Julia
Laskin
More information: https://www.chem.purdue.edu/jlaskin/
Material properties of fish skin mucus
Description:
Student will be responsible for collecting and testing the material properties of fish skin mucus from a variety of fishes in both salt and freshwater environments. Student may also perform simple manipulations of both fishes and mucus to test how environment and temperature changes mucus properties. Student will develop skills in experimental methods, imaging, and properties of visco-elastic fluids. Student will also gain experience in writing and presenting their work.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Ecology and Sustainability, Fluid Modelling and Simulation, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Biological Sciences
Professor:
Dylan
Wainwright
More information: https://www.dylanwainwright.com/
Mathematical modeling to forecast the 2026 US Elections
Description:
Election forecasting involves polling likely voters, weighting polling data, combining it with other information (e.g., how the economy is doing), and accounting for uncertainty. In the project, we will use publicly-available polling data, together with mathematical modeling, to produce forecasts of the 2026 US senatorial and gubernatorial elections. We will produce forecasts of the 2026 US elections and post our forecasts on our website. We will write code for handling polling data and weighting it in new ways.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Background in linear algebra and differential equations, programming skills (especially Matlab; R, Html, or Javascript are also helpful), enthusiasm for interdisciplinary research and collaboration
School/Dept.:
Mathematics
Professor:
Alexandria
Volkening
More information: https://c-r-u-d.gitlab.io/2024/
Micro Morphing Aircraft
Description:
Micro Morphing Aircraft are palm-sized unmanned vehicles that change their geometry in real time, adjusting camber, twist, and effective planform through lightweight, compliant mechanisms and micro-actuators. The goal is to keep control authority and efficiency in gusty, constrained environments. The technical core is structure–control co-design: morphing surfaces provide aerodynamic leverage when traditional control surfaces saturate, while embedded control algorithms coordinate shape change and actuation commands under tight computational limits. Selected students are expected to bring a builder's mindset and reliability, along with foundational skills in one or more of the following areas: mechanical design and rapid prototyping, embedded systems and sensors, or modeling and control.
Students in this role will contribute to the full research cycle: conceiving and refining morphing concepts; fabricating lightweight mechanisms and skins; integrating low-SWaP avionics, GPS/IMU sensing, and micro-actuation; and implementing onboard guidance and control algorithms suitable for resource-constrained hardware.
Students in this role will contribute to the full research cycle: conceiving and refining morphing concepts; fabricating lightweight mechanisms and skins; integrating low-SWaP avionics, GPS/IMU sensing, and micro-actuation; and implementing onboard guidance and control algorithms suitable for resource-constrained hardware.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Aeronautical and Astronautical Engineering
- Mechanical Engineering
- Electrical Engineering
- Robotics Engineering Technology
- Computer Science
Desired experience:
Experience with UAV design, system integration, and experimental flight is required. Applicants are expected to include a description of prior experience in UAV-relevant research and products/results.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Ran
Dai
Microbiome mediated stress resilience in tomato
Description:
The student will develop and conduct experiments to quantify changes in the composition and activity of root microbiomes in a set of tomato genotypes subject to pathogen or salinity stress. They will gain experience and apply the following skills: DNA/RNA extraction, PCR and RT-PCR, amplicon and metatranscriptomic sequencing, bioinformatic programs.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Ecology and Sustainability
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Horticulture & Landscape Architecture
Professor:
Lori
Hoagland
Microbiomes of Controlled Environment Agriculture
Description:
Student will build, operate, and analyze controlled environment agriculture systems (e.g., hydroponics) to further understand microbiome interactions that may aid in plant growth. Students will use molecular microbiological tools, as well as practical engineering, hydraulics, and construction skills
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biotechnology Data Insights, Ecology and Sustainability, Engineering the Built Environment, Environmental Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Experience with hydroponics is a benefit to the project, as is experience with molecular biology tools. Basic understanding of coding, hydraulics, and biology are desired.
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Caitlin
Proctor
Microfluidic system for studying the transport of therapeutics
Description:
The development of a microfluidic model for various therapeutics, ranging from various sizes and concentrations, will be explored. Transport within the hydrogels will be analyzed to generate correlations with the various aspects of the therapeutic solutions. Microfluidic models have been recognized as an interesting alternative to animal models for drug screening. These models can mimic some of the physiological characteristics across solid tumors to the physiological barriers. The therapeutic solution will be injected into one of the microchannels, while the other microchannel wells will have the hydrogel formulations in them. The transport of the therapeutic solution in the hydrogel will be measured by fluorescence microscopy or other techniques.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Arezoo
Ardekani
More information: https://engineering.purdue.edu/ComplexFlowLab/research
Modelica to Embedded C-Code Generation for Aerial Robotics with the Rumoca Compiler
Description:
This undergraduate research fellowship offers an opportunity to contribute to research at the intersection of **aerial robotics, embedded systems, model-based design, and compiler technology**. The project focuses on the **Rumoca** compiler and its use in translating **Modelica-based system models into embedded C code** for real-time robotic and flight applications.
The student will contribute to the development and integration of model-generated software components for use with the **Zephyr RTOS** and **Cognipilot’s Cerebri flight-control software**. The work will support drone and autonomous vehicle applications, with an emphasis on connecting high-level physical and control models to deployable embedded software.
A major part of the project will involve **firmware-in-the-loop simulation** and testing. The student will help interface embedded flight software with native simulation environments in which **sensor behavior is emulated by setting registers in the native binary**, allowing flight code to be exercised and evaluated without requiring full hardware deployment. The fellowship may also include support for drone experiments, software integration, and validation of generated components in simulated and real-world settings.
The student’s role will be hands-on and development-oriented, contributing to software integration, testing, debugging, and experimental validation across the modeling, compilation, and embedded deployment workflow. This work will help advance a toolchain for bringing model-based methods into real-time aerial robotics systems.
The student will contribute to the development and integration of model-generated software components for use with the **Zephyr RTOS** and **Cognipilot’s Cerebri flight-control software**. The work will support drone and autonomous vehicle applications, with an emphasis on connecting high-level physical and control models to deployable embedded software.
A major part of the project will involve **firmware-in-the-loop simulation** and testing. The student will help interface embedded flight software with native simulation environments in which **sensor behavior is emulated by setting registers in the native binary**, allowing flight code to be exercised and evaluated without requiring full hardware deployment. The fellowship may also include support for drone experiments, software integration, and validation of generated components in simulated and real-world settings.
The student’s role will be hands-on and development-oriented, contributing to software integration, testing, debugging, and experimental validation across the modeling, compilation, and embedded deployment workflow. This work will help advance a toolchain for bringing model-based methods into real-time aerial robotics systems.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Cybersecurity, Deep Learning, Microelectronics, System-on-a-Chip
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Aeronautical and Astronautical Engineering
- Computer Engineering
- Electrical Engineering
- Computer Science
Desired experience:
Coursework or experience in **embedded systems, controls, robotics, programming, aerospace systems, mechatronics, or computer systems** is desirable. Helpful experience includes programming in **C/C++ or Rust**, familiarity with **Linux-based development**, and exposure to **real-time software, firmware, simulation, drones, or hardware-software integration**. Experience with **RTOS-based development, embedded debugging, control systems, or robotics platforms** is beneficial, but not required.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
James
Goppert
More information: engineering.purdue.edu/PURT
Modeling catalyst-support interaction in hydrogen fuel cells
Description:
This project will study the interaction of Pt and Pt alloy nanoparticles with carbon support, and its dependence on the defect density, graphitization, surface oxygenated groups and doping of heteroatoms. The goal is to understand how to suppress migration, coalescence and detachment of supported nanocatalysts and associated loss of electrochemically active surface area in hydrogen fuel cells.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Energy and Environment, Material Modeling and Simulation, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Chemical Engineering
- Chemistry
- Materials Engineering
- Physics
Desired experience:
Basic knowledge in quantum mechanics and machine learning force field. Basic experience in python programing.
School/Dept.:
Chemistry
Professor:
Zhenhua
Zeng
More information: https://scholar.google.com/citations?user=RprmJAsAAAAJ&hl=en
Multi-Robot Coordination for Maritime Environments
Description:
Autonomous fleets of surface, underwater, and aerial vehicles promise significant capabilities in environmental monitoring and surveillance operations. Effective coordination in these settings, however, requires global and reliable exchange of monitoring updates and high-level control commands under extreme communication constraints. Our goal is to 1) develop a theoretical framework for multi-robot control and communication, 2) build simulations to verify our algorithms and 3) create system implementations to demonstrate performance gains in the real world. The student working on the SURF project will support all 3 phases - theoretical analysis, simulations, and system development.
Campus:
West Lafayette
Research categories:
Deep Learning, Fabrication and Robotics, Mobile Computing, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
- Coursework in control theory and/or wireless communication/networks and/or optimization would be a plus.
- Some experience working with ROS and/or other robotics platforms would also be a plus.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Vishrant
Tripathi
Multimodal Perception for Robots in Occluded Environments
Description:
The student will learn data collection, processing, and visualization.
Campus:
West Lafayette
Research categories:
Deep Learning, Fabrication and Robotics, Microelectronics, Mobile Computing
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Upinder
Kaur
More information: https://engineering.purdue.edu/ARIES
MycoBotic MIND: Bio-Hybrid Intelligent Sensing via Mycelial Integrated neuro-Electrophysiological Detection for Real-Time Environmental Monitoring in Autonomous Systems
Description:
In this project, we aim to develop bio-hybrid robots using living mycelial networks. The work lies at the intersection of robotics, embedded systems, electronics, and mycology. Students should be motivated and skilled in electronics.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Upinder
Kaur
Neutral atom array trapping on nanophotonic circuits for quantum science and applications
Description:
Trapping cold neutral atoms on nanophotonic circuits enables strong atom-light interactions for applications in quantum science and technology. We are developing an apparatus for trapping atom arrays on nanophotonic structures such as microring resonators or two-dimensional photonic crystals for experiments on many-body quantum optics and quantum communications. A summer research student is expected to utilize a spatial light modulator and high resolution microscope optics to engineer a programmable optical tweezer array for trapping single laser-cooled atoms. It is expected that some advanced algorithms or machine learning can facilitate generation of aberration-free, high resolution light patterns. Participating student should have prior knowledge in Fourier optics and is fluent in python programming. Basic knowledge in quantum mechanics.
Campus:
West Lafayette
Research categories:
Heterogeneous Integration, Nanotechnology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Physics
- Electrical Engineering
- Computer Engineering
Desired experience:
Optics, quantum mechanics, and computer programming skills.
School/Dept.:
Physics and Astronomy
Professor:
Chen-Lung
Hung
More information: https://ultracold.physics.purdue.edu
Neutrophil engager to treat tumor
Description:
The student will help design and produce engagers to mediate neutrophil killing of tumor cells.
Campus:
West Lafayette
Research categories:
Cellular Biology, Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Experiences in molecular cloning, cell culture and protein purification would be desired.
School/Dept.:
Biological Sciences
Professor:
Qing
Deng
More information: https://www.denglab.us/
Next-generation compact and robust 2D and 3D imaging system development
Description:
The student will collaborate closely with a graduate mentor to advance the development of an innovative camera system that meaningfully pushes the boundaries of current imaging technology. This project aims to achieve significant improvements in key performance metrics such as device footprint, imaging accuracy, spatial resolution, and energy efficiency. Throughout the summer, the student will gain hands-on experience in optical system design and assembly, develop and integrate deep learning-based image processing algorithms, and analyze real measurement data collected from the prototype system. The research experience will also include scientific communication training: the student will participate in writing and refining an academic paper to disseminate the research findings. This multidisciplinary project combines elements of optics, machine learning, and experimental validation, and builds upon the research themes showcased on Professor Qi Guo’s laboratory webpage: https://www.qiguo.org/
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Deep Learning, Mobile Computing, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Engineering
- Electrical Engineering
- Computer Science
- Mechanical Engineering
Desired experience:
Signals and Systems, Python and C programming, Microprocessor (optional)
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Qi
Guo
More information: https://www.qiguo.org/
Nondestructive characterization for hybrid additive manufacturing
Description:
The project addresses the challenge of resilient manufacturing in space. We leverage ultrasonics to characterize the mechanical properties of multi-material samples made using metal additive manufacturing and strengthened with secondary processing. The samples combine recycled metal and lunar soil simulant. Researchers will help elucidate the effect of composition and processing on the resulting material properties towards safe and sustainable space manufacturing.
Campus:
West Lafayette
Research categories:
Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Engineering major strongly preferred.
• Interest in Additive Manufacturing, materials, acoustics, and/or signal processing
• Enthusiasm for learning and collaboration
• Preference for hands-on learning
• Hands-on building, fabrication and/or testing is helpful but not required
• Familiarity with basic programming or CAD modeling is helpful but not required
School/Dept.:
School of Mechanical Engineering
Professor:
Luz
Sotelo
Novel Deep Learning Models Informed by Physics and Domain Knowledge
Description:
Deep learning has become one of the most powerful tools in science and engineering, yet many standard neural networks struggle in domains where physical laws must always be respected. For example, a model that predicts chemical processes must obey mass and energy conservation, and a model that makes decisions for an industrial system must follow safety and operational limits. Traditional neural networks learn patterns from data but can easily break these rules, especially when data are limited or noisy. This project introduces undergraduate students to a new kind of machine learning model called Optimization Inspired Neural Networks. Instead of adding physical and logical rules as soft penalties, these models incorporate mathematical optimization principles directly into the structure of the network so that the predictions are guaranteed to follow the required domain knowledge. Students will help develop and test these models on applications drawn from chemical engineering and related fields. Examples include predicting molecular properties while respecting chemistry laws, designing reliable process control models, or generating physically consistent reaction mechanisms. The student will gain experience with modern machine learning frameworks while also learning how scientific principles can be built into AI models to make them more trustworthy and reliable.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Computer Science
- Industrial Engineering
- Electrical Engineering
- Mathematics
- Chemical Engineering
Desired experience:
The ideal student is curious about the intersection of optimization, machine learning, and chemical engineering. A background in Python programming is important. Prior experience with machine learning libraries such as PyTorch or TensorFlow is helpful but not required if the student is motivated to learn. Some familiarity with linear algebra, differential equations, or optimization will make it easier to engage with the project, but the key requirement is enthusiasm for scientific computing and a willingness to explore how physics and domain knowledge can improve AI models. The project is suitable for students from chemical engineering, computer science, applied mathematics, or related areas.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Can
Li
Novel radiation sensors
Description:
We have ongoing sponsored projects with DoE-NNSA to develop a novel class of spectroscopic neutron-gamma radiation sensors in two areas: (1) Using the science of tensioned metastable fluids, and, (2) Renewable biopolymers. The research involves experimentation as well as modeling and simulations. Students are expected to be able to learn from graduate/post-doctoral staff and to conduct a specifically scoped task in the overall portfolio.
Students are expected to have a basic nuclear engineering/physics background and be willing and able to be qualified/trained for conducting experiments with radiation sources.
Students are expected to have a basic nuclear engineering/physics background and be willing and able to be qualified/trained for conducting experiments with radiation sources.
Campus:
West Lafayette
Research categories:
Energy and Environment, Environmental Characterization, Material Processing and Characterization, Medical Science and Technology, Radiation Hardening, Thermal Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
NUCL200 - Introduction to Nuclear Engineering
NUCL300 - Nuclear Structure and Radiation Interactions with Matter
School/Dept.:
School of Nuclear Engineering
Professor:
Rusi
Taleyarkhan
Operation and characterization of SPT-100 Hall thruster
Description:
Hall thrusters are widely utilized for spacecraft propulsion. Mars exploration missions currently planned by NASA utilize Deep Space Transport which is going to be propelled by Hall thruster technology. In Hall thruster neutral gas propellant is ionized and accelerated in ExB-field configuration to reach high propellant exhaust velocities in the range 10 - 50 km/s.
In this project student will work with Hall thruster SPT-100. The project will include operating the thruster and hollow cathode neutralizer, and measurements of electrical parameters of the thruster, exhaust plasma jet properties, and thrust. The student will use Langmuir probes (and other diagnostics) for measurements of plasma parameters and hanging pendulum thrust stand for the thrust measurements. In addition, the student is going to prepare and update related documentation for AAE 521 Plasma Lab.
In this project student will work with Hall thruster SPT-100. The project will include operating the thruster and hollow cathode neutralizer, and measurements of electrical parameters of the thruster, exhaust plasma jet properties, and thrust. The student will use Langmuir probes (and other diagnostics) for measurements of plasma parameters and hanging pendulum thrust stand for the thrust measurements. In addition, the student is going to prepare and update related documentation for AAE 521 Plasma Lab.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Alexey
Shashurin
More information: https://engineering.purdue.edu/EPPL
Optimization of Next-Generation Heat Exchangers to Enable Ultra-Efficient Data Centers
Description:
The Cooling Technologies Research Center (CTRC) is exploring new technologies and discovering ways to more effectively apply existing technologies to addresses the needs of companies and organizations in the area of high-performance heat removal from compact spaces. One of the distinctive features of working in this Center is training in practical applications relevant to industry. All of the projects involve close industrial support and collaboration in the research, often with direct transfer of the technologies to the participating industry members. Current research in the Center is motivated by rapidly evolving technology markets, including the electrification of the transportation sector, exponential growth in the generation, transmission, and processing of data, and the proliferation of interconnected computing devices, which bring new and extreme thermal management challenges. Our work explores new technologies and discovering ways to more effectively predict and control heat transport to enhance the performance and efficiency of thermal management systems in electric vehicles/aircraft, mobile phones, AI & high-performance computers, advanced semiconductors packages, and beyond. The projects typically involve both experimental and computational aspects, and are multi-disciplinary in nature. Our ongoing projects are aligned with major initiatives to develop thermal management systems that will enable carbon-neutral electrified aviation and eliminate energy and water use associated with data center cooling, and use emerging physic-informed machine learning techniques to better understand boiling heat transfer processes, to name a few.
This specific project project aims to develop a new generation of compact heat exchangers (HX) that improves performance while increasing overall compactness (surface area to volume ratio) compared to conventional designs. Such heat exchangers can enable improved efficiency in the thermal management of high-power AI computing data centers. Fabrication of these heat exchangers is enabled by additive manufacturing of the complex geometries, and the exploration of new design spaces is enabled by advanced topology optimization techniques. Throughout this project, the student will gain experience with OpenFOAM, an open-source simulation library, and perform various heat transfer analysis. Also, the student will get exposed to numerical methods for generating/manipulating minimal surfaces to build heat exchangers and physical process for fabricating such objects with additive manufacturing.
This specific project project aims to develop a new generation of compact heat exchangers (HX) that improves performance while increasing overall compactness (surface area to volume ratio) compared to conventional designs. Such heat exchangers can enable improved efficiency in the thermal management of high-power AI computing data centers. Fabrication of these heat exchangers is enabled by additive manufacturing of the complex geometries, and the exploration of new design spaces is enabled by advanced topology optimization techniques. Throughout this project, the student will gain experience with OpenFOAM, an open-source simulation library, and perform various heat transfer analysis. Also, the student will get exposed to numerical methods for generating/manipulating minimal surfaces to build heat exchangers and physical process for fabricating such objects with additive manufacturing.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Big Data/Machine Learning, Energy and Environment, Fluid Modelling and Simulation, Heterogeneous Integration, Material Modeling and Simulation, Microelectronics, Thermal Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
- Chemical Engineering
- Nuclear Engineering
Desired experience:
A list of preferred course work is below; however, the experience is open to any student who is willing to learn these topics/software during the internship.
-Undergraduate courses in fluid dynamics, heat transfer, and thermodynamics
-Familiarity with computer aided design (CAD) software (SolidWorks, CATIA, Fusion 260)
-Familiarity with computational fluid dynamics (CFD) software (OpenFOAM, Ansys, COMSOL, FEniCS)
School/Dept.:
School of Mechanical Engineering
Professor:
Justin
Weibel
More information: https://engineering.purdue.edu/CTRC/research/index.php; https://engineering.purdue.edu/Herrick/research
Optimize Cancer Prevention and Screening Adherence among Breast Cancer Survivors
Description:
The undergraduate student will engage in a mentored summer research experience focused on cancer prevention and survivorship, examining cancer screening patterns and barriers among breast cancer survivors.
Under faculty mentorship, the student will:
Participate in literature review
Assist with survey data analysis
Contribute to manuscripts and conference poster presentations
Work collaboratively with faculty and interdisciplinary research teams
Participate in research meetings and scholarly activities
Skills the student will develop:
Evidence synthesis and critical appraisal of scientific literature
Quantitative research skills, including survey data analysis
Understanding of cancer prevention, survivorship research
Tools and technologies may include:
Citation management software/database (e.g., EndNote, Zotero, Scite)
Statistical and data analysis tools (e.g., Excel, SPSS, R)
Survey platforms (e.g., REDCap, Qualtrics)
Data visualization tools (e.g., Excel, R)
Under faculty mentorship, the student will:
Participate in literature review
Assist with survey data analysis
Contribute to manuscripts and conference poster presentations
Work collaboratively with faculty and interdisciplinary research teams
Participate in research meetings and scholarly activities
Skills the student will develop:
Evidence synthesis and critical appraisal of scientific literature
Quantitative research skills, including survey data analysis
Understanding of cancer prevention, survivorship research
Tools and technologies may include:
Citation management software/database (e.g., EndNote, Zotero, Scite)
Statistical and data analysis tools (e.g., Excel, SPSS, R)
Survey platforms (e.g., REDCap, Qualtrics)
Data visualization tools (e.g., Excel, R)
Campus:
West Lafayette
Research categories:
Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Nursing
- Public Health
Desired experience:
Enrollment in Nursing, Public Health, Health Informatics, Epidemiology, or a related discipline
Experience with survey data analysis and statistical software is preferred
Interest in cancer prevention, survivorship, or health informatics
School/Dept.:
Health Sciences
Professor:
Fabiana Cristina
Dos Santos
Optimizing Resource Allocation in Time-Sensitive E-Commerce Logistics
Description:
E-commerce and logistics providers now routinely offer same-day and next-day delivery, creating intense pressure on how resources such as vehicles, drivers, warehouse capacity, and sorting staff are allocated. Inefficient decisions quickly lead to delays, congestion, and high operating costs. This project will design and analyze resource allocation strategies for such time-sensitive systems, focusing on how to meet strict delivery deadlines while controlling cost and maintaining reliable service quality.
Using examples from e-commerce fulfillment and last-mile delivery operations, the project will develop mathematical models (e.g., for order processing, dispatching, and routing) that explicitly incorporate tight time constraints and uncertainty in demand. The student is expected to use mathematical programming to study system performance under different policies, then implement and test these strategies computationally to quantify trade-offs between speed, cost, and resource utilization.
They will develop skills in: (1) Mathematical programming (e.g., linear/integer optimization), (2) Queuing theory applied to real logistics systems, (3) Programming and data analysis, using tools such as Python, optimization libraries, and optimization solver (e.g., Gurobi/CPLEX).
Using examples from e-commerce fulfillment and last-mile delivery operations, the project will develop mathematical models (e.g., for order processing, dispatching, and routing) that explicitly incorporate tight time constraints and uncertainty in demand. The student is expected to use mathematical programming to study system performance under different policies, then implement and test these strategies computationally to quantify trade-offs between speed, cost, and resource utilization.
They will develop skills in: (1) Mathematical programming (e.g., linear/integer optimization), (2) Queuing theory applied to real logistics systems, (3) Programming and data analysis, using tools such as Python, optimization libraries, and optimization solver (e.g., Gurobi/CPLEX).
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Prior coursework in operations research and optimization, as well as strong programming skills (e.g., Python), is strongly preferred.
School/Dept.:
Edwardson School of Industrial Engineering
Professor:
Reem
Khir
Optogenetic Control of BMP Signaling and pSmad Imaging in Zebrafish Embryos
Description:
This project investigates how BMP–Smad signaling dynamics are regulated in developing zebrafish embryos using optogenetic control and advanced imaging. Undergraduate researchers will perform hands-on experimental work including microinjection of zebrafish embryos with optogenetic constructs, light-controlled activation of signaling pathways, and immunostaining of pSmad to quantify pathway activity. Imaging will be carried out using light-sheet microscopy to capture high-resolution, whole-embryo datasets.
Students will gain experience in developmental biology techniques, including embryo handling, microinjection, optogenetic stimulation, and fluorescence imaging. The project also includes basic data processing and analysis of spatial signaling patterns. This work will contribute to understanding how signaling dynamics and noise influence tissue patterning during development.
Students will gain experience in developmental biology techniques, including embryo handling, microinjection, optogenetic stimulation, and fluorescence imaging. The project also includes basic data processing and analysis of spatial signaling patterns. This work will contribute to understanding how signaling dynamics and noise influence tissue patterning during development.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
BME
Professor:
Linlin
Li
Optomechanics with Nanostructured Membranes, Super-Resolution Optical Imaging through Scatter, and Optical Imaging for Neuroscience
Description:
Three projects are available.
One project relates to optomechanics, here meaning the study of the mechanical properties of light. The group’s work includes theoretical and numerical modeling of light-matter interactions, as well as experimental work involving thin membranes. This line of investigation relates to potential applications in engineering, such as sensing and actuation, and has impacts in the fundamental sciences regarding the physics of optical forces on small length scales. The student will work on experimental aspects, including writing scripts for automating experiments and analyzing the data.
Another project involves the investigation of enhancing optical imaging and sensing capabilities. Conventional optical imaging has a theoretical limit on its spatial resolution of about one half of the wavelength, and many applications can benefit from higher resolution. This project involves the fabrication of subwavelength structures and experiments with laser illumination and relative motion between the laser the sample to access super resolution information and could involve simulation studies. Imaging through random scatter using laser light has many applications if adequate resolution can be achieved. We are currently investigating the use of pulsed laser light for imaging through scatter using speckle information, and initial results are promising.
The third project relates to the development of a fluorescence lifetime imaging framework that allows us to quantitatively track protein aggregation in microscopy studies and in vivo studies to better understand the cause of Parkinson’s disease. The primary effort relates to monitoring alpha-synuclein in live neurons and tissue phantom experiments. The work can encompass experiments using an ultrafast laser and gated camera, to capture fluorescence information, and related imaging processing studies.
One project relates to optomechanics, here meaning the study of the mechanical properties of light. The group’s work includes theoretical and numerical modeling of light-matter interactions, as well as experimental work involving thin membranes. This line of investigation relates to potential applications in engineering, such as sensing and actuation, and has impacts in the fundamental sciences regarding the physics of optical forces on small length scales. The student will work on experimental aspects, including writing scripts for automating experiments and analyzing the data.
Another project involves the investigation of enhancing optical imaging and sensing capabilities. Conventional optical imaging has a theoretical limit on its spatial resolution of about one half of the wavelength, and many applications can benefit from higher resolution. This project involves the fabrication of subwavelength structures and experiments with laser illumination and relative motion between the laser the sample to access super resolution information and could involve simulation studies. Imaging through random scatter using laser light has many applications if adequate resolution can be achieved. We are currently investigating the use of pulsed laser light for imaging through scatter using speckle information, and initial results are promising.
The third project relates to the development of a fluorescence lifetime imaging framework that allows us to quantitatively track protein aggregation in microscopy studies and in vivo studies to better understand the cause of Parkinson’s disease. The primary effort relates to monitoring alpha-synuclein in live neurons and tissue phantom experiments. The work can encompass experiments using an ultrafast laser and gated camera, to capture fluorescence information, and related imaging processing studies.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology, Composite Materials and Alloys, Deep Learning, Material Modeling and Simulation, Nanotechnology
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Electrical Engineering
- Physics
- Mechanical Engineering
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Kevin
Webb
Organelle Doppler Frequency for Tissue Dynamics Spectroscopy
Description:
This project could potentially discover the Doppler knee frequency for organelle trafficking in living cells. The project uses biodynamic imaging and tissue dynamics spectroscopy using low-coherence light for interferometric detection of Doppler frequency of light scattered from the intracellular constituents of living cells and tissues. The Doppler frequency for organelles is expected to exist theoretically, but has never been detected previously. Likelihood for success is high, based on the deep experience of the biodynamic imaging lab. This would be a significant advance in the study of intracellular dynamics.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Cellular Biology, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Facility with optical equipment and light sources and data analysis.
School/Dept.:
Physics and Astronomy
Professor:
David
Nolte
More information: https://www.physics.purdue.edu/nlo/
Origins of Life and Astrobiology Research Assistant
Description:
This is an open call for an undergraduate research assistant to join the Laboratory for Origins and Astrobiology Research (LOAR) at Purdue University. The specific project will be developed in collaboration with the selected student based on their interests and background. Our group investigates the chemical and environmental processes that may give rise to life on early Earth, Titan, and Mars. We also investigate the search for agnostic biosignatures that could be used detect life elsewhere in our Solar System. We combine experiments, analytical chemistry, computational modeling, and microbial biology to explore these questions.
Potential directions include:
• Developing new Liquid Chromatography/Orbitrap-Mass Spectrometry methods to detect prebiotic species of interest.
• Modeling the thermodynamics and kinetics of biomolecule formation under planetary conditions.
• Studying microbial motility and responses to environmental stress using microscopy.
• Assisting in laboratory simulations of atmospheric, aqueous, and/or freeze–thaw environments.
The ideal student is curious, self-motivated, and excited to contribute to cutting-edge astrobiology research. A passion for astrobiology and a willingness to learn are essential; prior research experience is helpful but not required.
Potential directions include:
• Developing new Liquid Chromatography/Orbitrap-Mass Spectrometry methods to detect prebiotic species of interest.
• Modeling the thermodynamics and kinetics of biomolecule formation under planetary conditions.
• Studying microbial motility and responses to environmental stress using microscopy.
• Assisting in laboratory simulations of atmospheric, aqueous, and/or freeze–thaw environments.
The ideal student is curious, self-motivated, and excited to contribute to cutting-edge astrobiology research. A passion for astrobiology and a willingness to learn are essential; prior research experience is helpful but not required.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Past research experience is welcomed.
School/Dept.:
Earth, Atmospheric and Planetary Sciences
Professor:
Ben
Pearce
More information: www.benkdpearce.com
Particle Combustion in Post-Detonation Flow
Description:
As part of a larger project simulating the combustion of magnesium and carbon particles in a post-detonation shockwave, we will be performing experiments involving the combustion of carbon rods in high-speed (high-subsonic to low-supersonic) air flows. Experimental results will be used to validate particle group-combustion modeling techniques being developed by a broader team of researchers across several institutions. Research tasks may include thermal/fluid modeling, programming (MATLAB/Python), component design (CAD), fluid system design and construction, data acquisition, and more.
Campus:
West Lafayette
Research categories:
Energy and Environment, Fluid Modelling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
- Chemical Engineering
Desired experience:
The ideal undergraduate researcher will:
• Be available to work in person over the summer.
• Be proficient with SolidWorks or similar CAD modeling and assembly creation software.
• Be experienced with MATLAB or Python.
• Have prior experience and/or strong interest, in the design and construction and operation of fluid or experimental systems.
• Be eligible to work with export controlled information
School/Dept.:
School of Mechanical Engineering
Professor:
Daniel
Guildenbecher
More information: https://engineering.purdue.edu/GuildenbecherLab
Pattern Analysis of Player Movement for Space Creation in Soccer
Description:
In this project, the student will analyze player movement patterns in soccer to understand how players create space, either for themselves or for their teammates. The goal is to identify recurring movement behaviors and use them to build a player taxonomy, grouping players into different categories based on how they move on the field. The work will involve processing and analyzing spatio-temporal tracking data using Python. The student is expected to have basic Python knowledge (such as variables, loops, functions, and simple data structures), but does not need to be an expert. Programming skills will naturally improve throughout the project.
The student should also have a basic understanding of soccer, including the rules of the game, player positions, and general tactical concepts such as possession, pressing, and spacing. This background is important for correctly interpreting movement patterns and placing the results in realistic game contexts.
The project will introduce the student to pattern recognition and machine learning techniques applied to sports data. These may include clustering methods (e.g., K-means, DBSCAN), dimensionality reduction techniques (e.g., PCA, UMAP), classification models, and time-series approaches such as Hidden Markov Models and recurrent neural networks. Not all techniques will necessarily be used; instead, different methods will be explored and evaluated to determine which best captures meaningful movement patterns. Through this work, the student will gain experience in data-driven sports analytics and spatio-temporal modeling.
The student should also have a basic understanding of soccer, including the rules of the game, player positions, and general tactical concepts such as possession, pressing, and spacing. This background is important for correctly interpreting movement patterns and placing the results in realistic game contexts.
The project will introduce the student to pattern recognition and machine learning techniques applied to sports data. These may include clustering methods (e.g., K-means, DBSCAN), dimensionality reduction techniques (e.g., PCA, UMAP), classification models, and time-series approaches such as Hidden Markov Models and recurrent neural networks. Not all techniques will necessarily be used; instead, different methods will be explored and evaluated to determine which best captures meaningful movement patterns. Through this work, the student will gain experience in data-driven sports analytics and spatio-temporal modeling.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Simulation and Technology, Deep Learning, Human Factors
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
- Python
School/Dept.:
School of Materials Engineering
Professor:
Jan-Anders
Mansson
Pattern Analysis of Player Movement for Space Creation in Soccer
Description:
In this project, the student will analyze player movement patterns in soccer to understand how players create space, either for themselves or for their teammates. The goal is to identify recurring movement behaviors and use them to build a player taxonomy, grouping players into different categories based on how they move on the field. The work will involve processing and analyzing spatio-temporal tracking data using Python. The student is expected to have basic Python knowledge (such as variables, loops, functions, and simple data structures), but does not need to be an expert. Programming skills will naturally improve throughout the project.
The student should also have a basic understanding of soccer, including the rules of the game, player positions, and general tactical concepts such as possession, pressing, and spacing. This background is important for correctly interpreting movement patterns and placing the results in realistic game contexts.
The project will introduce the student to pattern recognition and machine learning techniques applied to sports data. These may include clustering methods (e.g., K-means, DBSCAN), dimensionality reduction techniques (e.g., PCA, UMAP), classification models, and time-series approaches such as Hidden Markov Models and recurrent neural networks. Not all techniques will necessarily be used; instead, different methods will be explored and evaluated to determine which best captures meaningful movement patterns. Through this work, the student will gain experience in data-driven sports analytics and spatio-temporal modeling.
The student should also have a basic understanding of soccer, including the rules of the game, player positions, and general tactical concepts such as possession, pressing, and spacing. This background is important for correctly interpreting movement patterns and placing the results in realistic game contexts.
The project will introduce the student to pattern recognition and machine learning techniques applied to sports data. These may include clustering methods (e.g., K-means, DBSCAN), dimensionality reduction techniques (e.g., PCA, UMAP), classification models, and time-series approaches such as Hidden Markov Models and recurrent neural networks. Not all techniques will necessarily be used; instead, different methods will be explored and evaluated to determine which best captures meaningful movement patterns. Through this work, the student will gain experience in data-driven sports analytics and spatio-temporal modeling.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Simulation and Technology, Deep Learning, Human Factors
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
- Python
School/Dept.:
School of Materials Engineering
Professor:
Jan-Anders
Mansson
Pharmacodynamic modeling of tuberculosis
Description:
The student working on this project will implement, test and optimize ordinary differential equation models of tuberculosis infection. The student will couple these differential equations with existing pharmacokinetic models of commonly used antibiotics against tuberculosis.
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Elsje
Pienaar
Photosensor Calibration for Dark Matter Detection
Description:
The student will gain hands-on experience calibrating photosensors used in dark matter detectors, which are sensitive to single photons. They will design and build an electronic signal chain inside a dark box, acquire signals from the photosensors using a commercial digitizer, and analyze the data to measure sensor gain. Through this project, the student will develop practical skills in electronics, data acquisition, and quantitative analysis.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Physics and Astronomy
Professor:
Qing (Shilo)
Xia
Physical AI for manufacturing with digital twin and data analytics
Description:
This project is on digitalization and data analysis of Manufacturing and Materials Research Laboratories (MMRL) equipment and testbeds as well as working with local manufacturing companies for data analytics.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Fabrication and Robotics, Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Engineering
- Mechanical Engineering
- Electrical Engineering
School/Dept.:
School of Mechanical Engineering
Professor:
Martin Byung Guk
Jun
More information: https://purduelamm.github.io/home/
Physically intelligent underactuated Structures
Description:
Soft robots have gained interest due to their ability to interact with their environment, adapt to external stimuli, and protect against external disturbances[1], [2], [3]. The inherent safety provided by the characteristic low modulus of materials used in soft robots allows them to perform tasks that are nearly impossible for their rigid counterparts[4]. The interplay between soft mechanics and controls intrinsic to soft robotics gives rise to innovative solutions for tasks ranging from simple grasping to complex manipulation and locomotion [5], [6], [7]. However, this interplay also poses challenges, complicating their modeling and control. These challenges include their infinite dimensionality, material nonlinearity, and large deformations that most soft robots exhibit [4]. As a result, sensory systems[8] and complex models/algorithms[9], [10] are required to implement closed-loop control, leading to computationally demanding models to represent the robot's behavior.
Physical nonlinearities in soft robotic architectures have provided a solution to address existing limitations by utilizing the robots' structure for physical control [11]. Elements such as snap-through instabilities, rate dependencies, and responsiveness to external stimuli have been integrated into soft robotics architectures to improve performance, functionality, and embodied control. By leveraging the system's mechanical response, various outputs can be encoded into the system through logic operations [11], [12], [13], alternating cycles [14], object size classification [15], [16], and an overall enhancement in robot performance [16]. However, some of these robotic architectures cannot interact with the environment and cannot use environmental changes as control signals to evolve and adapt. This work aims to design structures that interact and change in response to different environments. Specifically, we focus on structures and systems that can mechanically sense contact and, when integrated with larger systems, enable complex behaviors.
References
[1] P. Polygerinos et al., “Soft Robotics: Review of Fluid-Driven Intrinsically Soft Devices; Manufacturing, Sensing, Control, and Applications in Human-Robot Interaction,” Adv. Eng. Mater., vol. 19, no. 12, p. 1700016, Dec. 2017, doi: 10.1002/ADEM.201700016.
[2] S. Kim, C. Laschi, and B. Trimmer, “Soft robotics: a bioinspired evolution in robotics,” Trends Biotechnol., vol. 31, no. 5, pp. 287–294, May 2013, doi: 10.1016/J.TIBTECH.2013.03.002.
[3] C. Majidi, “Soft-Matter Engineering for Soft Robotics,” Adv. Mater. Technol., vol. 4, no. 2, Feb. 2019, doi: 10.1002/ADMT.201800477.
[4] D. Rus and M. T. Tolley, “Design, fabrication and control of soft robots,” Nature, vol. 521, no. 7553, pp. 467–475, May 2015, doi: 10.1038/nature14543.
[5] C. Laschi, B. Mazzolai, and M. Cianchetti, “Soft robotics: Technologies and systems pushing the boundaries of robot abilities,” Sci. Robot., vol. 1, no. 1, p. eaah3690, Dec. 2016, doi: 10.1126/scirobotics.aah3690.
[6] R. Pfeifer, M. Lungarella, and F. Iida, “The challenges ahead for bio-inspired ‘soft’ robotics,” Commun. ACM, vol. 55, no. 11, pp. 76–87, Nov. 2012, doi: 10.1145/2366316.2366335.
[7] D. Trivedi, C. D. Rahn, W. M. Kier, and I. D. Walker, “Soft Robotics: Biological Inspiration, State of the Art, and Future Research,” Appl. Bionics Biomech., vol. 5, no. 3, pp. 99–117, 2008, doi: 10.1080/11762320802557865.
[8] R. L. Truby et al., “Soft Somatosensitive Actuators via Embedded 3D Printing,” Adv. Mater., vol. 30, no. 15, p. 1706383, Apr. 2018, doi: 10.1002/ADMA.201706383.
[9] K. Chin, T. Hellebrekers, and C. Majidi, “Machine Learning for Soft Robotic Sensing and Control,” Adv. Intell. Syst., vol. 2, no. 6, p. 1900171, June 2020, doi: 10.1002/AISY.201900171.
[10] T. G. Thuruthel, B. Shih, C. Laschi, and M. T. Tolley, “Soft robot perception using embedded soft sensors and recurrent neural networks,” Sci. Robot., vol. 4, no. 26, p. eaav1488, Jan. 2019, doi: 10.1126/scirobotics.aav1488.
[11] E. Milana, C. D. Santina, B. Gorissen, and P. Rothemund, “Physical control: A new avenue to achieve intelligence in soft robotics,” Sci. Robot., 2025.
[12] S. Conrad et al., “3D-printed digital pneumatic logic for the control of soft robotic actuators,” Sci. Robot., vol. 9, no. 86, p. eadh4060, Jan. 2024, doi: 10.1126/scirobotics.adh4060.
[13] Y. Zhai et al., “Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits,” Sci. Robot., vol. 8, no. 79, p. eadg3792, June 2023, doi: 10.1126/scirobotics.adg3792.
[14] D. Drotman, S. Jadhav, D. Sharp, C. Chan, and M. T. Tolley, “Electronics-free pneumatic circuits for controlling soft-legged robots,” Sci. Robot., vol. 6, no. 51, p. 2627, Feb. 2021, doi: 10.1126/SCIROBOTICS.AAY2627/SUPPL_FILE/AAY2627_SM.PDF.
[15] S. Zou, S. Picella, J. De Vries, V. G. Kortman, A. Sakes, and J. T. B. Overvelde, “A retrofit sensing strategy for soft fluidic robots,” Nat. Commun., vol. 15, no. 1, p. 539, Jan. 2024, doi: 10.1038/s41467-023-44517-z.
[16] J. C. Osorio, J. S. Rincon, H. Morgan, and A. F. Arrieta, “Embodying Control in Soft Multistable Robots from Morphofunctional Co‐design,” Adv. Sci., p. e03206, July 2025, doi: 10.1002/advs.202503206.
[17] L. C. Van Laake, J. De Vries, S. Malek Kani, and J. T. B. Overvelde, “A fluidic relaxation oscillator for reprogrammable sequential actuation in soft robots,” Matter, vol. 5, no. 9, pp. 2898–2917, Sept. 2022, doi: 10.1016/j.matt.2022.06.002.
[18] B. Van Raemdonck, E. Milana, M. De Volder, D. Reynaerts, and B. Gorissen, “Nonlinear Inflatable Actuators for Distributed Control in Soft Robots,” Adv. Mater., p. 2301487, July 2023, doi: 10.1002/adma.202301487.
[19] K. Boddapati, J. C. Osorio, and A. F. Arrieta, “On the Loss of Stability of Bistable Laminates due to Clamping”.
[20] P. Rothemund et al., “A soft, bistable valve for autonomous control of soft actuators,” Sci. Robot., vol. 3, no. 16, p. eaar7986, Mar. 2018, doi: 10.1126/scirobotics.aar7986.
Physical nonlinearities in soft robotic architectures have provided a solution to address existing limitations by utilizing the robots' structure for physical control [11]. Elements such as snap-through instabilities, rate dependencies, and responsiveness to external stimuli have been integrated into soft robotics architectures to improve performance, functionality, and embodied control. By leveraging the system's mechanical response, various outputs can be encoded into the system through logic operations [11], [12], [13], alternating cycles [14], object size classification [15], [16], and an overall enhancement in robot performance [16]. However, some of these robotic architectures cannot interact with the environment and cannot use environmental changes as control signals to evolve and adapt. This work aims to design structures that interact and change in response to different environments. Specifically, we focus on structures and systems that can mechanically sense contact and, when integrated with larger systems, enable complex behaviors.
References
[1] P. Polygerinos et al., “Soft Robotics: Review of Fluid-Driven Intrinsically Soft Devices; Manufacturing, Sensing, Control, and Applications in Human-Robot Interaction,” Adv. Eng. Mater., vol. 19, no. 12, p. 1700016, Dec. 2017, doi: 10.1002/ADEM.201700016.
[2] S. Kim, C. Laschi, and B. Trimmer, “Soft robotics: a bioinspired evolution in robotics,” Trends Biotechnol., vol. 31, no. 5, pp. 287–294, May 2013, doi: 10.1016/J.TIBTECH.2013.03.002.
[3] C. Majidi, “Soft-Matter Engineering for Soft Robotics,” Adv. Mater. Technol., vol. 4, no. 2, Feb. 2019, doi: 10.1002/ADMT.201800477.
[4] D. Rus and M. T. Tolley, “Design, fabrication and control of soft robots,” Nature, vol. 521, no. 7553, pp. 467–475, May 2015, doi: 10.1038/nature14543.
[5] C. Laschi, B. Mazzolai, and M. Cianchetti, “Soft robotics: Technologies and systems pushing the boundaries of robot abilities,” Sci. Robot., vol. 1, no. 1, p. eaah3690, Dec. 2016, doi: 10.1126/scirobotics.aah3690.
[6] R. Pfeifer, M. Lungarella, and F. Iida, “The challenges ahead for bio-inspired ‘soft’ robotics,” Commun. ACM, vol. 55, no. 11, pp. 76–87, Nov. 2012, doi: 10.1145/2366316.2366335.
[7] D. Trivedi, C. D. Rahn, W. M. Kier, and I. D. Walker, “Soft Robotics: Biological Inspiration, State of the Art, and Future Research,” Appl. Bionics Biomech., vol. 5, no. 3, pp. 99–117, 2008, doi: 10.1080/11762320802557865.
[8] R. L. Truby et al., “Soft Somatosensitive Actuators via Embedded 3D Printing,” Adv. Mater., vol. 30, no. 15, p. 1706383, Apr. 2018, doi: 10.1002/ADMA.201706383.
[9] K. Chin, T. Hellebrekers, and C. Majidi, “Machine Learning for Soft Robotic Sensing and Control,” Adv. Intell. Syst., vol. 2, no. 6, p. 1900171, June 2020, doi: 10.1002/AISY.201900171.
[10] T. G. Thuruthel, B. Shih, C. Laschi, and M. T. Tolley, “Soft robot perception using embedded soft sensors and recurrent neural networks,” Sci. Robot., vol. 4, no. 26, p. eaav1488, Jan. 2019, doi: 10.1126/scirobotics.aav1488.
[11] E. Milana, C. D. Santina, B. Gorissen, and P. Rothemund, “Physical control: A new avenue to achieve intelligence in soft robotics,” Sci. Robot., 2025.
[12] S. Conrad et al., “3D-printed digital pneumatic logic for the control of soft robotic actuators,” Sci. Robot., vol. 9, no. 86, p. eadh4060, Jan. 2024, doi: 10.1126/scirobotics.adh4060.
[13] Y. Zhai et al., “Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits,” Sci. Robot., vol. 8, no. 79, p. eadg3792, June 2023, doi: 10.1126/scirobotics.adg3792.
[14] D. Drotman, S. Jadhav, D. Sharp, C. Chan, and M. T. Tolley, “Electronics-free pneumatic circuits for controlling soft-legged robots,” Sci. Robot., vol. 6, no. 51, p. 2627, Feb. 2021, doi: 10.1126/SCIROBOTICS.AAY2627/SUPPL_FILE/AAY2627_SM.PDF.
[15] S. Zou, S. Picella, J. De Vries, V. G. Kortman, A. Sakes, and J. T. B. Overvelde, “A retrofit sensing strategy for soft fluidic robots,” Nat. Commun., vol. 15, no. 1, p. 539, Jan. 2024, doi: 10.1038/s41467-023-44517-z.
[16] J. C. Osorio, J. S. Rincon, H. Morgan, and A. F. Arrieta, “Embodying Control in Soft Multistable Robots from Morphofunctional Co‐design,” Adv. Sci., p. e03206, July 2025, doi: 10.1002/advs.202503206.
[17] L. C. Van Laake, J. De Vries, S. Malek Kani, and J. T. B. Overvelde, “A fluidic relaxation oscillator for reprogrammable sequential actuation in soft robots,” Matter, vol. 5, no. 9, pp. 2898–2917, Sept. 2022, doi: 10.1016/j.matt.2022.06.002.
[18] B. Van Raemdonck, E. Milana, M. De Volder, D. Reynaerts, and B. Gorissen, “Nonlinear Inflatable Actuators for Distributed Control in Soft Robots,” Adv. Mater., p. 2301487, July 2023, doi: 10.1002/adma.202301487.
[19] K. Boddapati, J. C. Osorio, and A. F. Arrieta, “On the Loss of Stability of Bistable Laminates due to Clamping”.
[20] P. Rothemund et al., “A soft, bistable valve for autonomous control of soft actuators,” Sci. Robot., vol. 3, no. 16, p. eaar7986, Mar. 2018, doi: 10.1126/scirobotics.aar7986.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
Desired experience:
Basic mechanics courses
School/Dept.:
School of Mechanical Engineering
Professor:
Andres
Arrieta
More information: https://engineering.purdue.edu/ProgrammableStructures/
Physics-Informed Machine Learning to Improve the Predictability of Extreme Weather Events
Description:
Atmospheric blocking events and 'Bomb Cyclones' are an important contributor to high impact extreme weather events. Both these weather extremes lead to heat waves, cold spells, droughts, and heavy precipitation episodes, which have dire consequences for the public health, economy, and ecosystem. For example, the blocking-induced heat waves of 2003 in Europe led to tens of thousands of human casualties and tens of billions of dollars of financial damage.
Traditionally, prediction of extreme weather events is based on direct numerical simulation of regional or global atmospheric models, which are expensive to conduct and involve a large number of tunable parameters. However, with the rapid rise of data science and machine learning in recent years, this proposed work will apply convolutional neural network to an idealized atmospheric model to conduct predictability analysis of extreme weather events within this model. With this proposed machine-learning algorithm, our project will provide a robust forecast of heat waves and atmospheric blocking with a lead-time of a few weeks. With more frequent record-breaking heat waves in the future, such a prediction will offer a crucial period of time (a few weeks) for our society to take proper preparedness steps to protect our vulnerable citizens.
This project is based on developing and verifying the machine learning algorithm for detecting extreme weather events in an idealized model. We will use Purdue’s supercomputer Bell to conduct the simulations. The undergraduate student will play an active and important role in running the idealized model, and participate in developing the algorithms. As an important component of climate preparedness, the proposed work aims to develop a physics-informed machine learning framework to improve predictability of extreme weather events.
Closely advised by Prof. Wang, the student will conduct numerical simulations of an idealized and very simple climate model, and use python-based machine learning tools to predict extreme weather events within the model. Prof. Wang will provide weekly tutorial sessions to teach key techniques along with interactive hands-on sessions. The students will get access to the big datasets on Purdue’s Data Depot, analyze and visualize data of an idealized atmospheric model. The student will use convolutional neural networks (CNNs) to train and assess a Machine-Learning model. The student will further use feature tracking algorithm to backward identify the physical structure in the atmosphere that is responsible for the onset of extreme weather events.
Traditionally, prediction of extreme weather events is based on direct numerical simulation of regional or global atmospheric models, which are expensive to conduct and involve a large number of tunable parameters. However, with the rapid rise of data science and machine learning in recent years, this proposed work will apply convolutional neural network to an idealized atmospheric model to conduct predictability analysis of extreme weather events within this model. With this proposed machine-learning algorithm, our project will provide a robust forecast of heat waves and atmospheric blocking with a lead-time of a few weeks. With more frequent record-breaking heat waves in the future, such a prediction will offer a crucial period of time (a few weeks) for our society to take proper preparedness steps to protect our vulnerable citizens.
This project is based on developing and verifying the machine learning algorithm for detecting extreme weather events in an idealized model. We will use Purdue’s supercomputer Bell to conduct the simulations. The undergraduate student will play an active and important role in running the idealized model, and participate in developing the algorithms. As an important component of climate preparedness, the proposed work aims to develop a physics-informed machine learning framework to improve predictability of extreme weather events.
Closely advised by Prof. Wang, the student will conduct numerical simulations of an idealized and very simple climate model, and use python-based machine learning tools to predict extreme weather events within the model. Prof. Wang will provide weekly tutorial sessions to teach key techniques along with interactive hands-on sessions. The students will get access to the big datasets on Purdue’s Data Depot, analyze and visualize data of an idealized atmospheric model. The student will use convolutional neural networks (CNNs) to train and assess a Machine-Learning model. The student will further use feature tracking algorithm to backward identify the physical structure in the atmosphere that is responsible for the onset of extreme weather events.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Fluid Modelling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Computer Science
- Physics
- Atmospheric Science/Meteorology
Desired experience:
Familiar with Machine Learning or prior knowledge of convolutional neural networks (CNNs); Have basic level training on PHYS172 Modern Mechanics or PHYS 15200 Mechanics or equivalent courses from other institutions; Familiar with Python scripting and visualization.
School/Dept.:
Earth, Atmospheric and Planetary Sciences
Professor:
Lei
Wang
More information: https://www.leiw.org
Portable AI-Empowered Tactile Sensing for Real-Time Healthcare Palpation
Description:
This project aims to develop a low-cost, portable, AI-enhanced tactile sensing system for real-time detection of surface-level and subdermal abnormalities through manual palpation. Unlike conventional imaging tools such as CT or MRI, which are costly and inaccessible in many rural and underserved areas, the proposed device offers an affordable and scalable solution for point-of-care screening. The system integrates high-resolution vision-based tactile sensing with real-time machine learning algorithms to identify variations in tissue stiffness and compliance—key indicators of early-stage diseases such as tumors or fibrotic lesions. The research will proceed through three core tasks: (1) development of a handheld tactile device, (2) training AI models to interpret tactile data, and (3) testing the system using phantom tissues and simulated clinical environments. Students will be immersed in all phases of the project and gain hands-on experience in sensor design, AI modeling, biomedical validation, and research communication.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
sensor hardware design, signal process, machine learning algorithm development.
Students must be from John Martinson Honors College (JMHC) due to funding constraints.
School/Dept.:
Edwardson School of Industrial Engineering
Professor:
Yu
She
More information: https://www.purduemars.com/home
Predictive modeling of aortic root dilation progression in pediatric patients
Description:
Aortic root dilation is a life-threatening cardiovascular condition affecting children with connective tissue disorders such as Marfan syndrome. Early detection of abnormal dilation patterns can dramatically improve clinical decisions and long-term outcomes. Our lab develops advanced imaging and AI tools to better quantify and predict disease progression in these patients.
In this project, the student will contribute to building a predictive model of aortic root dilation using ultrasound imaging data and computational analysis. This is an excellent opportunity for students interested in clinically meaningful applications of machine learning in biomedical imaging.
The student will take on hands-on components of the research pipeline, such as:
Cleaning and organizing echocardiographic datasets (cine loops and diameter tracings)
Extracting quantitative features from images using existing Python tools
Training baseline predictive models (e.g., regression, random forests, or simple neural networks)
Evaluating model performance, visualizing time-series diameter curves and identifying which imaging features best capture disease progression
Contributing to the development of a preliminary manuscript or abstract for a future conference
Skills Student Will Develop:
Understanding of cardiovascular physiology and pediatric aortic disease
Practical experience working with biomedical imaging data
Using Python and associated libraries/packages
Experience with data preprocessing, feature engineering, and basic supervised learning
Scientific presentation and communication skills (student will present at the SURF symposium)
In this project, the student will contribute to building a predictive model of aortic root dilation using ultrasound imaging data and computational analysis. This is an excellent opportunity for students interested in clinically meaningful applications of machine learning in biomedical imaging.
The student will take on hands-on components of the research pipeline, such as:
Cleaning and organizing echocardiographic datasets (cine loops and diameter tracings)
Extracting quantitative features from images using existing Python tools
Training baseline predictive models (e.g., regression, random forests, or simple neural networks)
Evaluating model performance, visualizing time-series diameter curves and identifying which imaging features best capture disease progression
Contributing to the development of a preliminary manuscript or abstract for a future conference
Skills Student Will Develop:
Understanding of cardiovascular physiology and pediatric aortic disease
Practical experience working with biomedical imaging data
Using Python and associated libraries/packages
Experience with data preprocessing, feature engineering, and basic supervised learning
Scientific presentation and communication skills (student will present at the SURF symposium)
Campus:
West Lafayette
Research categories:
Cardiovascular Disease Research, Deep Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Biomedical Engineering
- Computer Science
- Computer Engineering
Desired experience:
Python, MATLAB (optional) programming and an interest in medical imaging.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Craig
Goergen
More information: https://www.youtube.com/watch?v=HkxSvFNhubg
Probing transitions in motor learning via physics-informed state-space models
Description:
Probing transitions in motor learning via physics-informed state-space models
Behavioral and cognitive functions emerge from the dynamic interactions of many neurons in the mammalian brain. These neural dynamics are intrinsically routed through evolutionarily preserved circuits, and understanding how circuit connectivity gives rise to neural dynamics and behavior is a central goal in systems neuroscience. For instance, humans can retain learned motor skills for many years, which requires preserved yet flexible neural codes that evolve daily as new skills are acquired. How the brain enables this flexible learning strategy under network constraints remains elusive, in part due to limitations of chronic recording technologies and scalable inference methods that can dissect how neural dynamics change as a function of underlying connectivity.
In the Nano Neurotechnology Lab, we combine innovative multimodal approaches to understand brain dynamics during skillful movement. In particular, we are interested in how emergent dynamics in the motor cortex evolve during specific transitions in motor skill acquisition and how these dynamics change over time. This project focuses on building a physics-informed inference pipeline that integrates multimodal datasets into interpretable state-space models, leveraging tools such as latent linear dynamical systems and recurrent neural network–based state-space models. The students' expected role will be to explore methods, including hidden Markov models, Bayesian state-space inference, Gaussian process priors over latent trajectories, and switching dynamical systems constrained by biophysical properties of cortical circuits. By integrating surface potentials organized as wave-like patterns with population-wide cellular recordings, these models will infer low-dimensional latent states, identify transition structure between skill phases, and link changes in connectivity to shifts in latent dynamics. The anticipated outcome of this project is a validated modeling framework and open-source toolkit that can predict learning-related transitions from ongoing neural activity and guide the design of adaptive, closed-loop BCI strategies for motor rehabilitation.
By the end of this project the student would have gained a deep knowledge on neural dynamics, latent variable analysis, and closed-loop control.
Behavioral and cognitive functions emerge from the dynamic interactions of many neurons in the mammalian brain. These neural dynamics are intrinsically routed through evolutionarily preserved circuits, and understanding how circuit connectivity gives rise to neural dynamics and behavior is a central goal in systems neuroscience. For instance, humans can retain learned motor skills for many years, which requires preserved yet flexible neural codes that evolve daily as new skills are acquired. How the brain enables this flexible learning strategy under network constraints remains elusive, in part due to limitations of chronic recording technologies and scalable inference methods that can dissect how neural dynamics change as a function of underlying connectivity.
In the Nano Neurotechnology Lab, we combine innovative multimodal approaches to understand brain dynamics during skillful movement. In particular, we are interested in how emergent dynamics in the motor cortex evolve during specific transitions in motor skill acquisition and how these dynamics change over time. This project focuses on building a physics-informed inference pipeline that integrates multimodal datasets into interpretable state-space models, leveraging tools such as latent linear dynamical systems and recurrent neural network–based state-space models. The students' expected role will be to explore methods, including hidden Markov models, Bayesian state-space inference, Gaussian process priors over latent trajectories, and switching dynamical systems constrained by biophysical properties of cortical circuits. By integrating surface potentials organized as wave-like patterns with population-wide cellular recordings, these models will infer low-dimensional latent states, identify transition structure between skill phases, and link changes in connectivity to shifts in latent dynamics. The anticipated outcome of this project is a validated modeling framework and open-source toolkit that can predict learning-related transitions from ongoing neural activity and guide the design of adaptive, closed-loop BCI strategies for motor rehabilitation.
By the end of this project the student would have gained a deep knowledge on neural dynamics, latent variable analysis, and closed-loop control.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Cellular Biology, Deep Learning, Fluid Modelling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Controls, signal processing, math,
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Krishna
Jayant
Project Polaris: ROS2-Based Autonomous Navigation for Off-Road Agricultural Vehicles
Description:
Modern agricultural environments pose significant challenges for autonomous ground vehicles, including deformable soil, uneven terrain, wheel slip, and dynamic load variations from towed implements. NSF ERC IoT4Ag’s customized Polaris Ranger (equipped with LiDAR, cameras, GPS, and onboard GPUs) provides a full-scale experimental platform for advancing field autonomy.
This project will develop and validate a robust ROS2-based navigation and control stack using a scaled mini-robot operating on deformable and uneven terrain. Emphasis will be placed on terrain-aware perception, state estimation under slip conditions, and modeling dynamic load effects. The resulting software framework will be modular, simulation-compatible, and designed for seamless transfer to the full-scale Polaris Ranger platform.
This project will develop and validate a robust ROS2-based navigation and control stack using a scaled mini-robot operating on deformable and uneven terrain. Emphasis will be placed on terrain-aware perception, state estimation under slip conditions, and modeling dynamic load effects. The resulting software framework will be modular, simulation-compatible, and designed for seamless transfer to the full-scale Polaris Ranger platform.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Environmental Characterization, Fabrication and Robotics, Internet of Things (IoT), IoT for Precision Agriculture
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Coursework or experience in robotics, control systems, ROS2, C++/Python programming, and state estimation or perception (e.g., LiDAR/GPS sensor fusion), along with familiarity with Linux and basic vehicle dynamics, is desired.
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Yaguang
Zhang
More information: https://oatscenter.org/
Project Polaris: ROS2-Based Autonomous Navigation for Off-Road Agricultural Vehicles - Telecommunication Track
Description:
Modern agricultural environments pose significant challenges for autonomous ground vehicles, including deformable soil, uneven terrain, wheel slip, and dynamic load variations from towed implements. NSF ERC IoT4Ag’s customized Polaris Ranger (equipped with LiDAR, cameras, GPS, and onboard GPUs) provides a full-scale experimental platform for advancing field autonomy.
This project will develop and validate a robust ROS2-based navigation and control stack using a scaled mini-robot operating on deformable and uneven terrain. Emphasis will be placed on terrain-aware perception, state estimation under slip conditions, and modeling dynamic load effects. The resulting software framework will be modular, simulation-compatible, and designed for seamless transfer to the full-scale Polaris Ranger platform.
This project will develop and validate a robust ROS2-based navigation and control stack using a scaled mini-robot operating on deformable and uneven terrain. Emphasis will be placed on terrain-aware perception, state estimation under slip conditions, and modeling dynamic load effects. The resulting software framework will be modular, simulation-compatible, and designed for seamless transfer to the full-scale Polaris Ranger platform.
Campus:
West Lafayette
Research categories:
Environmental Characterization, Fabrication and Robotics, Internet of Things (IoT), IoT for Precision Agriculture
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Coursework or experience in wireless telecommunication, robotics, control systems, ROS2, C++/Python programming, and state estimation or perception (e.g., LiDAR/GPS sensor fusion), along with familiarity with Linux and basic vehicle dynamics, is desired.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
James
Krogmeier
More information: https://oatscenter.org/
Quantum Materials for Neuromorphic Computing
Description:
Enabling the next technological leap in computing will require new paradigms in hardware that can support the complexity of tomorrow’s computational advances. Towards this, neuromorphic architectures hold promise for lower energy consumption processors, enhanced computation, fundamentally different computational modes, native learning, and enhanced pattern recognition. Vanadium oxides show tremendous promise for neuromorphic computing because they can be used to make both artificial neurons and synapses. Recently a new type of non-volatile memory, driven by repeated partial temperature cycling through the insulator-to-metal transition, was discovered in vanadium oxides. Spatially resolved optical contrast measurements show that, surprisingly, the repeated advance and retreat of metal and insulator domains causes memory to be accumulated throughout the entirety of the sample, rather than only at the boundaries of domains. The memory appears as shifts in the local temperature at which the material transitions from insulator to metal upon heating, or from metal to insulator upon cooling. The undergraduate project will be to perform detailed modeling of the macroscopic resistivity during this process, predicted from optically derived images of metal and insulator domains in the Mott metal insulator transition material VO2.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Physics
School/Dept.:
Physics and Astronomy
Professor:
Erica
Carlson
More information: https://www.physics.purdue.edu/~erica/index.html
Quantum Visual Computing
Description:
Combining quantum computing, computer vision, computer graphics, and machine learning to solve complex problems, including energy and the environment challenges.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Energy and Environment, Engineering the Built Environment
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
- Computer and Information Technology
Desired experience:
Knowledge of quantum computing; knowledge of visual computing is a plus.
School/Dept.:
Computer Science
Professor:
Daniel
Aliaga
Quartz particle shaping and impact experiments to inform ANSYS CFX simulations
Description:
Project aims to shape sub-millimeter quartz particles to canonical shapes (e.g., cylinders, spheres) for ballistic impact experiments. Results will be used to inform CFD simulations for erosion studies via ANSYS Fluent/CFX.
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Fluid Modelling and Simulation, Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Aeronautical and Astronautical Engineering
- Mechanical Engineering
- Materials Engineering
Desired experience:
Aerodynamics, ANSYS Fluent, ANSYS CFX
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Zherui
Guo
Radiation and Agglomeration in Metalized Solid Fuel Propulsion Environments
Description:
We are seeking a motivated undergraduate researcher to join an experimental combustion research project investigating radiative heat transfer and particle dynamics in metalized solid fuel combustion, with applications to advanced propulsion systems. This project is part of a research effort sponsored by the Office of Naval Research (ONR) and conducted at Purdue University.
Metalized solid fuels, commonly containing aluminum or other reactive metals, are widely used to enhance propulsion performance. During combustion, metal particles produce intense thermal radiation that can significantly influence fuel burn rates. Despite its importance, this radiative feedback mechanism remains poorly quantified. The goal of this project is to develop and apply advanced optical diagnostics to directly measure radiative heat flux and particle properties in these extreme environments, and to connect experimental results with physics-based models.
The undergraduate researcher will contribute to experimental and analytical tasks that may include:
• Design and assembly of experimental components for combustion diagnostics
• Optical measurements of particle-laden flames (imaging, extinction, holography)
• Data processing and analysis using Python or MATLAB
• Thermal modeling related to particle combustion and radiation
• Support of laboratory experiments involving solid fuel combustion
The exact scope will be tailored to the student’s background and interests.
Metalized solid fuels, commonly containing aluminum or other reactive metals, are widely used to enhance propulsion performance. During combustion, metal particles produce intense thermal radiation that can significantly influence fuel burn rates. Despite its importance, this radiative feedback mechanism remains poorly quantified. The goal of this project is to develop and apply advanced optical diagnostics to directly measure radiative heat flux and particle properties in these extreme environments, and to connect experimental results with physics-based models.
The undergraduate researcher will contribute to experimental and analytical tasks that may include:
• Design and assembly of experimental components for combustion diagnostics
• Optical measurements of particle-laden flames (imaging, extinction, holography)
• Data processing and analysis using Python or MATLAB
• Thermal modeling related to particle combustion and radiation
• Support of laboratory experiments involving solid fuel combustion
The exact scope will be tailored to the student’s background and interests.
Campus:
West Lafayette
Research categories:
Energy and Environment, Fluid Modelling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
- Chemical Engineering
Desired experience:
The ideal candidate:
• Is available to work in person for the duration of the summer
• Has coursework or strong interest in mechanical engineering, aerospace engineering, physics, or a related field
• Has experience with or interest in MATLAB or Python
• Has experience with or interest in experimental systems, instrumentation, or laboratory work
• Experience with CAD (e.g., SolidWorks) is a plus
• Must be eligible to work with export-controlled information
Prior research experience is helpful but not required.
School/Dept.:
School of Mechanical Engineering
Professor:
Daniel
Guildenbecher
More information: https://engineering.purdue.edu/GuildenbecherLab
Reaction Syntheses of Porous Surfaces on Dense Fe-based and Ni-based Materials
Description:
Porous metals and metallic alloys, or dense metals and alloys with porous surfaces, are utilized in a number of industrial catalytic, biomedical, and defense applications (e.g., Raney nickel catalysts for the syntheses of various organic molecules). However, current processes used to generate such porous metals/alloys (or porous metal/alloy surfaces) require the use of high temperatures (e.g., to melt a Ni-Al-based alloy), high-energy processes (grinding/milling), and/or caustic reagents (e.g., sodium hydroxide). For this proposed SURF project, which requires 2 students, an alternate series of low-to-moderate temperature reactions (without high-energy milling and without caustic reagents) will be evaluated for producing porous Fe-based and Ni-based metal powders and foils.
One student will focus on Fe-based materials and the other student will focus on Ni-based materials. Each student will conduct a series of reactions with powders and foils. After each reaction step, each student will evaluate changes in the morphology, phase content, and compositions of the powders and foils using optical microscopy, electron microscopy (SEM), X-ray diffraction, and (for powders) nitrogen adsorption (BET/BJH) analyses. The students will develop skills in the characterization and reaction processing of metal powders and foils.
One student will focus on Fe-based materials and the other student will focus on Ni-based materials. Each student will conduct a series of reactions with powders and foils. After each reaction step, each student will evaluate changes in the morphology, phase content, and compositions of the powders and foils using optical microscopy, electron microscopy (SEM), X-ray diffraction, and (for powders) nitrogen adsorption (BET/BJH) analyses. The students will develop skills in the characterization and reaction processing of metal powders and foils.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis, Energy and Environment, Material Processing and Characterization, Nanotechnology, Thermal Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Materials Engineering
Desired experience:
MSE 230, MSE 260, MSE 270, MSE 330, MSE 340, and MSE 367.
School/Dept.:
School of Materials Engineering
Professor:
Kenneth
Sandhage
Real-Time FPGA Acceleration of Attention-Based Bidirectional LSTM Networks
Description:
In this project, the undergraduate researcher will work at the intersection of machine learning, signal processing, and hardware systems to develop and deploy attention-based bidirectional LSTM models for real-time analog signal prediction and control. The student will play an active role across the full workflow, from algorithm development and software simulation to hardware deployment and experimental validation.
The student will first develop and train machine-learning models using Python-based frameworks on time-series data representing analog signals. They will implement and compare baseline recurrent neural networks (LSTM) with attention-augmented bidirectional LSTM architectures, gaining experience in sequence modeling, hyperparameter optimization, and model evaluation. High-performance computing resources will be used to accelerate training and enable systematic benchmarking across model variants.
Following software validation, the student will translate the trained models to edge-computing platforms. They will deploy the models on an embedded GPU platform (e.g., NVIDIA Jetson Nano) to evaluate real-time inference performance, latency, and power consumption. In parallel, the student will assist in implementing quantized or fixed-point versions of the models on an FPGA-based platform, learning how algorithmic design choices translate into hardware constraints such as timing determinism, bandwidth, and resource utilization.
Throughout the project, the student will develop practical skills in machine learning, signal processing, embedded systems, and hardware–software co-design. Tools and technologies used in the project will include Python, PyTorch/TensorFlow, MATLAB (for signal analysis), FPGA development tools (e.g., Vivado), embedded GPU platforms, and basic instrumentation for signal generation and measurement. The project will provide hands-on experience with end-to-end system design, from data-driven modeling to real-time hardware implementation, preparing the student for future research or industry roles in AI-driven cyber-physical systems and edge computing.
The student will first develop and train machine-learning models using Python-based frameworks on time-series data representing analog signals. They will implement and compare baseline recurrent neural networks (LSTM) with attention-augmented bidirectional LSTM architectures, gaining experience in sequence modeling, hyperparameter optimization, and model evaluation. High-performance computing resources will be used to accelerate training and enable systematic benchmarking across model variants.
Following software validation, the student will translate the trained models to edge-computing platforms. They will deploy the models on an embedded GPU platform (e.g., NVIDIA Jetson Nano) to evaluate real-time inference performance, latency, and power consumption. In parallel, the student will assist in implementing quantized or fixed-point versions of the models on an FPGA-based platform, learning how algorithmic design choices translate into hardware constraints such as timing determinism, bandwidth, and resource utilization.
Throughout the project, the student will develop practical skills in machine learning, signal processing, embedded systems, and hardware–software co-design. Tools and technologies used in the project will include Python, PyTorch/TensorFlow, MATLAB (for signal analysis), FPGA development tools (e.g., Vivado), embedded GPU platforms, and basic instrumentation for signal generation and measurement. The project will provide hands-on experience with end-to-end system design, from data-driven modeling to real-time hardware implementation, preparing the student for future research or industry roles in AI-driven cyber-physical systems and edge computing.
Campus:
West Lafayette
Research categories:
Deep Learning, Fabrication and Robotics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
- Computer Engineering
Desired experience:
Students participating in this project are expected to have a strong interest in computational and hardware-oriented research. Prior coursework in linear algebra, probability and statistics, signals and systems, or introductory machine learning is desirable but not strictly required. Familiarity with Python programming is strongly preferred.
Helpful background includes experience with numerical computation, time-series analysis, or basic digital logic. Exposure to MATLAB, PyTorch or TensorFlow, or embedded systems (e.g., microcontrollers or single-board computers) will be advantageous but not mandatory. Students with coursework or project experience in digital signal processing, computer architecture, or VLSI/FPGA design will be well positioned to engage with the hardware components of the project.
No prior experience with advanced machine learning or FPGA development is assumed. Training will be provided in neural network modeling, attention-based sequence learning, model deployment on embedded GPU platforms, and introductory FPGA-based acceleration. The project is structured to support students from diverse academic backgrounds and to provide hands-on training in both software and hardware aspects of AI-enabled systems.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Alexander
Kildishev
Real-Time Transient Forecasting & Space Telescope Data Analysis
Description:
We are seeking a motivated student to participate in a cutting-edge summer research project focused on upgrading REFITT — the Recommender Engine For Intelligent Transient Tracking (refitt.org) — for compatibility with the forthcoming Vera C. Rubin Observatory (LSST) alert stream.
REFITT is an autonomous strategy-developer that uses signal prediction, probabilistic forecasting, utility estimation, and large-scale data analytics to guide global networks of telescopes toward the most scientifically valuable transient targets. It designs optimal observing strategies in real time, balancing scientific benefit, instrument capability, and observational cost to avoid duplicative efforts and maximize community scientific return.
This project will involve:
1. Integrating REFITT with LSST-scale, high-volume real-time alert streams
Developing methods for on-the-fly estimation of physical parameters from sparse, low-latency data
2. Enhancing REFITT’s forecasting, prioritization, and telescope-allocation algorithms
Building and testing simulation pipelines to evaluate and validate performance
3. Conducting scientific analysis of observations from the Hubble Space Telescope (HST) and James Webb Space Telescope (JWST)
This position offers rigorous training in modern time-domain astrophysics and also develops highly transferable computing and analytical skills sought after in industry roles involving data engineering, AI, real-time analytics, and autonomous decision-making systems.
REFITT is an autonomous strategy-developer that uses signal prediction, probabilistic forecasting, utility estimation, and large-scale data analytics to guide global networks of telescopes toward the most scientifically valuable transient targets. It designs optimal observing strategies in real time, balancing scientific benefit, instrument capability, and observational cost to avoid duplicative efforts and maximize community scientific return.
This project will involve:
1. Integrating REFITT with LSST-scale, high-volume real-time alert streams
Developing methods for on-the-fly estimation of physical parameters from sparse, low-latency data
2. Enhancing REFITT’s forecasting, prioritization, and telescope-allocation algorithms
Building and testing simulation pipelines to evaluate and validate performance
3. Conducting scientific analysis of observations from the Hubble Space Telescope (HST) and James Webb Space Telescope (JWST)
This position offers rigorous training in modern time-domain astrophysics and also develops highly transferable computing and analytical skills sought after in industry roles involving data engineering, AI, real-time analytics, and autonomous decision-making systems.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Physics
School/Dept.:
Physics and Astronomy
Professor:
Danny
Milisavljevic
Regenerative Pharmaceutical Production from Fungal Mycelium in Resource-Limited Environments
Description:
The necessity of vitamin supplementing and the continuous demand for pharmaceuical
producƟon in space and other environments with limited resources is a criƟcal challenge that
must be addressed. This project aims to improve the producƟon and hygienic collecƟon of
bioacƟve exudates from fungal mycelium as a potenƟal regeneraƟve plaƞorm for supplement
generaƟon.
The project will culƟvate mulƟple fungal strains, including variaƟons of Pleurotus, within
controlled mycoponic tube systems designed to support conƟnuous growth and secreƟon. The
first objecƟve will develop and evaluate sterile methods for collecƟng fungal exudates while
prevenƟng contaminaƟon. The collecƟon system will focus on extracƟng the exudates that
accumulate at the boƩom of the jar aŌer they have been secreted by the fungi. In order to
reduce the amount of liquid media that mixes with the exudates at the boƩom of the jar, an
automated pump system will be implemented for controlled media delivery, providing enough
nutrients for healthy mycelial growth while avoiding oversaturaƟon.
The second objecƟve will idenƟfy environmental and biochemical condiƟons that increase
exudate producƟon. The experiment will expose fungal cultures to controlled ultraviolet light
treatments using three wavelength ranges, UVA, UVB, and UVC. These treatments will test
whether photo stress sƟmulates metabolic secreƟon. Exudate volume, producƟon frequency,
exudate concentraƟon per mL, and mycelial growth will be recorded and compared across
treatments in order to determine which condiƟons promote the highest producƟon and the
most concentrated exudates. To further invesƟgate mechanisms of secreƟon control, the project
will integrate electrodes within the mycoponic tube system. The electrodes will monitor
environmental sƟmuli received by the fungi and may help detect differences in fungal responses
under varying light wavelengths.
Signals associated with stress responses are expected, along with measurable differences in
secreƟon behavior under different environmental condiƟons. The study will compare
producƟon across mulƟple fungal strains to determine whether different species produce
disƟnct exudates or exhibit different secreƟon rates under idenƟcal condiƟons
producƟon in space and other environments with limited resources is a criƟcal challenge that
must be addressed. This project aims to improve the producƟon and hygienic collecƟon of
bioacƟve exudates from fungal mycelium as a potenƟal regeneraƟve plaƞorm for supplement
generaƟon.
The project will culƟvate mulƟple fungal strains, including variaƟons of Pleurotus, within
controlled mycoponic tube systems designed to support conƟnuous growth and secreƟon. The
first objecƟve will develop and evaluate sterile methods for collecƟng fungal exudates while
prevenƟng contaminaƟon. The collecƟon system will focus on extracƟng the exudates that
accumulate at the boƩom of the jar aŌer they have been secreted by the fungi. In order to
reduce the amount of liquid media that mixes with the exudates at the boƩom of the jar, an
automated pump system will be implemented for controlled media delivery, providing enough
nutrients for healthy mycelial growth while avoiding oversaturaƟon.
The second objecƟve will idenƟfy environmental and biochemical condiƟons that increase
exudate producƟon. The experiment will expose fungal cultures to controlled ultraviolet light
treatments using three wavelength ranges, UVA, UVB, and UVC. These treatments will test
whether photo stress sƟmulates metabolic secreƟon. Exudate volume, producƟon frequency,
exudate concentraƟon per mL, and mycelial growth will be recorded and compared across
treatments in order to determine which condiƟons promote the highest producƟon and the
most concentrated exudates. To further invesƟgate mechanisms of secreƟon control, the project
will integrate electrodes within the mycoponic tube system. The electrodes will monitor
environmental sƟmuli received by the fungi and may help detect differences in fungal responses
under varying light wavelengths.
Signals associated with stress responses are expected, along with measurable differences in
secreƟon behavior under different environmental condiƟons. The study will compare
producƟon across mulƟple fungal strains to determine whether different species produce
disƟnct exudates or exhibit different secreƟon rates under idenƟcal condiƟons
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Biotechnology Data Insights, Cellular Biology, Composite Materials and Alloys, Ecology and Sustainability, Engineering the Built Environment, Environmental Characterization, Fluid Modelling and Simulation, Heterogeneous Integration, Learning and Evaluation, Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biological Engineering - multiple concentrations
- Horticulture (multiple concentrations)
Desired experience:
biochem, cell bio, basic chem
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
David Marshall
Porterfield
Research Dissemination & Digital Engagement With Focus on Wellbeing of Caregivers of Children With Rare Genetic Conditions
Description:
The student will support the dissemination of research findings of a clinical trial focused on wellbeing of caregivers of children with rare neurogenetic conditions. Responsibilities include cleaning and preparing datasets for reporting, organizing webinar presentation materials, and supporting ongoing writing projects.
The intern will also identify appropriate dissemination outlets and smaller grant opportunities to support future initiatives. In addition, the student will create visually engaging social media posts that translate research findings into accessible, shareable content.
The internship will help the student build competencies in the process of scientific writing, data management, research translation, presentation development, digital content creation, and strategic communication. The student will work with spreadsheet software, presentation tools, online funding databases, and graphic design platforms to produce professional dissemination materials.
The intern will also identify appropriate dissemination outlets and smaller grant opportunities to support future initiatives. In addition, the student will create visually engaging social media posts that translate research findings into accessible, shareable content.
The internship will help the student build competencies in the process of scientific writing, data management, research translation, presentation development, digital content creation, and strategic communication. The student will work with spreadsheet software, presentation tools, online funding databases, and graphic design platforms to produce professional dissemination materials.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Psychological Sciences
Professor:
Bridgette
Kelleher
More information: https://kelleherlab.weebly.com/
Resource allocation to mitochondria at slow growth rates
Description:
As cells grow faster, they need more ATP to meet their increasing biosynthetic demands. It is thus expected that faster-growing cells more heavily depend on mitochondria and invest more into their biosynthesis. However, we've observed that when growth rate is modulated by varying the quality of the carbon source (e.g., glucose, glycerol, acetate), mitochondria are larger (more resources are invested into mitochondria). To further test this seemingly counterintuitive observation, we will vary the growth rate by adjusting the dilution rate in a chemostat while keeping the carbon source unchanged. This approach will allow us to achieve very low growth rates. The project involves standardizing continuous culturing for two physiologically contrasting yeast species in a cost-efficient and small automated culturing system, the Pioreactor, as well as probing the physiology and proteome allocation to mitochondria at low growth rates.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Cellular Biology, Energy and Environment, Genetics, Microelectronics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Biological Sciences
Professor:
Sergio
Munoz-Gomez
More information: https://ecsolab.com/
Rheo-physical measurements of surfactant pastes
Description:
Student will set-up software and hardware that is used for rheo-physical measurements of surfactant pastes in partnership with The Procter and Gamble company.
Campus:
West Lafayette
Research categories:
Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Materials Engineering
Professor:
Kendra
Erk
Robust Machine Learning Research
Description:
David I. Inouye’s research vision is to develop trustworthy machine learning methods that are robust to imperfect distributional and computational assumptions.
Can causality help us understand and mitigate ML robustness issues?
What is the interplay between ML explanations and robustness?
How can we perform robust collaborative learning on a dynamic network of edge devices?
Can we find robust distribution matching methods to alleviate distribution shifts?
Specific project details will be decided closer to the start date, sometimes in collaboration with the student.
Can causality help us understand and mitigate ML robustness issues?
What is the interplay between ML explanations and robustness?
How can we perform robust collaborative learning on a dynamic network of edge devices?
Can we find robust distribution matching methods to alleviate distribution shifts?
Specific project details will be decided closer to the start date, sometimes in collaboration with the student.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Enjoys thinking deeply about problems and solutions.
Able to manage and overcome a large amount of uncertainty.
Able to learn new topics independently.
Teachable.
Excited about research.
Strong programming experience.
Strong math background.
Good written and oral communication skills.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
David
Inouye
More information: https://www.davidinouye.com/
Rydberg Photonics: Combining the Best of Both Worlds for the Next Quantum Revolution
Description:
We are an interdisciplinary team of physicists, electrical engineers, computer engineers, and computer scientists working at the forefront of atom-nanophotonics, a field that bridges atomic physics with quantum nanophotonics. Our overarching goal is to merge the strengths of these two domains to create scalable, integrable, and robust quantum technologies on a chip, in a manner analogous to today’s electronic microchips. In our research, we focus on Rydberg excitons in cuprous oxide (Cu₂O)—highly excited, atom-like quasiparticles in a solid-state environment. These excitons exhibit remarkably large electron–hole wavefunctions, extending up to micrometer scales, which makes them some of the most spatially extended quantum objects known in condensed matter. Their macroscopic size leads to strong mutual interactions mediated by long-range van der Waals forces, opening the door to engineered many-body quantum effects in a solid-state platform.
At the Quantum Nano-Photonics (QNP) Laboratory, we employ high-resolution laser spectroscopy at cryogenic temperatures, adapting techniques originally developed for atomic physics, to probe and quantify Rydberg exciton interactions in Cu₂O. Building on this foundation, we design and fabricate nanophotonic circuits—including waveguides, resonators, and beam splitters—to couple light to Rydberg excitons and exert precise control over their properties directly on a chip. In addition, we use ultrafast pulsed laser spectroscopy to excite Rydberg excitons and study their relaxation pathways and temporal dynamics. Through this unique integration of atomic-inspired spectroscopy with solid-state nanophotonics, our work establishes Cu₂O Rydberg excitons as a powerful platform for next-generation quantum photonic technologies.
At the Quantum Nano-Photonics (QNP) Laboratory, we employ high-resolution laser spectroscopy at cryogenic temperatures, adapting techniques originally developed for atomic physics, to probe and quantify Rydberg exciton interactions in Cu₂O. Building on this foundation, we design and fabricate nanophotonic circuits—including waveguides, resonators, and beam splitters—to couple light to Rydberg excitons and exert precise control over their properties directly on a chip. In addition, we use ultrafast pulsed laser spectroscopy to excite Rydberg excitons and study their relaxation pathways and temporal dynamics. Through this unique integration of atomic-inspired spectroscopy with solid-state nanophotonics, our work establishes Cu₂O Rydberg excitons as a powerful platform for next-generation quantum photonic technologies.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Physics
- Electrical Engineering
Desired experience:
Optics lab, Fourier Optics, Quantum Optics
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Hadiseh
Alaeian
More information: https://engineering.purdue.edu/qnp
SCALE HI-AP: Atomistic Modeling of MXenes for Electronic Applications
Description:
Description
MXenes are a new class of 2D metal carbides, as shown in Figure 1. Their range of chemistries and metallic conductivity makes them ideal candidates for a range of electronic applications.
Figure 1: MXene layered structures.
(Please see: https://nanohub.org/groups/scale/research/purdue/surf for the full SCALE project descriptions including images.
This project will use electronic structure calculations to characterize optical and electronic characteristics of MXenes.
Type of work
The student will play an active role in designing and running DFT simulations, defining simulation inputs, selecting materials systems, and analyzing structural, energetic and electronic properties. Through this work, the student will develop practical skills in materials modeling, scientific programming, and data analysis while gaining experience working with modern computational research tools.
By the end of the project, the student will have hands-on experience in computational materials research and machine learning, contributing to efforts to design materials and electronic components.
MXenes are a new class of 2D metal carbides, as shown in Figure 1. Their range of chemistries and metallic conductivity makes them ideal candidates for a range of electronic applications.
Figure 1: MXene layered structures.
(Please see: https://nanohub.org/groups/scale/research/purdue/surf for the full SCALE project descriptions including images.
This project will use electronic structure calculations to characterize optical and electronic characteristics of MXenes.
Type of work
The student will play an active role in designing and running DFT simulations, defining simulation inputs, selecting materials systems, and analyzing structural, energetic and electronic properties. Through this work, the student will develop practical skills in materials modeling, scientific programming, and data analysis while gaining experience working with modern computational research tools.
By the end of the project, the student will have hands-on experience in computational materials research and machine learning, contributing to efforts to design materials and electronic components.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Big Data/Machine Learning, Heterogeneous Integration, Microelectronics
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Mechanical Engineering
- Materials Engineering
- Aeronautical and Astronautical Engineering
- Physics
Desired experience:
Qualifications:
1) You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this SURF project. Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Led by Purdue University, funded by the Department of Defense, and managed by NSWC Crane, SCALE is the leading U.S. Program for semiconductor workforce development in the defense sector. SCALE is revolutionizing the training of highly-skilled U.S. microelectronics engineers, hardware designers, and manufacturing experts, ensuring U.S. leadership in this vital field. SCALE offers unique courses, mentoring, internships, targeted research opportunity matching, scholarships, and job placement assistance for graduate and undergraduate students.
2) Preferred Majors:
• Mechanical Engineering, Materials Science and Engineering, Aeronautical and Astronautical Engineering, Physics.
3) Required Experience and Skills:
We are looking for motivated and hard-working undergraduates who are available to work in person over the summer. All applicants should be capable of working independently while effectively communicating within a team setting.
4) Desired experience:
▪ Have experience with Python, or similar programming tools for data analysis or computational tasks.
▪ Be comfortable learning new software tools and working with structured input/output data.
▪ Have interest in workflow automation, scientific computing, or engineering simulations.
▪ Be able to document results clearly and communicate findings effectively.
School/Dept.:
School of Materials Engineering
Professor:
Alejandro
Strachan
More information: https://engineering.purdue.edu/MSE/people/ptProfile?id=33239
SCALE HI-AP: Engineering Materials for Thermal Transport for Semiconductor Packaging
Description:
Description
Does your phone or laptop ever get too hot to touch? Within electronic devices, heat generated by the components doing calculations must be dissipated to through the electronics package to the environment to prevent failure and to protect the users. This project focuses on engineering materials with either high thermal conductivity to effectively dissipate the heat or extremely low thermal conductivity to isolate and protect delicate components in the system (or combinations of material properties that enable routing of heat within the system). A combination of experimental property measurements, microstructural analysis, and performance tests will help identify routes to achieve better performance.
Type of work
Students in this project will fabricate new materials, measure their thermal properties, analyze their microstructures, integrate them into electronic packages, and/or test their thermal performance. Note that multiple students may contribute to the project in collaboration with graduate student mentoring.
Qualifications
You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this FTR project.
Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Students are not required to have prior heat transfer or materials experience to apply for and excel at this research project! It is beneficial, but not required, for students to have taken thermodynamics, fluid dynamics, and/or heat transfer courses. Programming and experimental skills are a plus.
Does your phone or laptop ever get too hot to touch? Within electronic devices, heat generated by the components doing calculations must be dissipated to through the electronics package to the environment to prevent failure and to protect the users. This project focuses on engineering materials with either high thermal conductivity to effectively dissipate the heat or extremely low thermal conductivity to isolate and protect delicate components in the system (or combinations of material properties that enable routing of heat within the system). A combination of experimental property measurements, microstructural analysis, and performance tests will help identify routes to achieve better performance.
Type of work
Students in this project will fabricate new materials, measure their thermal properties, analyze their microstructures, integrate them into electronic packages, and/or test their thermal performance. Note that multiple students may contribute to the project in collaboration with graduate student mentoring.
Qualifications
You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this FTR project.
Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Students are not required to have prior heat transfer or materials experience to apply for and excel at this research project! It is beneficial, but not required, for students to have taken thermodynamics, fluid dynamics, and/or heat transfer courses. Programming and experimental skills are a plus.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Energy and Environment, Heterogeneous Integration, Material Processing and Characterization, Microelectronics, Thermal Technology
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- All Engineering Majors
Desired experience:
Qualifications
1) You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this SURF project. Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Led by Purdue University, funded by the Department of Defense, and managed by NSWC Crane, SCALE is the leading U.S. Program for semiconductor workforce development in the defense sector. SCALE is revolutionizing the training of highly-skilled U.S. microelectronics engineers, hardware designers, and manufacturing experts, ensuring U.S. leadership in this vital field. SCALE offers unique courses, mentoring, internships, targeted research opportunity matching, scholarships, and job placement assistance for graduate and undergraduate students.
2) Preferred Majors:
• All engineering majors
3) Required Experience and Skills:
• All academic years are eligible.
• No specific skills or experience are required.
4) Desired experience:
• Students are not required to have prior heat transfer or materials experience to apply for and excel at this research project!
• It is beneficial, but not required, for students to have taken thermodynamics, fluid dynamics, and/or heat transfer courses.
• Programming and experimental skills are a plus.
To Apply:
In your SURF application, be sure to discuss this specific project and state how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in. You can view all SURF SCALE projects here: https://nanohub.org/groups/scale/research/purdue/surf.
School/Dept.:
School of Mechanical Engineering
Professor:
Amy
Marconnet
More information: https://engineering.purdue.edu/MTEC
SCALE HI-AP: Interconnect Schemes for 3D Heterogeneous Integration and Advanced Packaging
Description:
Description
As the demand for high performance computing increases, interconnect and die attach materials are needed to meet the requirements of 0.5 µm line width, sub-10 µm bump pitch, high I/O density, and power density for 1 nm Silicon Node and beyond (Figure 1). Cu-pillar or micro-bump technology with hybrid bonding has achieved sub-1 µm bump pitch posing several benefits such as increased I/O density, increased bandwidth, improved 3D stacking, enhanced power efficiency, and reduced parasitics and thermal resistance attributed to the absence of underfill. Although wafer-to-wafer (W2W) hybrid bonding can achieve 50 nm alignment accuracy; thermal budget, reliability, and chip-to-substrate hybrid bonding remain as drawbacks of this technology. In this study, novel Cu-pillar (micro-bump) bonding methods will be developed for chip-to-package interconnections at 10 µm bump pitch. Co-Packaged Optics (CPO) is an advanced heterogeneous integration of optics and silicon on a single packaged substrate aimed at addressing next generation bandwidth and power challenges. Here, bonding methodologies for CPO will be developed. Process recipes, test structures and reliability testing will be developed for fine-pitch Cu-microbumps, through Si and through glass vias and CPO for long-term reliability.
See the full project description at https://nanohub.org/groups/scale/research/purdue/surf for the image.
Type of work
Students in this project will develop new bonding methods for Cu-microbumps and evaluate reliability of test structures. Note that multiple students may contribute to the project in collaboration with graduate student mentoring.
As the demand for high performance computing increases, interconnect and die attach materials are needed to meet the requirements of 0.5 µm line width, sub-10 µm bump pitch, high I/O density, and power density for 1 nm Silicon Node and beyond (Figure 1). Cu-pillar or micro-bump technology with hybrid bonding has achieved sub-1 µm bump pitch posing several benefits such as increased I/O density, increased bandwidth, improved 3D stacking, enhanced power efficiency, and reduced parasitics and thermal resistance attributed to the absence of underfill. Although wafer-to-wafer (W2W) hybrid bonding can achieve 50 nm alignment accuracy; thermal budget, reliability, and chip-to-substrate hybrid bonding remain as drawbacks of this technology. In this study, novel Cu-pillar (micro-bump) bonding methods will be developed for chip-to-package interconnections at 10 µm bump pitch. Co-Packaged Optics (CPO) is an advanced heterogeneous integration of optics and silicon on a single packaged substrate aimed at addressing next generation bandwidth and power challenges. Here, bonding methodologies for CPO will be developed. Process recipes, test structures and reliability testing will be developed for fine-pitch Cu-microbumps, through Si and through glass vias and CPO for long-term reliability.
See the full project description at https://nanohub.org/groups/scale/research/purdue/surf for the image.
Type of work
Students in this project will develop new bonding methods for Cu-microbumps and evaluate reliability of test structures. Note that multiple students may contribute to the project in collaboration with graduate student mentoring.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Processing and Characterization, Microelectronics
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- All Engineering
Desired experience:
Qualifications:
1. You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this SURF project. Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Led by Purdue University, funded by the Department of Defense, and managed by NSWC Crane, SCALE is the leading U.S. Program for semiconductor workforce development in the defense sector. SCALE is revolutionizing the training of highly-skilled U.S. microelectronics engineers, hardware designers, and manufacturing experts, ensuring U.S. leadership in this vital field. SCALE offers unique courses, mentoring, internships, targeted research opportunity matching, scholarships, and job placement assistance for graduate and undergraduate students.
2. Preferred Majors:
• All Engineering Majors
3. Required Experience and Skills:
• None
4. Desired experience:
• Basic knowledge of materials science
• Labview/python programming
To Apply:
In your SURF application, be sure to discuss this specific project and state how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in. You can view all SURF SCALE projects here: https://nanohub.org/groups/scale/research/purdue/surf.
Websites
https://engineering.purdue.edu/EMRSL
https://nanohub.org/groups/scale/research/purdue/surf
School/Dept.:
School of Mechanical Engineering
Professor:
Shubhra
Bansal
More information: https://engineering.purdue.edu/EMRSL
SCALE HI-AP: Multijunction devices for electroluminescent on-chip cooling of 3D Stacked-Die Assembly
Description:
Description:
Rapid and continuing growth of compact 3D heterogeneously integrated (3D-HI) microsystems is limited by inadequate thermal management, which requires rejecting heat from semiconductor devices. 3D-HI microsystems employed in high-performance computing (HPC) typically consists of single layer of logic with stacked memory, but stacking of multiple-tiers of logic is limited due to lack of heat dissipation from hot-spots. State-of-the-art cooling technologies have a significant footprint that constrains the size, weight, and power (SWaP) of microsystems in high performance computing, including in artificial intelligence and machine learning applications. Electroluminescence, the underlying operating principle of light-emitting diodes (LEDs), is a phenomenon where the semiconductor emits light as a result of radiative recombination of injected charge carriers. Electroluminescence in LEDs can be a cooling process, as each electron-hole pair needs to absorb additional energy in the form of thermal lattice vibrations from semiconductor lattice to emit photons equivalent to the electronic bandgap energy of the semiconductor [1].
[1] Y. Park, S. Fui, “Multijunction Electroluminescent Cooling”, PRX Energy, 3, 033002 (2024).
Type of work
Students in this project will model a multi-junction halide perovskite LEDs as a proof-of-concept for electroluminescent cooling. Note that multiple students may contribute to the project in collaboration with graduate student mentoring.
Rapid and continuing growth of compact 3D heterogeneously integrated (3D-HI) microsystems is limited by inadequate thermal management, which requires rejecting heat from semiconductor devices. 3D-HI microsystems employed in high-performance computing (HPC) typically consists of single layer of logic with stacked memory, but stacking of multiple-tiers of logic is limited due to lack of heat dissipation from hot-spots. State-of-the-art cooling technologies have a significant footprint that constrains the size, weight, and power (SWaP) of microsystems in high performance computing, including in artificial intelligence and machine learning applications. Electroluminescence, the underlying operating principle of light-emitting diodes (LEDs), is a phenomenon where the semiconductor emits light as a result of radiative recombination of injected charge carriers. Electroluminescence in LEDs can be a cooling process, as each electron-hole pair needs to absorb additional energy in the form of thermal lattice vibrations from semiconductor lattice to emit photons equivalent to the electronic bandgap energy of the semiconductor [1].
[1] Y. Park, S. Fui, “Multijunction Electroluminescent Cooling”, PRX Energy, 3, 033002 (2024).
Type of work
Students in this project will model a multi-junction halide perovskite LEDs as a proof-of-concept for electroluminescent cooling. Note that multiple students may contribute to the project in collaboration with graduate student mentoring.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Modeling and Simulation, Microelectronics, Thermal Technology
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Any Engineering
Desired experience:
Qualifications:
1. You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this SURF project. Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Led by Purdue University, funded by the Department of Defense, and managed by NSWC Crane, SCALE is the leading U.S. Program for semiconductor workforce development in the defense sector. SCALE is revolutionizing the training of highly-skilled U.S. microelectronics engineers, hardware designers, and manufacturing experts, ensuring U.S. leadership in this vital field. SCALE offers unique courses, mentoring, internships, targeted research opportunity matching, scholarships, and job placement assistance for graduate and undergraduate students.
2. Preferred Majors:
• All Engineering Majors
3. Required Experience and Skills:
• None
4. Desired experience:
• Basic knowledge of materials science
• Labview/python programming
To Apply:
In your SURF application, be sure to discuss this specific project and state how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in. You can view all SURF SCALE projects here: https://nanohub.org/groups/scale/research/purdue/surf.
School/Dept.:
School of Mechanical Engineering
Professor:
Shubhra
Bansal
More information: https://engineering.purdue.edu/EMRSL
SCALE HI-AP: Multiphysics Simulation of Solder Separation During Surface Mount Reflow in BGA Packages
Description:
Description
Advanced semiconductor packaging requirements for increased functionality and faster performance continue to grow for consumer devices, including automotive, medical, and mobile applications. Exposure to harsh environments in these applications requires evaluating the robustness of semiconductor packages during and after assembly. Board-level reliability testing (BLRT) evaluates the reliability of a semiconductor package once the device is soldered to a printed circuit board (PCB). While BLRT has focused mostly on thermal fatigue, failure during assembly remains a less explored critical problem. A reliability issue observed during assembly is solder separation post surface mount onto a PCB, in which a crack is observed along the interface between the intermetallic compound (IMC) and the solder ball. During the post-surface mount reflow, the solder next to the ball grid array (BGA) substrate is still molten, while the solder next to the PCB is solidified due to higher thermal dissipation on that side. This thermal gradient creates an inhomogeneous distribution of thermal expansion that produces internal stresses in the solder during solidification. These stresses may cause separation between the solder and the pad. Furthermore, due to the difference in the coefficient of thermal expansion with the BGA pad, localized stresses develop close to the BGA substrate. This may result in fracture along the interface between the intermetallic compound layer and the Sn solder ball.
In this project, 3D finite-element simulations will be used to study the mechanisms that may cause solder separation of flip chip ball grid array (FCBGA) IC packages. The numerical model will include solder solidification, IMC growth, thermal transport to account for temperature gradients and cooling rate, and mechanical response for different geometries and solder materials.
Type of work
The undergraduate researcher will focus on identifying and compiling the necessary thermodynamic, kinetic, and interfacial parameters for different solder materials from the literature and available experimental datasets, implementing these parameters in the existing simulation framework and running controlled simulation studies that vary cooling rate, thermal gradient magnitude, pad/substrate material properties, different geometries to quantify stress evolution during solidification and identify conditions that promote IMC/solder interfacial separation.
Advanced semiconductor packaging requirements for increased functionality and faster performance continue to grow for consumer devices, including automotive, medical, and mobile applications. Exposure to harsh environments in these applications requires evaluating the robustness of semiconductor packages during and after assembly. Board-level reliability testing (BLRT) evaluates the reliability of a semiconductor package once the device is soldered to a printed circuit board (PCB). While BLRT has focused mostly on thermal fatigue, failure during assembly remains a less explored critical problem. A reliability issue observed during assembly is solder separation post surface mount onto a PCB, in which a crack is observed along the interface between the intermetallic compound (IMC) and the solder ball. During the post-surface mount reflow, the solder next to the ball grid array (BGA) substrate is still molten, while the solder next to the PCB is solidified due to higher thermal dissipation on that side. This thermal gradient creates an inhomogeneous distribution of thermal expansion that produces internal stresses in the solder during solidification. These stresses may cause separation between the solder and the pad. Furthermore, due to the difference in the coefficient of thermal expansion with the BGA pad, localized stresses develop close to the BGA substrate. This may result in fracture along the interface between the intermetallic compound layer and the Sn solder ball.
In this project, 3D finite-element simulations will be used to study the mechanisms that may cause solder separation of flip chip ball grid array (FCBGA) IC packages. The numerical model will include solder solidification, IMC growth, thermal transport to account for temperature gradients and cooling rate, and mechanical response for different geometries and solder materials.
Type of work
The undergraduate researcher will focus on identifying and compiling the necessary thermodynamic, kinetic, and interfacial parameters for different solder materials from the literature and available experimental datasets, implementing these parameters in the existing simulation framework and running controlled simulation studies that vary cooling rate, thermal gradient magnitude, pad/substrate material properties, different geometries to quantify stress evolution during solidification and identify conditions that promote IMC/solder interfacial separation.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Modeling and Simulation, Microelectronics
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Any Engineering
Desired experience:
Qualifications:
1) You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this SURF project. Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Led by Purdue University, funded by the Department of Defense, and managed by NSWC Crane, SCALE is the leading U.S. Program for semiconductor workforce development in the defense sector. SCALE is revolutionizing the training of highly-skilled U.S. microelectronics engineers, hardware designers, and manufacturing experts, ensuring U.S. leadership in this vital field. SCALE offers unique courses, mentoring, internships, targeted research opportunity matching, scholarships, and job placement assistance for graduate and undergraduate students.
2) Preferred Majors:
• Any engineering major
3) Required Experience and Skills:
Familiar with computer simulations
4) Desired experience:
(No additional desired experience)
To Apply:
In your SURF application, be sure to discuss this specific project and state how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in. You can view all SURF SCALE projects here: https://nanohub.org/groups/scale/research/purdue/surf.
School/Dept.:
School of Mechanical Engineering
Professor:
Marisol
Koslowski
More information: https://koslowskigroup.org
SCALE RH: Advancing X-ray Spectroscopy and Radiation Shielding Solutions
Description:
Dr. Aaron Specht's research focuses on developing innovative technologies for elemental measurements. Most work in his lab focuses on spectroscopy for environmental measurements -- such as through x-ray fluorescence. This cutting-edge technology is intended to improve the assessment of metal exposure and toxicity in both occupational and environmental health contexts with the ultimate goal to improve health through community or public health efforts.
Dr. Specht's interdisciplinary research spans exposure assessment, epidemiology, and physics. His projects focus on instrumentation development and optimization for application in broader health studies with a focus on the kinetics, storage, and transport of toxicants in the body for accurate implementation of novel instruments in health studies. He has projects spanning nuclear engineering, working with doctors in a clinic, or measuring live animals in the countryside. A aspect of radiation and electronics centers in his work identifying how electric fields interact with X-ray and gamma shielding design. Utilizing these fields for potential in radiation dose reduction or enhancement in the presence of varying electric fields, which has broad applications in radiation hardening of electronics.
Type of work
The student will aid in experiment design, data collection, and analysis including: gamma spectroscopy and fitting procedures; basic statistical testing; data cleaning and presentation; experiment design.
Dr. Specht's interdisciplinary research spans exposure assessment, epidemiology, and physics. His projects focus on instrumentation development and optimization for application in broader health studies with a focus on the kinetics, storage, and transport of toxicants in the body for accurate implementation of novel instruments in health studies. He has projects spanning nuclear engineering, working with doctors in a clinic, or measuring live animals in the countryside. A aspect of radiation and electronics centers in his work identifying how electric fields interact with X-ray and gamma shielding design. Utilizing these fields for potential in radiation dose reduction or enhancement in the presence of varying electric fields, which has broad applications in radiation hardening of electronics.
Type of work
The student will aid in experiment design, data collection, and analysis including: gamma spectroscopy and fitting procedures; basic statistical testing; data cleaning and presentation; experiment design.
Campus:
West Lafayette
Research categories:
Microelectronics, Radiation Hardening, Other
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Any Engineering
- Nuclear Engineering
Desired experience:
Qualifications:
1) You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this SURF project. Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Led by Purdue University, funded by the Department of Defense, and managed by NSWC Crane, SCALE is the leading U.S. Program for semiconductor workforce development in the defense sector. SCALE is revolutionizing the training of highly-skilled U.S. microelectronics engineers, hardware designers, and manufacturing experts, ensuring U.S. leadership in this vital field. SCALE offers unique courses, mentoring, internships, targeted research opportunity matching, scholarships, and job placement assistance for graduate and undergraduate students.
2) Preferred or required majors:
• All Engineering majors can apply
• NUCL preferred
3) Required Experience and Skills
• Interest in radiation physics or spectroscopy.
• Strong analytical and problem-solving skills.
• All Academic Years are Eligible
4) Desired experience and skills:
Would be helpful but not required:
• Experience with experimental instrumentation or data analysis.
• Familiarity with X-ray fluorescence or related spectroscopy techniques.
• Interest in public health applications.
To Apply:
In your SURF application, be sure to discuss this specific project and state how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in. You can view all SURF SCALE projects here: https://nanohub.org/groups/scale/research/purdue/surf.
The priority application deadline is January 15, 2025 to be considered for the Pathways program, possible housing assistance for Purdue students, and possible housing fee waivers for external students.
School/Dept.:
Health Sciences
Professor:
Aaron
Specht
More information: https://hhs.purdue.edu/directory/aaron-specht/
SCALE RH: Atomistic Modeling of Radiation Damage in Semiconductors
Description:
Description
Understanding how radiation interacts with materials is increasingly important for technologies operating in extreme environments such as space systems, nuclear energy, and radiation-hardened electronics. High-energy particles can create permanent atomic-scale damage that alters material performance in ways that are difficult to study experimentally. Molecular dynamics simulations provide a powerful approach for modeling these processes directly.
This project uses molecular dynamics simulations to study how high-energy nuclei interact with semiconductor and metallic materials, with a focus on the formation of permanent radiation damage. The work will examine how interfaces, defects, and other microstructural features influence damage creation and evolution, helping connect atomic-scale mechanisms to material behavior.
Type of work
The student will play an active role in designing and running radiation damage simulations, defining simulation inputs, selecting materials systems, and analyzing collision cascades and defect formation. Through this work, the student will develop practical skills in molecular dynamics, scientific programming, and data analysis while gaining experience working with modern computational research tools.
By the end of the project, the student will have hands-on experience in computational materials research and radiation damage modeling, contributing to efforts to design materials and electronic components that are more resilient to radiation.
Understanding how radiation interacts with materials is increasingly important for technologies operating in extreme environments such as space systems, nuclear energy, and radiation-hardened electronics. High-energy particles can create permanent atomic-scale damage that alters material performance in ways that are difficult to study experimentally. Molecular dynamics simulations provide a powerful approach for modeling these processes directly.
This project uses molecular dynamics simulations to study how high-energy nuclei interact with semiconductor and metallic materials, with a focus on the formation of permanent radiation damage. The work will examine how interfaces, defects, and other microstructural features influence damage creation and evolution, helping connect atomic-scale mechanisms to material behavior.
Type of work
The student will play an active role in designing and running radiation damage simulations, defining simulation inputs, selecting materials systems, and analyzing collision cascades and defect formation. Through this work, the student will develop practical skills in molecular dynamics, scientific programming, and data analysis while gaining experience working with modern computational research tools.
By the end of the project, the student will have hands-on experience in computational materials research and radiation damage modeling, contributing to efforts to design materials and electronic components that are more resilient to radiation.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation, Microelectronics, Radiation Hardening
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Mechanical Engineering
- Materials Engineering
- Aeronautical and Astronautical Engineering
- Physics
Desired experience:
Qualifications:
1) You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this SURF project. Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Led by Purdue University, funded by the Department of Defense, and managed by NSWC Crane, SCALE is the leading U.S. Program for semiconductor workforce development in the defense sector. SCALE is revolutionizing the training of highly-skilled U.S. microelectronics engineers, hardware designers, and manufacturing experts, ensuring U.S. leadership in this vital field. SCALE offers unique courses, mentoring, internships, targeted research opportunity matching, scholarships, and job placement assistance for graduate and undergraduate students.
2) Preferred Majors:
• Mechanical Engineering, Materials Science and Engineering, Aeronautical and Astronautical Engineering, Physics.
3) Required Experience and Skills:
We are looking for motivated and hard-working undergraduates who are able to work in person over the summer. All applicants should be capable of working independently while effectively communicating within a team setting.
4) Desired experience:
▪ Have experience with Python, or similar programming tools for data analysis or computational tasks.
▪ Be comfortable learning new software tools and working with structured input/output data.
▪ Have interest in workflow automation, scientific computing, or engineering simulations.
▪ Be able to document results clearly and communicate findings effectively.
▪ Experience with the LAMMPS software package is preferred.
To Apply:
In your SURF application, be sure to discuss this specific project and state how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in. You can view all SURF SCALE projects here: https://nanohub.org/groups/scale/research/purdue/surf.
School/Dept.:
School of Materials Engineering
Professor:
Alejandro
Strachan
More information: https://engineering.purdue.edu/MSE/people/ptProfile?id=33239
SCALE RH: Development and Application of Nuclear Techniques and Machine Learning to Study Metals and Human Health
Description:
Description
Metals play a pivotal role in human health. On one hand, humans rely on the intake of essential metals to survive; on the other hand, exposure to toxic or nonessential metals, as well as overexposure to essential metals, leads to a myriad of diseases and adverse health outcomes such as neurodegeneration, diabetes, and cancer.
(See figure 1 in the pdf description of this research in the SCALE group in nanoHUB - to be posted shortly - https://nanohub.org/groups/scale/research/purdue/surf)
Figure 1: Nuclear Techniques in Human Health: Synchrotron X-ray Fluorescence, High Yield Deuterium-Deuterium (DD) Neutron Generator, Monte Carlo Simulation, K-x-ray Fluorescence for Bone Metal Quantification, Advanced Photon Source (APS) at Argonne National Laboratory (ANL), In Vivo Neutron Activation Analysis (IVNAA)
Dr. Nie’s group focuses on developing radiation-based instruments and methodologies for applications in human health. Students in her lab work on projects that advance neutron and X-ray technologies for measuring metals and trace elements in human bones and tissues in vivo. They also participate in high-resolution mapping of elemental concentrations and speciation in human and animal brains using state-of-the-art synchrotron facilities. These innovative approaches are applied to study metal exposure and its impact on health, the relationship between nutrition and health, and the links between metal exposure and neurodegeneration. As part of their research, students engage in Monte Carlo simulations, laboratory experiments, and data analysis. A new research direction involves leveraging artificial intelligence, particularly machine learning, to detect metal deposition patterns in healthy and neurodegenerative brains and to explore the connections between metal exposure, brain metal accumulation, and neurodegeneration.
Type of work
Students can select to work on development of novel neutron activation analysis technologies, applying machine learning in elemental mapping, or data analysis. Specifically, students can choose to: make standards for elemental quantification; conduct Monte Carlo simulations on radiation transportation; radiation instrumentation design and development; perform experiments with the neutron generator, x-ray devices, and radiation detectors available in Dr. Nie’s lab; conduct experiments at the advanced photon source (APS) synchrotron facility at Argonne National Lab; x-ray and gamma ray spectroscopy, spectral fitting, statistical data analysis; and machine learning applications.
To Apply:
In your SURF application, be sure to discuss this specific project and state how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in. You can view all SURF SCALE projects here: https://nanohub.org/groups/scale/research/purdue/surf.
Metals play a pivotal role in human health. On one hand, humans rely on the intake of essential metals to survive; on the other hand, exposure to toxic or nonessential metals, as well as overexposure to essential metals, leads to a myriad of diseases and adverse health outcomes such as neurodegeneration, diabetes, and cancer.
(See figure 1 in the pdf description of this research in the SCALE group in nanoHUB - to be posted shortly - https://nanohub.org/groups/scale/research/purdue/surf)
Figure 1: Nuclear Techniques in Human Health: Synchrotron X-ray Fluorescence, High Yield Deuterium-Deuterium (DD) Neutron Generator, Monte Carlo Simulation, K-x-ray Fluorescence for Bone Metal Quantification, Advanced Photon Source (APS) at Argonne National Laboratory (ANL), In Vivo Neutron Activation Analysis (IVNAA)
Dr. Nie’s group focuses on developing radiation-based instruments and methodologies for applications in human health. Students in her lab work on projects that advance neutron and X-ray technologies for measuring metals and trace elements in human bones and tissues in vivo. They also participate in high-resolution mapping of elemental concentrations and speciation in human and animal brains using state-of-the-art synchrotron facilities. These innovative approaches are applied to study metal exposure and its impact on health, the relationship between nutrition and health, and the links between metal exposure and neurodegeneration. As part of their research, students engage in Monte Carlo simulations, laboratory experiments, and data analysis. A new research direction involves leveraging artificial intelligence, particularly machine learning, to detect metal deposition patterns in healthy and neurodegenerative brains and to explore the connections between metal exposure, brain metal accumulation, and neurodegeneration.
Type of work
Students can select to work on development of novel neutron activation analysis technologies, applying machine learning in elemental mapping, or data analysis. Specifically, students can choose to: make standards for elemental quantification; conduct Monte Carlo simulations on radiation transportation; radiation instrumentation design and development; perform experiments with the neutron generator, x-ray devices, and radiation detectors available in Dr. Nie’s lab; conduct experiments at the advanced photon source (APS) synchrotron facility at Argonne National Lab; x-ray and gamma ray spectroscopy, spectral fitting, statistical data analysis; and machine learning applications.
To Apply:
In your SURF application, be sure to discuss this specific project and state how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in. You can view all SURF SCALE projects here: https://nanohub.org/groups/scale/research/purdue/surf.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Medical Science and Technology, Radiation Hardening
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- applied nuclear physics
- radiation sciences
- biomedical engineering
- computer science
- Engineering Majors
Desired experience:
Qualifications:
1) You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this SURF project. Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Led by Purdue University, funded by the Department of Defense, and managed by NSWC Crane, SCALE is the leading U.S. Program for semiconductor workforce development in the defense sector. SCALE is revolutionizing the training of highly-skilled U.S. microelectronics engineers, hardware designers, and manufacturing experts, ensuring U.S. leadership in this vital field. SCALE offers unique courses, mentoring, internships, targeted research opportunity matching, scholarships, and job placement assistance for graduate and undergraduate students.
2) Preferred Majors:
• Applied nuclear physics, radiation sciences, biomedical engineering, computer science
3) Required Experience and Skills:
• Juniors and seniors
• STEM field
• Motivation to learn
4) Desired experience:
• Analytical skills
• Programming
• Basic knowledge on radiation sciences and radiation instrumentation
• Interest in human health
School/Dept.:
Health Sciences
Professor:
Linda
Nie
More information: https://hhs.purdue.edu/directory/linda-nie/
SCALE RH: Hybrid Radiation Shielding Design and Multi-objective Optimization
Description:
Research Areas:
Microelectronics, Radiation Hardening, radiation shielding, Monte Carlo simulations, Materials
Description
Since there are multiple types of radiation in space environments, it is important to shield against these different sources. However, different materials have different levels of shielding against different radiation sources. In this project, we will devise a hybrid shielding material to protect against multiple sources of radiation (e.g., neutrons and protons). Enabling simulation tools for this study will primarily include Stopping Range of Ions in Matter (SRIM) and Geant4.
Type of work
Students will primarily use established simulation tools (such as Geant4 or SRIM) or may perform experiments to assess shielding effectiveness.
Microelectronics, Radiation Hardening, radiation shielding, Monte Carlo simulations, Materials
Description
Since there are multiple types of radiation in space environments, it is important to shield against these different sources. However, different materials have different levels of shielding against different radiation sources. In this project, we will devise a hybrid shielding material to protect against multiple sources of radiation (e.g., neutrons and protons). Enabling simulation tools for this study will primarily include Stopping Range of Ions in Matter (SRIM) and Geant4.
Type of work
Students will primarily use established simulation tools (such as Geant4 or SRIM) or may perform experiments to assess shielding effectiveness.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation, Microelectronics, Radiation Hardening
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- All Engineering Majors
- Nuclear Engineering
- Electrical Engineering
- Mechanical Engineering
- Materials Engineering
Desired experience:
Qualifications
1) You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this SURF project. Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Led by Purdue University, funded by the Department of Defense, and managed by NSWC Crane, SCALE is the leading U.S. Program for semiconductor workforce development in the defense sector. SCALE is revolutionizing the training of highly-skilled U.S. microelectronics engineers, hardware designers, and manufacturing experts, ensuring U.S. leadership in this vital field. SCALE offers unique courses, mentoring, internships, targeted research opportunity matching, scholarships, and job placement assistance for graduate and undergraduate students.
2) Preferred Majors:
• All Engineering Majors can apply
• Nuclear Engineering
• Electrical Engineering
• Mechanical Engineering
• Materials Engineering
3) Required Experience and Skills:
• Enthusiasm for scientific programming
• Completion of introductory physics courses
• Juniors and seniors with the desired experience will be preferred, but all undergraduates are also eligible to apply.
4) Desired experience:
• Experience with programming in Python, C/C++, and/or MATLAB.
• Understanding of radiation transport and electromagnetism.
• Helpful to have previously taken (or at least signed up for) NUCL 200 or 205 as well as ECE 20001 and ECE 20007.
To Apply:
In your SURF application, be sure to discuss this specific project and state how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in. You can view all SURF SCALE projects here: https://nanohub.org/groups/scale/research/purdue/surf.
School/Dept.:
School of Nuclear Engineering
Professor:
Stylianos
Chatzidakis
SCALE RH: Modeling Radiation Effects on Semiconductor Diodes
Description:
Research Areas:
Microelectronics, Radiation Hardening, Semiconductor Modeling, Electron Emission, Space-charge Limited Current, Theory, Device Reliability
Description
One of the important limits for semiconductor device operation is the space-charge limit, which corresponds to the maximum allowed current before no more electrons can be emitted into a diode. This limit is given by the Mott-Gurney law in a trap-free solid or the Mark-Helfrich law for a solid with traps distributed exponentially in energy. Because ionizing radiation will create electrons and ions in a semiconductor device, this project will involve elucidating the effect of these charges on these limits. This may include using simulations to characterize behavior or adapting analytic theories to include ionizing radiation effects.
Figure 1: (go to the project description in nanoHUB to view the image) Fowler-Nordheim (FN) plot demonstrating breakdown of a nanoscale device in atmospheric pressure as indicated by the spike deviating from the FN equation for field emission (in red) [H. Wang, R. S. Brayfield II, A. M. Loveless, A. M. Darr, and A. L. Garner, “Experimental study of gas breakdown and electron emission in nanoscale gaps at atmospheric pressure,” Appl. Phys. Lett. 120, 124103 (2022)]
Type of work
Some combination of analytic theory (deriving equations and using Mathematica or MATLAB to analyze performance) and simulation using commercial software depending on students’ interest and skillsets.
Microelectronics, Radiation Hardening, Semiconductor Modeling, Electron Emission, Space-charge Limited Current, Theory, Device Reliability
Description
One of the important limits for semiconductor device operation is the space-charge limit, which corresponds to the maximum allowed current before no more electrons can be emitted into a diode. This limit is given by the Mott-Gurney law in a trap-free solid or the Mark-Helfrich law for a solid with traps distributed exponentially in energy. Because ionizing radiation will create electrons and ions in a semiconductor device, this project will involve elucidating the effect of these charges on these limits. This may include using simulations to characterize behavior or adapting analytic theories to include ionizing radiation effects.
Figure 1: (go to the project description in nanoHUB to view the image) Fowler-Nordheim (FN) plot demonstrating breakdown of a nanoscale device in atmospheric pressure as indicated by the spike deviating from the FN equation for field emission (in red) [H. Wang, R. S. Brayfield II, A. M. Loveless, A. M. Darr, and A. L. Garner, “Experimental study of gas breakdown and electron emission in nanoscale gaps at atmospheric pressure,” Appl. Phys. Lett. 120, 124103 (2022)]
Type of work
Some combination of analytic theory (deriving equations and using Mathematica or MATLAB to analyze performance) and simulation using commercial software depending on students’ interest and skillsets.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation, Material Processing and Characterization, Microelectronics, Radiation Hardening
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- All Engineering Majors
- Mathematics
- Physics
- Computer Science
Desired experience:
Qualifications
1) You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this SURF project. Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Led by Purdue University, funded by the Department of Defense, and managed by NSWC Crane, SCALE is the leading U.S. Program for semiconductor workforce development in the defense sector. SCALE is revolutionizing the training of highly-skilled U.S. microelectronics engineers, hardware designers, and manufacturing experts, ensuring U.S. leadership in this vital field. SCALE offers unique courses, mentoring, internships, targeted research opportunity matching, scholarships, and job placement assistance for graduate and undergraduate students.
2) Preferred Majors:
• All Engineering Majors can apply
• Mathematics
• Physics
• Computer Science
3) Required Experience and Skills:
• Strong understanding of fundamental calculus (derivatives, integrals, series expansions) and basic physics.
• Rising Juniors and seniors are preferred, but all undergraduate students are eligible to apply.
4) Desired experience:
• Experience with programming in Python, C/C++, and/or MATLAB.
• Knowledge of Mathematica is helpful, but not required.
• Enthusiasm for scientific programming.
• Understanding of radiation transport and electromagnetism.
• Helpful to have previously taken (or at least signed up for) NUCL 200 or 205 as well as ECE 20001 and ECE 20007.
To Apply:
In your SURF application, be sure to discuss this specific project and state how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in. You can view all SURF SCALE projects here: https://nanohub.org/groups/scale/research/purdue/surf.
School/Dept.:
School of Nuclear Engineering
Professor:
Allen
Garner
More information: https://sites.google.com/site/garnerresearchgroup/
SCALE RH: Monitoring of Ionizing Radiation Dose in Impacted Electronic Components
Description:
Description
Ionizing radiation is well-known to lead to atomic displacements/secondary radiation and malfunction of sensitive electronic components that use semiconductor chips, as well as possible degradation of structural (e.g., 3D printed) materials from lack of ductility, etc. One such area of relevance to the Department of War (DoW) would be space-based systems (manned and unmanned); these venues involve cosmic and galactic (neutron, gamma, alpha) radiation fields that can damage biological specimens and alter the performance. The dose levels may extend from the nGy (e.g., process upsets in chips due to alpha activity) and above into the kGy (0.1 MRad) and above range, from a collection of different types of high-energy (MeV+) radiation, such as gamma rays, neutrons, protons, alphas, and other charge particles. The ability to closely monitor the radiation dose comports with the mission requirements. Having an integrated sensor (either active or passive – i.e., one requiring electric power or not) alongside the system would prove valuable in this regard to help understand the life-span in real-world terrestrial and extraterrestrial locations.
To monitor for electronic component upset from alpha radiation in chips, the main emitters are isotopes of ppm quantities of U and Th in silicon chips. For such systems, students would deploy the Tensioned Metastable Fluid Detector (TMFD) technology in which a small (< 0.01g) quantity of chip is entered into the sensing fluid of the TMFD to count for alpha activity with ~100% intrinsic efficiency – and 100% blindness to beta-gamma radiation.
Figures 1 & 2 are illustrations relating to tensioned metastable fluid detector (TMFD) sensors. (Please go to the SCALE SURF research page to view the full project description, including figures).
Figure 1 depicts a Centrifugally Tensioned Metastable Fluid Detector (CTMFD) and an Acoustically Tensioned Metastable Fluid Detector (ATMFD). Figure 2 provides a comparison of the intrinsic neutron detection efficiency for TMFDs versus other state-of-the-art neutron detector types. Figure 3 presents evidence for 1 keV resolved alpha spectroscopy to enable discerning between Pu-239 and Pu-240 isotopes.
Figure 1: (A) Centrifugally Tensioned Metastable Fluid Detector (CTMFD), (B) Acoustically Tensioned Metastable Fluid Detector (ATMFD). Tensioned metastable fluid detector sensing technology for multifarious-multiscale applications in the nuclear fuel cycle. Journal of Radioanalytical and Nuclear Chemistry, 334(2), 1139–1156 (2025). https://doi.org/10.1007/s10967-024-09583-7
Figure 2. Intrinsic neutron detection efficiency comparisons between TMFD and state-of-the-art detectors. NE-213 represents a fast neutron detector; BF3 is a thermal neutron detector, and SDD is a superheated drop detector. JRANC (2025).
Figure 3: TMFD-based 1-keV resolved alpha spectroscopic identification of Pu-239 vs Pu-240. JRANC (2025).
The SCALE-SURF project will also involve adapting the novel, ultra-low cost ($0.1/unit), VOC-free, renewable polylactic acid biopolymer sensor technology referred to as PLAD to monitor for space-related higher radiation dose levels from the Gy to kGy range – involving massless photons (e.g., MeV gammas) and mass-bearing particles (neutrons and/or recoil protons) to be able to use simple laboratory techniques to adjudicate for the radiation dose. The ability to distinguish between gamma and neutron doses would be a major plus, extending that capability to also monitor the performance of future nuclear-powered propulsion and energy systems.
Figure 4: $0.1/PLAD neutron-gamma sensor beads (Left); GammaCellTM Irradiator (Right). Jiang, W., Miller, T., Barlow, T., Boyle, N., & Taleyarkhan, R. P. (2023). A Novel, Rapid Response Renewable Biopolymer Neutron and Gamma Radiation Solid-State Detector for Dosimetry and Nuclear Reactor Flux-Power Mapping. Instruments (Basel), 7(3), 26. https://doi.org/10.3390/instruments7030026.
Figure 4 shows the size and shape of PLA biopolymer resin beads (left), and Purdue University’s GammaCellTM irradiator used for gamma dosimetry. Figure 5 presents results of variation in the ratio of PLA bead mass dissolved vs. dose (neutron plus gamma radiation from Purdue University’s PUR-1 fission nuclear reactor, and gamma radiation alone. The cited reference in Instruments (2023) provides further details on how one can conduct neutron vs. gamma dosimetry.
Total Dose (kGy)
Figure 5: Illustrative results of GammaCellTM (gamma only) and PUR-1 (neutron & gamma) irradiation dose monitoring via mass dissolution physical metric – Instruments (2023).
Type of work
The selected student(s) would work in a team environment alongside experienced students and post-doctoral scholars to conduct irradiations in Purdue’s Co-60 (calibrated-certified) GammaCellTM irradiator over the Gy to kGy dose levels and then develop mathematical relations connecting physical changes in key properties of the PLAD detector (such as hardness, relative viscosity, color, hydrolysis, and swelling) with the radiation dose field using previously irradiated PLAD samples in PUR-1 (Purdue’s 10-kW research reactor) the technique(s) for near real-time readout. Separately, for ultra-low dose levels, the TMFD sensor technology will be utilized to develop a protocol for sensing the presence of alpha activity at 10-3-4 Bq type levels.
Ionizing radiation is well-known to lead to atomic displacements/secondary radiation and malfunction of sensitive electronic components that use semiconductor chips, as well as possible degradation of structural (e.g., 3D printed) materials from lack of ductility, etc. One such area of relevance to the Department of War (DoW) would be space-based systems (manned and unmanned); these venues involve cosmic and galactic (neutron, gamma, alpha) radiation fields that can damage biological specimens and alter the performance. The dose levels may extend from the nGy (e.g., process upsets in chips due to alpha activity) and above into the kGy (0.1 MRad) and above range, from a collection of different types of high-energy (MeV+) radiation, such as gamma rays, neutrons, protons, alphas, and other charge particles. The ability to closely monitor the radiation dose comports with the mission requirements. Having an integrated sensor (either active or passive – i.e., one requiring electric power or not) alongside the system would prove valuable in this regard to help understand the life-span in real-world terrestrial and extraterrestrial locations.
To monitor for electronic component upset from alpha radiation in chips, the main emitters are isotopes of ppm quantities of U and Th in silicon chips. For such systems, students would deploy the Tensioned Metastable Fluid Detector (TMFD) technology in which a small (< 0.01g) quantity of chip is entered into the sensing fluid of the TMFD to count for alpha activity with ~100% intrinsic efficiency – and 100% blindness to beta-gamma radiation.
Figures 1 & 2 are illustrations relating to tensioned metastable fluid detector (TMFD) sensors. (Please go to the SCALE SURF research page to view the full project description, including figures).
Figure 1 depicts a Centrifugally Tensioned Metastable Fluid Detector (CTMFD) and an Acoustically Tensioned Metastable Fluid Detector (ATMFD). Figure 2 provides a comparison of the intrinsic neutron detection efficiency for TMFDs versus other state-of-the-art neutron detector types. Figure 3 presents evidence for 1 keV resolved alpha spectroscopy to enable discerning between Pu-239 and Pu-240 isotopes.
Figure 1: (A) Centrifugally Tensioned Metastable Fluid Detector (CTMFD), (B) Acoustically Tensioned Metastable Fluid Detector (ATMFD). Tensioned metastable fluid detector sensing technology for multifarious-multiscale applications in the nuclear fuel cycle. Journal of Radioanalytical and Nuclear Chemistry, 334(2), 1139–1156 (2025). https://doi.org/10.1007/s10967-024-09583-7
Figure 2. Intrinsic neutron detection efficiency comparisons between TMFD and state-of-the-art detectors. NE-213 represents a fast neutron detector; BF3 is a thermal neutron detector, and SDD is a superheated drop detector. JRANC (2025).
Figure 3: TMFD-based 1-keV resolved alpha spectroscopic identification of Pu-239 vs Pu-240. JRANC (2025).
The SCALE-SURF project will also involve adapting the novel, ultra-low cost ($0.1/unit), VOC-free, renewable polylactic acid biopolymer sensor technology referred to as PLAD to monitor for space-related higher radiation dose levels from the Gy to kGy range – involving massless photons (e.g., MeV gammas) and mass-bearing particles (neutrons and/or recoil protons) to be able to use simple laboratory techniques to adjudicate for the radiation dose. The ability to distinguish between gamma and neutron doses would be a major plus, extending that capability to also monitor the performance of future nuclear-powered propulsion and energy systems.
Figure 4: $0.1/PLAD neutron-gamma sensor beads (Left); GammaCellTM Irradiator (Right). Jiang, W., Miller, T., Barlow, T., Boyle, N., & Taleyarkhan, R. P. (2023). A Novel, Rapid Response Renewable Biopolymer Neutron and Gamma Radiation Solid-State Detector for Dosimetry and Nuclear Reactor Flux-Power Mapping. Instruments (Basel), 7(3), 26. https://doi.org/10.3390/instruments7030026.
Figure 4 shows the size and shape of PLA biopolymer resin beads (left), and Purdue University’s GammaCellTM irradiator used for gamma dosimetry. Figure 5 presents results of variation in the ratio of PLA bead mass dissolved vs. dose (neutron plus gamma radiation from Purdue University’s PUR-1 fission nuclear reactor, and gamma radiation alone. The cited reference in Instruments (2023) provides further details on how one can conduct neutron vs. gamma dosimetry.
Total Dose (kGy)
Figure 5: Illustrative results of GammaCellTM (gamma only) and PUR-1 (neutron & gamma) irradiation dose monitoring via mass dissolution physical metric – Instruments (2023).
Type of work
The selected student(s) would work in a team environment alongside experienced students and post-doctoral scholars to conduct irradiations in Purdue’s Co-60 (calibrated-certified) GammaCellTM irradiator over the Gy to kGy dose levels and then develop mathematical relations connecting physical changes in key properties of the PLAD detector (such as hardness, relative viscosity, color, hydrolysis, and swelling) with the radiation dose field using previously irradiated PLAD samples in PUR-1 (Purdue’s 10-kW research reactor) the technique(s) for near real-time readout. Separately, for ultra-low dose levels, the TMFD sensor technology will be utilized to develop a protocol for sensing the presence of alpha activity at 10-3-4 Bq type levels.
Campus:
West Lafayette
Research categories:
Microelectronics, Radiation Hardening
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Qualifications:
1) You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this SURF project. Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Led by Purdue University, funded by the Department of Defense, and managed by NSWC Crane, SCALE is the leading U.S. Program for semiconductor workforce development in the defense sector. SCALE is revolutionizing the training of highly-skilled U.S. microelectronics engineers, hardware designers, and manufacturing experts, ensuring U.S. leadership in this vital field. SCALE offers unique courses, mentoring, internships, targeted research opportunity matching, scholarships, and job placement assistance for graduate and undergraduate students.
2) Preferred Majors:
• Nuclear Engineering
• Chemical Engineering
• Mechanical Engineering
3) Required Experience and Skills:
• All applicants should have taken NUCL200 (Intro. To Nuclear Engineering) or have equivalent background
• The ability to work independently and in a team setting.
4) Desired experience:
None.
To Apply:
In your SURF application, be sure to discuss this specific project and state how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in. You can view all SURF SCALE projects here: https://nanohub.org/groups/scale/research/purdue/surf.
School/Dept.:
School of Nuclear Engineering
Professor:
Rusi
Taleyarkhan
More information: https://engineering.purdue.edu/NE/people?group_id=2780&resource_id=3700
SCALE RH: Testing Radiation Effects on Microelectronics
Description:
Research Areas:
Microelectronics, Radiation Hardening, Failure Mechanisms, Device Reliability
Description
Commercial off-the-shelf electronics are appealing for satellite applications because of their high capabilities (e.g., processing speed or memory). While they are generally tested for reliability for terrestrial applications, most manufacturers don’t have time to test or qualify them for space applications. In this project, we’ll select a novel commercial device to test, and develop a test procedure for testing. Candidates include various types of read-only memory, microcontroller-based systems, and optical transceivers. While this work will not in itself provide spaceflight qualification, the insights provided will help inform such work at DoD, NASA, and other major entities launching space vehicles.
Type of work
We will utilize a Subcritical Assembly to expose the devices to a thermal neutron flux. The failure rate of the devices under test controlled by time and neutron flux will be studied. Using previously collected neutron flux information, several devices will be tested at once at varying neutron fluxes and durations. The devices will be initialized, and data will be collected after the irradiation to characterize the degradation. This experiment design allows for observation of varying degradation between different memory capacities, package types, neutron fluxes, and irradiation times. If time and personnel allow, we may also explore the potential effects and benefits of radiation shielding.
Microelectronics, Radiation Hardening, Failure Mechanisms, Device Reliability
Description
Commercial off-the-shelf electronics are appealing for satellite applications because of their high capabilities (e.g., processing speed or memory). While they are generally tested for reliability for terrestrial applications, most manufacturers don’t have time to test or qualify them for space applications. In this project, we’ll select a novel commercial device to test, and develop a test procedure for testing. Candidates include various types of read-only memory, microcontroller-based systems, and optical transceivers. While this work will not in itself provide spaceflight qualification, the insights provided will help inform such work at DoD, NASA, and other major entities launching space vehicles.
Type of work
We will utilize a Subcritical Assembly to expose the devices to a thermal neutron flux. The failure rate of the devices under test controlled by time and neutron flux will be studied. Using previously collected neutron flux information, several devices will be tested at once at varying neutron fluxes and durations. The devices will be initialized, and data will be collected after the irradiation to characterize the degradation. This experiment design allows for observation of varying degradation between different memory capacities, package types, neutron fluxes, and irradiation times. If time and personnel allow, we may also explore the potential effects and benefits of radiation shielding.
Campus:
West Lafayette
Research categories:
Microelectronics, Radiation Hardening
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Any Engineering Major
- Math
- Physics
- Computer Science
Desired experience:
Qualifications:
1. You must be a SCALE student to be considered for this project.
If you are not yet a SCALE student, you can concurrently apply to SCALE and to this SURF project. Read more about SCALE and access the SCALE application link here: https://www.scale4me.org/students.
Led by Purdue University, funded by the Department of Defense, and managed by NSWC Crane, SCALE is the leading U.S. Program for semiconductor workforce development in the defense sector. SCALE is revolutionizing the training of highly-skilled U.S. microelectronics engineers, hardware designers, and manufacturing experts, ensuring U.S. leadership in this vital field. SCALE offers unique courses, mentoring, internships, targeted research opportunity matching, scholarships, and job placement assistance for graduate and undergraduate students.
2. Preferred Majors:
• Any Engineering Major
• Math
• Physics
• CS
3. Required Experience and Skills:
• Strong understanding of fundamental calculus (derivatives, integrals, series expansions).
• Strong understanding of basic physics.
4. Desired experience:
• Rising Juniors and seniors are preferred, but all undergraduate students are eligible to apply.
• Experience with programming in Python, C/C++, and/or MATLAB.
• Knowledge of Mathematica is helpful, but not required.
• Enthusiasm for scientific programming.
• Understanding of radiation transport and electromagnetism.
• Helpful to have previously taken (or at least signed up for) NUCL 200 or 205 as well as ECE 20001 and ECE 20007.
To Apply:
In your SURF application, be sure to discuss this specific project and state how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in. You can view all SURF SCALE projects here: https://nanohub.org/groups/scale/research/purdue/surf.
Apply here: https://engineering.purdue.edu/Engr/Research/EURO/SURF.
Read more about Purdue SURF here: https://engineering.purdue.edu/Engr/Research/EURO/students/about-SURF.
Websites
https://www.scale4me.org/radiation-hardening
https://nanohub.org/groups/scale/research/purdue/surf
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Peter
Bermel
More information: https://www.scale4me.org/radiation-hardening
SCALE SoC: SoC design, verification, programming, and test
Description:
This project is restricted to SCALE Students. Please go to the SCALE SURF Page in the SCALE group on nanoHUB to read the full project description and requirements: https://nanohub.org/groups/scale/research/purdue/surf.
All questions about this project should go to the faculty and mentor listed on the project description.
System on Chip Extension Technologies (SoCET) is a long-running chip design team intended primarily for undergraduates to get experience in as many aspects of chip design, fabrication, and test as possible. Work on SoCET directly supports the SCALE program for which System on Chip design is one of the five specialty areas specifically targeted by SCALE.
The team is organized like a small company with sub-teams for logic design, verification, chip-layout, analog design, printed circuit board (PCB) design, test, software, and research collaborations. Research projects include AI hardware accelerators, GPU architecture, hardware security, and signal processing for space applications. Some project areas have very specific prerequisite requirements, so team leaders will work with you to evaluate your background and interests and assign you to an appropriate sub-team or special project. Almost any kind of background in circuit design, logic design, circuit simulation, computer architecture, and microcontroller programming will be useful in some but not all parts of the team. For more details on possible projects and sub-teams, see https://engineering.purdue.edu/SoC-Team.
As an example project, figure 1 illustrates how SoCET designs might use chiplets. Modern high performance SoCs are split across multiple pieces of Silicon referred to as chiplets.
Type of work:
The expected contributions will depend on the area of the SoCET to which you are assigned. Depending on your background, part of the work will involve learning skills necessary for the assignment project. Possible contributions include creation of subsystems to be used in a future chip design, creation of circuit layouts for an IC design, writing software to be used on an existing System on Chip design, FPGA prototyping, design of printed circuit boards, for IC testing, or participation in research collaborations with other faculty. See https://engineering.purdue.edu/SoC-Team for more examples.
All questions about this project should go to the faculty and mentor listed on the project description.
System on Chip Extension Technologies (SoCET) is a long-running chip design team intended primarily for undergraduates to get experience in as many aspects of chip design, fabrication, and test as possible. Work on SoCET directly supports the SCALE program for which System on Chip design is one of the five specialty areas specifically targeted by SCALE.
The team is organized like a small company with sub-teams for logic design, verification, chip-layout, analog design, printed circuit board (PCB) design, test, software, and research collaborations. Research projects include AI hardware accelerators, GPU architecture, hardware security, and signal processing for space applications. Some project areas have very specific prerequisite requirements, so team leaders will work with you to evaluate your background and interests and assign you to an appropriate sub-team or special project. Almost any kind of background in circuit design, logic design, circuit simulation, computer architecture, and microcontroller programming will be useful in some but not all parts of the team. For more details on possible projects and sub-teams, see https://engineering.purdue.edu/SoC-Team.
As an example project, figure 1 illustrates how SoCET designs might use chiplets. Modern high performance SoCs are split across multiple pieces of Silicon referred to as chiplets.
Type of work:
The expected contributions will depend on the area of the SoCET to which you are assigned. Depending on your background, part of the work will involve learning skills necessary for the assignment project. Possible contributions include creation of subsystems to be used in a future chip design, creation of circuit layouts for an IC design, writing software to be used on an existing System on Chip design, FPGA prototyping, design of printed circuit boards, for IC testing, or participation in research collaborations with other faculty. See https://engineering.purdue.edu/SoC-Team for more examples.
Campus:
West Lafayette
Research categories:
Microelectronics, System-on-a-Chip
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
- Electrical Engineering
- Computer Engineering
- Computer Science
Desired experience:
• Digital design and simulation using Verilog
• Analog circuit design
• Printed circuit board design
• Computer processor design
• IC testing
• Microcontroller programming.
To Apply:
In your SURF application, please discuss this specific project. State how you meet the project requirements and how this project relates to your academic and professional goals.
By applying to any SCALE project, you can be considered for multiple SCALE projects with one application. Please include a short paragraph for each SCALE project that you are interested in, ranked by interest, starting with the project you are most interested in.
You can view all SCALE SURF projects here: https://nanohub.org/groups/scale/research/purdue/surf.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Mark
Johnson
More information: https://engineering.purdue.edu/SoC-Team
Scanning Thermal Microscopy for Studying Defects in Solids
Description:
In this project, the student will use Scanning Thermal Microscopy to perform function study the defects. SThM provides sub 100-nm spatial resolution that is useful to study defects in different materials. Most importantly, it can be used to extract thermal resistance across grain boundaries of a polycrystalline film which is a useful metric for certain thermal interface materials used for heterogeneous integration in advanced packaging. The student will learn to operate the SThM tool and it is expected that she will do basic data analysis to extract useful information from the raw data.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Materials Engineering
School/Dept.:
School of Materials Engineering
Professor:
Raisul
Islam
More information: https://engineering.purdue.edu/RISE-Lab
Sensors, Computer Vision, and AI techniques for human factors engineering
Description:
We propose using real-time physical and cognitive load sensing to develop semi-autonomous systems. These user-aware, adaptive systems that can enhance human-human/ human-robot training and performance.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Human Factors, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Edwardson School of Industrial Engineering
Professor:
Denny
Yu
More information: https://engineering.purdue.edu/YuGroup
Sensors, Computer Vision, and AI techniques for human factors engineering
Description:
We propose using real-time physical and cognitive load sensing to develop semi-autonomous systems. These user-aware, adaptive systems that can enhance human-human/ human-robot training and performance.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Human Factors, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
School/Dept.:
Edwardson School of Industrial Engineering
Professor:
Denny
Yu
More information: https://engineering.purdue.edu/YuGroup
Sex-based differences in the gut-joint axis
Description:
This project is related to the gut-joint axis, or the study of the interactions among the gut microbiome, the synovial joint, and the systemic factors that mediate this relationship. The participant will learn about methods to manipulate and study the microbiome in murine model systems and to evaluate content and structural changes in the musculoskeletal system. The regions of the musculoskeletal system may include the forelimbs and hindlimbs, as well as the lumbar spine, and experience different mechanical loading throughout daily activities. The participant will evaluate the differences between male and female in both the gut microbiome response and the corresponding changes to the musculoskeletal system. The participant will learn computational tools for statistical comparisons between sexes, as well as microbiome and image analysis.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
- Biology
- Microbiology
- Biochemistry (Biology)
- Biological Engineering - multiple concentrations
Desired experience:
Biology, biochemistry, statistics
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Deva
Chan
More information: engineering.purdue.edu/ChanLab
Shock wave interaction with droplets and ice crystals
Description:
The student(s) will work with Prof. Jewell's graduate students and Research Scientist, Dr. Carson Lay, to operate and improve the Purdue University 3-inch Shock Tube, with applications in shock wave measurements, high-speed videography of particle (including ice particle) and droplet interactions, and high-speed pressure transducers. The project may also include the development and implementation of new optical measurement systems, including laser-based systems, as well as the development and improvement of fast-acting valves to introduce particles. High-Mach shock tunnel operations will be commissioned via the use of helium in the driver tube.
Campus:
West Lafayette
Research categories:
Fluid Modelling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Aeronautical and Astronautical Engineering
- Mechanical Engineering
Desired experience:
Good performance in AAE333 and AAE334 (or equivalent)--up through compressible fluids and shock waves. Experience with MATLAB. Experience with CAD software. Willingness to work with laser light sources. Ability to work towards goals independently.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Joseph
Jewell
More information: https://engineering.purdue.edu/AAE/people/ptProfile?resource_id=221718
Small Scale Investigations Into Modernizing Manufacturing of Critical Salts
Description:
This project involves investigating important salt syntheses through a small scale mechanistic lenses. Students will be responsible for investigate rate laws, and how to leverage in-situ analytics for real time concentration determination. Students will gain competency in autonomous flow chemistry, Raman spectroscopy, and HPLC. By the end of the project students will have generate a rate law, and validated said law with experimental studies
Campus:
West Lafayette
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Material Modeling and Simulation, Material Processing and Characterization
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Stephen
Beaudoin
Small molecule inducers of Streptomyces natural products
Description:
The student will study small molecules as chemical tools to induce production of silent biosynthetic gene clusters. The student will work directly with a graduate student with expertise in this area. They will develop skills in bacterial growth, metabolomics, qPCR, and natural product isolation.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Elizabeth
Parkinson
More information: https://www.parkinsonlaboratory.com/
Solutions towards returning chemically contaminated infrastructure to safe use
Description:
When man-made and natural disasters occur such as wildfires, floods, and chemical spills, chemicals can find there way into civil infrastructure systems such as water distribution networks and building plumbing. Little information is available to help public works and public health professionals make decisions about returning the contaminated infrastructure to safe use. It is essential that new knowledge be created to determine when infrastructure materials should be replaced or can be decontaminated in-situ.
This project will involve a student assisting a graduate student design and conduct experiments that explore how organic chemicals interact with piping materials and biofilms. This work is a results of 10+ years of experience by the research team assisting public works and public health professionals respond to and recover from disasters.
Analytical methods for the summer experiments have already been developed and optimized by the graduate student mentor. The SURF student would learn and apply these methods that include basic chemical laboratory practices and microbiological practices. Data interpretation and analysis would be carried-out based on these experiments.
This project will involve a student assisting a graduate student design and conduct experiments that explore how organic chemicals interact with piping materials and biofilms. This work is a results of 10+ years of experience by the research team assisting public works and public health professionals respond to and recover from disasters.
Analytical methods for the summer experiments have already been developed and optimized by the graduate student mentor. The SURF student would learn and apply these methods that include basic chemical laboratory practices and microbiological practices. Data interpretation and analysis would be carried-out based on these experiments.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Composite Materials and Alloys, Engineering the Built Environment, Environmental Characterization, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Above all, a strong commitment to learn and apply methods to generate knowledge is essential. Prior laboratory experience could be helpful, but is not necessary.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Andrew
Whelton
More information: https://engineering.purdue.edu/PlumbingSafety
Stem cell immunoengineering for targeted cancer therapy
Description:
Cancer is a major threat for humans worldwide, with over 18 million new cases and 9.6 million cancer-related deaths in 2019. Although most common cancer treatments include surgery, chemotherapy, and radiotherapy, unsatisfactory cure rates require new therapeutic approaches. Recently, adoptive cellular immunotherapies with chimeric antigen receptor (CAR) engineered T and natural killer (NK) cells have shown impressive clinical responses in patients with various blood and solid cancers. However, current clinical practices are limited by the need of large numbers of healthy immune cells, resistance to gene editing, lack of in vivo persistence, and a burdensome manufacturing strategy that requires donor cell extraction, modulation, expansion, and re-introduction per each patient. The ability to generate universally histocompatible and
genetically-enhanced immune cells from continuously renewable human pluripotent stem cell (hPSC) lines offers the potential to develop a true off-the-shelf cellular immunotherapy. While functional CAR-T and NK cells have been successfully derived from hPSCs, a significant gap remains in the scalability, time-consuming (5 or more weeks), purity and robustness of the differentiation methods due to the cumbersome use of serum, and/or feeder cells, which will incur potential risk for contamination and may cause batch-dependency in the treatment. This project thus aims to develop a novel, chemically-defined platform for robust production of CAR-T and CAR-NK cells from hPSCs. The students recruited will help to engineer stem cells with gene editing tools, differentiate stem cells into immune cells, and perform molecular and cellular assays to characterize the cells.
genetically-enhanced immune cells from continuously renewable human pluripotent stem cell (hPSC) lines offers the potential to develop a true off-the-shelf cellular immunotherapy. While functional CAR-T and NK cells have been successfully derived from hPSCs, a significant gap remains in the scalability, time-consuming (5 or more weeks), purity and robustness of the differentiation methods due to the cumbersome use of serum, and/or feeder cells, which will incur potential risk for contamination and may cause batch-dependency in the treatment. This project thus aims to develop a novel, chemically-defined platform for robust production of CAR-T and CAR-NK cells from hPSCs. The students recruited will help to engineer stem cells with gene editing tools, differentiate stem cells into immune cells, and perform molecular and cellular assays to characterize the cells.
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology, Cardiovascular Disease Research, Cellular Biology, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Previous experience with cell culture and molecular biology is a bonus, but NOT required.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Xiaoping
Bao
More information: https://sites.google.com/view/xiaoping-bao/home
Study of quantum properties of 2d materials
Description:
Student will work on fabrication and characterization of 2d materials.
Campus:
West Lafayette
Research categories:
Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Physics and Astronomy
Professor:
Yihang
Zeng
Studying the quantum properties of 2d materials
Description:
Student fabrication nanodevices based on two-dimensional materials and characterize their quantum properties at miliKelvin temperature.
Campus:
West Lafayette
Research categories:
Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Hands on experience with 2d materials is preferred.
School/Dept.:
Physics and Astronomy
Professor:
Yihang
Zeng
More information: https://sites.google.com/view/zenglab2024/home
Supply Chain Management doe Resiliency
Description:
Supply Chain Management for Resiliency
Campus:
West Lafayette
Research categories:
Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Edwardson School of Industrial Engineering
Professor:
Stephan
Biller
Supply Chain Management for Resiliency
Description:
Assist in Supply Chain Resiliency Research
Campus:
West Lafayette
Research categories:
Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Industrial Engineering
School/Dept.:
Edwardson School of Industrial Engineering
Professor:
Stephan
Biller
Sustainable Recovery of Lithium-Ion Battery Cathode Materials
Description:
This project aims to explore sustainable ways of separating cathode active materials from spent lithium-ion batteries or electrode scraps while preserving their original functional structures. The goal is to identify conditions that maximize material recovery and support more sustainable battery recycling. The student will play an integral role in the sustainable recovery of lithium-ion battery cathode materials, working at the interface of materials processing, electrochemistry, and data-driven analysis. Under close faculty supervision, the student will actively participate in experimental design, materials processing, characterization, and performance evaluation of recovered cathode materials. Specific responsibilities will include preparation and post-treatment of spent cathode feedstocks, execution of sustainable recovery processes, and systematic assessment of how processing conditions influence material structure and electrochemical performance.
Campus:
Indianapolis
Research categories:
Big Data/Machine Learning, Chemical Catalysis and Synthesis, Ecology and Sustainability, Energy and Environment, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Interest in sustainable energy, materials science, or chemical/mechanical engineering
Basic knowledge of chemistry or materials processing is helpful but not required.
Motivation to learn new techniques and work independently
School/Dept.:
School of Mechanical Engineering
Professor:
Hosop
Shin
More information: https://engineering.purdue.edu/GEMSlab
Swimming efficiency of chiral microswimmers in viscosity gradients
Description:
Natural waters (like the ocean and many lakes) aren’t mechanically uniform. Temperature and salinity variations create gradients in density and viscosity, and those inhomogeneities These inhomogeneities shape large-scale phenomena like ocean circulation and climate-driven transport, but they also strongly affect the motion of small, suspended particles—from microorganisms to synthetic particles. Recent studies show that viscosity gradients can actively steer swimmers, causing them to reorient and migrate (taxis). While we now have a growing understanding of how viscosity gradients alter the translational and rotational velocity of a microswimmer, a key question remains open: “Is swimming in a viscosity gradient actually more energy-efficient?” Some microswimmers can move faster in gradients, but faster motion may come at a higher energetic cost. To know whether a swimmer is truly “better off,” we must quantify the power dissipated in the surrounding fluid and the resulting swimming efficiency.
This project studies chiral microswimmers—swimmers whose geometry or motion has a handedness (like a helix). Chirality is common in nature: it lets swimmers convert rotation into forward propulsion, and it can dramatically change how swimmers respond to their environment. Experiments have explored chiral swimmers such as E. coli, yet much of the existing theory still concentrates on non-chiral models. Our goal is to develop theory to answer how viscosity gradients change the power dissipation and swimming efficiency of chiral microswimmers. Understanding this could help explain the swimming behaviours of microswimmers in real aquatic environments and guide the design of efficient microrobots for biomedical or environmental applications.
This project studies chiral microswimmers—swimmers whose geometry or motion has a handedness (like a helix). Chirality is common in nature: it lets swimmers convert rotation into forward propulsion, and it can dramatically change how swimmers respond to their environment. Experiments have explored chiral swimmers such as E. coli, yet much of the existing theory still concentrates on non-chiral models. Our goal is to develop theory to answer how viscosity gradients change the power dissipation and swimming efficiency of chiral microswimmers. Understanding this could help explain the swimming behaviours of microswimmers in real aquatic environments and guide the design of efficient microrobots for biomedical or environmental applications.
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology, Energy and Environment, Fluid Modelling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Chemical Engineering
- Biological Engineering - multiple concentrations
- Physics
- Chemistry
- Mathematics
Desired experience:
Basic understanding of MATLAB, differential equations
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Vivek
Narsimhan
More information: https://viveknarsimhan.wixsite.com/website
Synergistic effect of nanosilica and Type IL cement on concretes containing nontraditional and natural pozzolans
Description:
Concrete is the most widely used construction material in the world; however, cement production alone contributes approximately 8% of global CO₂ emissions. To reduce this environmental impact, the construction industry has widely adopted supplementary cementitious materials (SCMs), such as fly ash and slag, to partially replace cement. As the availability of these materials declines, nontraditional and natural pozzolans (NNPs), including calcined clays and volcanic ash, are emerging as promising alternatives. In parallel, innovations such as Type IL cement (containing 5–15% ground limestone) and nanosilica (nS) offer additional pathways to reduce cement consumption while maintaining or improving performance.
This project, conducted in collaboration with the Center of Durable and Resilient Transportation Infrastructure, investigates the combined effects of nanosilica and limestone content in Type IL cement on the performance of concrete incorporating NNPs. Previous mortar-scale studies have identified optimal material combinations; this phase focuses on evaluating concrete performance, including fresh properties, mechanical strength, and durability.
The undergraduate researcher will play an active role in laboratory experimentation. Responsibilities include preparing aggregates, mixing concrete, casting specimens, and conducting fresh property tests such as workability. The student will also assist in evaluating hardened properties (e.g., compressive strength) and durability performance, as well as preparing samples for microstructural analysis.
Through this experience, the student will develop hands-on skills in standardized concrete testing, experimental procedures, and laboratory safety. The project will also provide training in data analysis, visualization, and interpretation of engineering results. By the end of the program, the student will gain a strong foundation in sustainable construction materials and contribute to generating performance-based data that supports the implementation of low-carbon concrete in infrastructure applications.
This project, conducted in collaboration with the Center of Durable and Resilient Transportation Infrastructure, investigates the combined effects of nanosilica and limestone content in Type IL cement on the performance of concrete incorporating NNPs. Previous mortar-scale studies have identified optimal material combinations; this phase focuses on evaluating concrete performance, including fresh properties, mechanical strength, and durability.
The undergraduate researcher will play an active role in laboratory experimentation. Responsibilities include preparing aggregates, mixing concrete, casting specimens, and conducting fresh property tests such as workability. The student will also assist in evaluating hardened properties (e.g., compressive strength) and durability performance, as well as preparing samples for microstructural analysis.
Through this experience, the student will develop hands-on skills in standardized concrete testing, experimental procedures, and laboratory safety. The project will also provide training in data analysis, visualization, and interpretation of engineering results. By the end of the program, the student will gain a strong foundation in sustainable construction materials and contribute to generating performance-based data that supports the implementation of low-carbon concrete in infrastructure applications.
Campus:
West Lafayette
Research categories:
Engineering the Built Environment, Material Processing and Characterization, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Civil Engineering, materials engineering, environmenta
Desired experience:
previous laboratory experience will be helpful.
School/Dept.:
Civil and Construction Engineering
Professor:
Jan
Olek
Synthesis of novel 2D materials
Description:
Anasori group at Purdue focuses on the design and synthesis of a novel family of nanomaterials, known as MXenes. MXenes are two-dimensional (2D) transition metal carbides, nitrides, and carbonitrides that exhibit unique combinations of metallic conductivity, tunable surface chemistry, and layered structures. Because of these properties, MXenes are being explored for a broad range of applications, including energy storage and conversion, catalysis, electromagnetic interference shielding, sensing, biomedical uses, water purification, and high-temperature structural materials.
In this project, undergraduate researchers will work on synthesizing new families of MXenes, including double transition-metal and high-entropy MXenes, and optimizing their synthesis conditions. The resulting multi-layered and few-layered MXene flakes will be characterized to understand their structure, stability, and surface properties. Students will then explore how these properties relate to MXene applications across different fields, for example, electrocatalysis for hydrogen generation, energy storage in batteries and supercapacitors, shielding against electromagnetic waves, and potential use in extreme environments.
This research will give students hands-on experience in nanomaterials synthesis, advanced characterization techniques, and application-oriented testing, while contributing to the discovery and development of next-generation materials.
Type of student work:
Synthesis of novel two-dimensional MXenes
Optimizing synthesis conditions (e.g., reaction time, temperature)
In this project, undergraduate researchers will work on synthesizing new families of MXenes, including double transition-metal and high-entropy MXenes, and optimizing their synthesis conditions. The resulting multi-layered and few-layered MXene flakes will be characterized to understand their structure, stability, and surface properties. Students will then explore how these properties relate to MXene applications across different fields, for example, electrocatalysis for hydrogen generation, energy storage in batteries and supercapacitors, shielding against electromagnetic waves, and potential use in extreme environments.
This research will give students hands-on experience in nanomaterials synthesis, advanced characterization techniques, and application-oriented testing, while contributing to the discovery and development of next-generation materials.
Type of student work:
Synthesis of novel two-dimensional MXenes
Optimizing synthesis conditions (e.g., reaction time, temperature)
Campus:
West Lafayette
Research categories:
Material Processing and Characterization, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Materials Engineering
Professor:
Babak
Anasori
More information: www.babakanasori.com
Synthesis of zeolite catalysts with tailored diffusion and reaction properties
Description:
Olefin oligomerization is a key step in light hydrocarbon gas upgrading routes to heavier molecular weight products. Acidic zeolites are an important class of materials to catalyze oligomerization reactions, but reaction rates and selectivities are influenced by coupled reaction-transport phenomena, and by the distribution of active sites within different pores of the material. This project will focus on synthesizing zeolite crystallites with tailored diffusion properties (e.g., crystal size and morphology, acid site distributions) to influence the rates and selectivities of olefin oligomerization.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemical Engineering
- Chemistry
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Rajamani
Gounder
More information: https://sites.google.com/site/rgounder/
Tactile-based reactive control for robotic manipulation
Description:
The goal of this project is to develop perception, control, and planning algorithms for robotic manipulation for pick-and-place tasks. The robotic system includes a UR5, a robotiq gripper, a vision-based fingertip tactile sensor (i.e., GelSight sensor), as well as a depth camera (e.g., Kinect or RealSense). First, it is expected to develop computer vision algorithms to use the depth camera to identify interested objects in a given environment. Second, motion planning algorithms are expected to be developed for the UR5 to move the robotic arm from the home position to the location of the object that is estimated by the depth camera. Third, a gripping controller is expected to be developed for the Robotiq gripper, which will leverage the tactile feedback from the GelSight sensor for robust grasping of the deformable objects, and placing the objects in another location.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics
Desired experience:
one or more training/experience from the following areas: robotics, robotic manipulation, motion planning, computer vision, tactile sensing, control
School/Dept.:
Edwardson School of Industrial Engineering
Professor:
Yu
She
More information: www.purduemars.com
Teaching Robots to See, Understand, and Predict Human Motion in 3D
Description:
This project investigates next-generation vision-centric autonomy by developing a unified perception stack that leverages foundation vision models for dense 3D scene understanding, human behavior modeling, and interaction-aware prediction. Students will work on multi-view and monocular 3D reconstruction, neural scene representations (e.g., neural fields, Gaussian-based representations), human pose and shape estimation, multi-human tracking, and short- to mid-horizon behavior and intent forecasting. The system will integrate semantic, geometric, and motion cues into a unified representation that enables robots to reason about human motion, interaction zones, and future scene evolution. Emphasis will be placed on real-time inference using on-device accelerators, robustness to real-world noise, and generalization across environments. The project is explicitly designed to target publishable outcomes, with the goal of producing research suitable for submission to top-tier venues in computer vision and robotics (e.g., CVPR, ICCV, ECCV, ICRA, IROS).
Campus:
West Lafayette
Research categories:
Fabrication and Robotics, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Computer Science
- Electrical Engineering
- Computer Engineering
School/Dept.:
Computer Science
Professor:
Aniket
Bera
More information: https://ideas.cs.purdue.edu/
Technology and Society: impacts and interventions
Description:
Student will support resource collection and analysis, data collection and analysis, and design and implementation of interactive systems and experiences intended to impact human behavior. Example contexts include AI and robotics literacy, game-based socialization, and game-based learning.
Campus:
West Lafayette
Research categories:
Human Factors
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Computer Graphics Technology
Professor:
Rua
Williams
The Deliberate Innovation of Fusion Power: An Overview of Key Technologies and The Critical Role of Neutron Detectors
Description:
The nuclear fusion generation of electricity has the potential to power human energy needs at dramatically higher levels long into the future. Although stars continuously demonstrate the viability of fusion energy harnessing it for electricity generation on earth has proven to be among the most difficult problems humans have had to address. The student will survey the key technologies that enable fusion, especially the role of neutron detectors in understanding and controlling fusion phenomena. The student will undertake both experimental and computational activities - testing neutron detectors and learning how to simulate the underlying physics.
Campus:
West Lafayette
Research categories:
Energy and Environment, Microelectronics, Other
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Electrical Engineering
Desired experience:
previous experience with fusion technology
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Joseph
Pekny
The biochemical and electrophysiological mechanism of post-TBI neuronal degeneration using TBI-on-a-chip
Description:
Traumatic brain injury (TBI) is a major cause of morbidity and mortality worldwide, yet effective treatments remain lacking. Blast-induced TBI (bTBI), common in military settings, presents a particularly significant challenge. Mild blast-induced TBI (mbTBI), the most prevalent form, often produces minimal initial symptoms, leading to frequent underdiagnosis and missed therapeutic windows. However, secondary biochemical cascades triggered by TBI can drive long-term neurodegeneration and increase the risk of disorders such as Alzheimer's Disease and Parkinson's Disease. Oxidative stress is a key mechanism underlying secondary injury. In particular, acrolein, a highly reactive aldehyde generated during lipid peroxidation, acts as both a product and amplifier of oxidative damage and contributes significantly to post-traumatic neuronal injury. To better investigate these mechanisms, we developed an in vitro blast model, “bTBI-on-a-chip,” in which neuronal networks are cultured on microelectrode arrays (MEAs) to enable real-time monitoring of single-channel and network activity before, during, and after blast exposure. Using this platform, we examine immediate and long-term changes in neuronal firing patterns following mild blast injury, while simultaneously assessing biochemical changes such as acrolein production. This system provides unprecedented temporal resolution for studying blast-induced neural network dysfunction and offers a powerful platform for identifying pathological mechanisms and potential therapeutic strategies for blast-related neurotrauma.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology, Deep Learning, Medical Science and Technology, System-on-a-Chip
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Basic Medical Sciences
Professor:
Riyi
Shi
The impact of avocado consumption on inter- and intra-individual healthoutcome responses in post-menopausal women: a pilot study
Description:
Menopause leads to an accelerated decline in key metabolic functions, including bone
remodeling, cardiovascular health, and kidney function, increasing the risk of debilitating conditions such as osteoporosis, cardiovascular disease, and chronic kidney disease. Food-based interventions, particularly those rich in fruits and vegetables, have been shown to preserve bone mineral density, improve vascular health, and slow the decline in kidney function. These beneficial effects may be mediated through their impact on the gut microbiome, including increased short-chain fatty acid (SCFA) production. Avocados are nutrient-dense foods high in dietary fiber and other beneficial nutrients. Specifically, one medium avocado provides 10g of total fiber, with approximately 3g of this fiber being pectin. Pectin is a fermentable, viscous fiber that may provide benefits to bone, vascular, and kidney health through the production of SCFAs. However, the impact of daily avocado consumption on these health outcomes has not been explored in postmenopausal women. The goal of this proposal is to perform a pilot study evaluating the short-term effects of consuming meals with and without one medium avocado daily, providing approximately one-third of the adequate intake of dietary fiber on the gut microbiota composition, fecal SCFA, and markers of bone, vascular, and kidney health in postmenopausal women using a randomized, controlled, crossover design. Data collected will be utilized for a power analysis necessary for future grant applications. The student will be involved with data collection, analysis, and abstract/poster presentation.
remodeling, cardiovascular health, and kidney function, increasing the risk of debilitating conditions such as osteoporosis, cardiovascular disease, and chronic kidney disease. Food-based interventions, particularly those rich in fruits and vegetables, have been shown to preserve bone mineral density, improve vascular health, and slow the decline in kidney function. These beneficial effects may be mediated through their impact on the gut microbiome, including increased short-chain fatty acid (SCFA) production. Avocados are nutrient-dense foods high in dietary fiber and other beneficial nutrients. Specifically, one medium avocado provides 10g of total fiber, with approximately 3g of this fiber being pectin. Pectin is a fermentable, viscous fiber that may provide benefits to bone, vascular, and kidney health through the production of SCFAs. However, the impact of daily avocado consumption on these health outcomes has not been explored in postmenopausal women. The goal of this proposal is to perform a pilot study evaluating the short-term effects of consuming meals with and without one medium avocado daily, providing approximately one-third of the adequate intake of dietary fiber on the gut microbiota composition, fecal SCFA, and markers of bone, vascular, and kidney health in postmenopausal women using a randomized, controlled, crossover design. Data collected will be utilized for a power analysis necessary for future grant applications. The student will be involved with data collection, analysis, and abstract/poster presentation.
Campus:
West Lafayette
Research categories:
Cardiovascular Disease Research, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Nutrition Science
School/Dept.:
Nutrition Science
Professor:
Annabel
Biruete
More information: https://hhs.purdue.edu/directory/annabel-biruete/
The interfaces project and AI-ready datasets
Description:
This project aims to establish a next-generation, open scientific infrastructure for understanding and designing catalytic interfaces under realistic operating conditions. Modern catalyst discovery increasingly relies on artificial intelligence; however, existing datasets are largely built on idealized atomic models that fail to capture the dynamic reconstruction, chemical complexity, and environmental responsiveness of real interfaces. This project will develop a physics-validated database of experimentally and computationally derived interface structures, including reconstructed surfaces, oxide/metal interfaces, metal/metal interfaces, oxide/oxide interfaces, and their response to electrochemical environments. By integrating atomistic simulations, in situ experimental insights, and machine-learning-ready descriptors, the database will provide realistic atomic ensembles rather than simplified static models. The Interfaces Project will enable researchers to train predictive AI models on physically accurate data, improving transferability from model systems to real catalysts. Ultimately, this effort will create a community resource that accelerates catalyst discovery, enhances reproducibility across theory and experiment, and establishes a new standard for interface science in the AI era.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Energy and Environment, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Ming
Chen
The oocyte-to-egg and egg-to-embryo transitions in mammalian female gametes
Description:
The cell plasma membrane with the cell cortex (the region of the cytoplasm underlying the plasma membrane) is a critical region with important functions in all cells, serving as the interface between the cell's interior and the cell's exterior. We are interested in this region in female gametes, studying this in mouse as a mammalian model system. The functions and functionality of the oocyte membrane and cortex change through the different stages of oocyte development. This includes the final stages of oocyte development in the ovarian follicle, developing competency to complete meiosis and to initiate embryo development, and to respond to a fertilizing sperm. We are particularly interested in the cytoskeletal components of the cell cortex, specifically the actomyosin cytoskeleton, and how this changes through the developmental transitions of oocyte-to-egg (i.e., oocyte in the ovary to fertilizable egg in the oviduct) and egg-to-embryo (the egg responding to a fertilizing sperm). The goal of this project is to characterize candidate actin-associated proteins that we hypothesize are modified in response to intracellular signaling occurring during the oocyte-to-egg and the egg-to-embryo transitions. This project will mesh with complementary analyses in the lab of upstream signaling molecules, actin rearrangements, cellular mechanics (i.e., how soft or how rigid the cell is), and computational modeling of these cellular transitions.
Campus:
West Lafayette
Research categories:
Cellular Biology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Coursework in cell biology, understanding of microscopy, willingness to work with mice as experimental animals
School/Dept.:
Biological Sciences
Professor:
Janice
Evans
Thermal Cycling of Interconnects in 3D Heterogenous Integration in Packaging
Description:
This project will encompass image analysis and quantification of cracking, delamination, and the effect of defects, such as pores on thermal cycling behavior and reliability of Sn-based solder ball grid arrays (BGAs).
Campus:
West Lafayette
Research categories:
Advanced Packaging, Material Processing and Characterization, Microelectronics
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
School/Dept.:
MSE
Professor:
Nik
Chawla
More information: https://engineering.purdue.edu/ChawlaResGroup
Tumor Stromal Targeting
Description:
Our goal is to advance a local delivery system for the sustained release of hyaluronidase that can be placed with image guidance directly into the tumor to bypass the physical barriers present in PDAC tumors to reduce the mass transport limitations that occur due to desmoplasia. We hypothesize that by reducing the physical barrier created by desmoplasia, we can improve immune cell infiltration and the accumulation of systemically administered drugs into PDAC tumors. Our preliminary data demonstrates that we can inject a phase-sensitive in situ forming implant (ISFI) directly into a PDAC tumor under image guidance. Furthermore, we can deliver biologically active hyaluronidase from ISFIs. From these data we can predict not only the rate of release from the implants but optimize a placement plan within the tumors. Students will engage in experimental design, development of controlled release systems, and will learn to manage projects.
Campus:
West Lafayette
Research categories:
Medical Science and Technology
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Luis
Solorio
UPWARDS: AI-Assisted Design Automation of Integrated Circuits and Systems
Description:
This project is open to UPWARDS 11 partner universities. Selected participants will conduct research in electronic and multiphysics design automation of integrated circuits and systems, specifically in developing AI-based methods and techniques to overcome the limitation of current methodologies in modeling, simulation, and design. Strong background in mathematics, software programing, integrated circuits, electromagnetics physics is desired.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Big Data/Machine Learning, Deep Learning, Heterogeneous Integration, Microelectronics, Radiation Hardening, System-on-a-Chip, Thermal Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering
- Computer Engineering
- Computer Science
Desired experience:
Rising junior or senior students are preferred.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Dan
Jiao
More information: https://engineering.purdue.edu/~djiao/
Ultrafast Squeezed Light Generation and Control
Description:
In this project, the student will closely work with graduate students on experiments to generate and control ultrafast squeezed light from a nonlinear optical interaction in solid, liquid and gas phase targets. The goal of the project is to enhance the levels of achievable squeezing using strong laser field driven enhancement of nonlinear optical response. The student will assist with experiments and work on sophisticated data analysis. The student will gain experience with ultrafast femtosecond lasers and develop skills in ultrafast quantum optics.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Quantum Mechanics, Electromagnetism and other foundational physics courses. General interest and knowledge of Quantum Optics.
School/Dept.:
Physics and Astronomy
Professor:
Niranjan
Shivaram
More information: https://ultrafast.physics.purdue.edu/
Understanding carbohydrate storage across diverse plant species
Description:
Plants make carbohydrates through photosynthesis. Carbohydrates are then used by plants to support their metabolism and survival. Importantly, plants can store carbohydrates away for later use. These reserves are like a savings bank that plants use to persist during times of stress when they cannot perform photosynthesis. Understanding where plants store their carbohydrates throughout their bodies and how storage changes throughout the year is important for predicting plant resilience to environmental change. In this project, the student will be responsible for performing carbohydrate analyses in different plant tissues from a diversity of plant species. There is also the possibility of performing greenhouse work and field work for related projects. They will have the opportunity to learn skills in wet laboratory protocols, data analysis, data visualization, and data interpretation.
Campus:
West Lafayette
Research categories:
Ecology and Sustainability, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Botany and Plant Pathology
Professor:
Morgan
Furze
More information: www.morganfurze.com
Underwater Robotic Manipulation
Description:
Underwater operations such as inspection, assembly, and retrieval are critical to the maintenance and protection of seabed infrastructure, smart ports, and defense assets. This project is under the umbrella of a collaborative project to develop an autonomous underwater robotic platform equipped with a dexterous manipulator, multimodal sensing in complex underwater environments. Specifically, the student working on this SURF project will support the hardware assembly, calibration, testing, programming, as well as data collection and analysis.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics, Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Edwardson School of Industrial Engineering
Professor:
Yu
She
More information: https://www.purduemars.com/home
Unraveling the mechanisms by which BAM mediates OMP biogenesis
Description:
In our proposed studies, we will focus on characterizing the mechanism BAM uses to mediate OMP biogenesis to decipher if a conserved mechanism is used for the biogenesis of all OMPs and how it initially recognizes its substrates in cooperation with the periplasmic chaperones. These studies will address outstanding basic science questions about membrane protein biogenesis and trafficking in bacteria, while at the same time, providing the molecular blueprints and framework necessary for the discovery and development of novel vaccines and antibiotics to combat multi-drug resistance. The student's role in this project will be to determine the structures of BAM in complex with select substrates and with substrate mimics and with chaperones. The student will learn cloning, protein expression and purification, and develop a solid foundation in biophysical methods and structural biology including X-ray crystallography and cryo-EM.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Biological Sciences
Professor:
Nicholas
Noinaj
More information: https://www.noinajlab.com/
Untitled Project
Description:
The project goal is to develop ML models to simulate the behavior of materials under shock loading. Due to the complexity of the system, the simulation is expensive computationally. We will explore ML models to accelerate the prediction of damage, thermal response, and chemical reactions.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Marisol
Koslowski
Using network science for precision learning intervention
Description:
The goal of this project is to develop precision learning intervention technology that leverages semantic network science to support early language learning and early intervention for developmental language disorder (DLD). DLD affects approximately 7% of the population, and results in lifelong risks for poor biomedical, educational, and professional outcomes, leading to tremendous costs to individuals and society. Our group seeks to combine recent theoretical and technical advances to develop methods for early identification and intervention of this common, yet understudied condition. Student will participate in coding / development of automated tools that tune early language learning targets according to the knowledge of the learner and will help pilot and assess efficacy of different intervention approaches. Student will work with senior members of the lab (postdocs and lab manager) to develop and acquire data.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Learning and Evaluation, Medical Science and Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Preferred qualifications include: proficiency in R and/or Python, familiarity with Gitlab, exposure to or interest in learning about network science, and an interest in using remote technology to create engaging and effective early learning interventions in children under the age of 5.
School/Dept.:
Speech, Language, and Hearing Sciences
Professor:
Arielle
Borovsky
VAP 3D Printing of Energetics
Description:
This project will utilize explosive slurry mixtures to 3D print pore/void containing explosives. Printed explosives will be filled with inert and energetic materials to characterize various explosive phenomena such as jetting, penetration, detonation rate, fireball, and impulse. This project will consist of explosives mixing, 3D printing slurries, 3D modeling, charge preparation, test setup, high-speed camera operation, and data analysis.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/Energetics
VPS35 mediated trafficking in AD
Description:
Using genome editing tools to investigate how disruption of VPS35 expression impairs intracellular trafficking.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Biological Sciences
Professor:
Matthew
Tegtmeyer
Velocity and depth estimation of particle rebound using computer vision and machine learning
Description:
We have particle flight and rebound images that require velocity estimation from 1- and 2-camera systems. Students will use computer vision and machine learning algorithms for these efforts.
Campus:
West Lafayette
Research categories:
Deep Learning, Fluid Modelling and Simulation, Material Modeling and Simulation, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Zherui
Martinez-Guo
Video analytics for dairy feed management
Description:
Video analytics has the ability to fundamentally change animal agriculture by providing real-time analysis of operations on animal farms. In this project, we will focus on feed management. Potential projects include automating a video system to estimate the feed intake of individual
dairy cattle, and automating a video system that assesses the texture consistency of feed during the mixing and preparation processes. These video systems need to be robust and accurate even in challenging environments with various illumination and shadows.
The goal of this summer project is to explore computer vision methods to incorporate into these systems. The student will implement and conduct experiments using computer vision methods applied to a relevant set of videos. Depending on the project, the student may need to label with some videos with corresponding ground truth. The student will discuss their updates at weekly meetings and will present their findings with a written report and oral presentation. The student will end the summer with a greater understanding of how video analytics can assist precision agriculture, and particularly, dairy production.
dairy cattle, and automating a video system that assesses the texture consistency of feed during the mixing and preparation processes. These video systems need to be robust and accurate even in challenging environments with various illumination and shadows.
The goal of this summer project is to explore computer vision methods to incorporate into these systems. The student will implement and conduct experiments using computer vision methods applied to a relevant set of videos. Depending on the project, the student may need to label with some videos with corresponding ground truth. The student will discuss their updates at weekly meetings and will present their findings with a written report and oral presentation. The student will end the summer with a greater understanding of how video analytics can assist precision agriculture, and particularly, dairy production.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering Technology
- Computer Engineering
- Computer Science
Desired experience:
Python programming is required
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Amy
Reibman
Visual learning paradigms in mouse models of autism spectrum disorders
Description:
The student will learn to run freely moving behavioral paradigms of mouse models of visual learning and apply them for mouse models of autism spectrum disorders (ASDs). This will be followed by deep learning-based analysis of behavioral videos of mice.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Genetics, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Biological Sciences
Professor:
Alexander
Chubykin
More information: https://chubykinlab.wixsite.com/chubykinlab
Volumetric Imaging of Mineral Precipitation on RoughFracture Surfaces in Rock
Description:
Subsurface engineering activities often involve the injection and withdrawal of fluids through fracture networks. Fluid - rock interactions can induced geochemical reactions that can alter these fluid conduits. In this study, research will examine the role of mineralogy along the fracture surface on calcium carbonate mineral precipitation. The student will (1) use 3D X-ray imaging to identify the mineralogy of a fractured rock sample and to capture the growth of precipitates along the surface, and (2) perform image analysis to quantify the changes in the surface caused by mineral precipitation and extract area or volumetric distributions of precipitates and determine.
Campus:
West Lafayette
Research categories:
Energy and Environment, Environmental Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Previous work in a laboratory
School/Dept.:
Physics and Astronomy
Professor:
Laura
Pyrak-Nolte
Wireless Synchronized Smart Wearable Network for Continuous Cardiovascular Health Monitoring in Daily Life
Description:
People commonly wear multiple accessories simultaneously. We are designing unobtrusive smart jewelry devices (e.g., nail and necklace form factors), users can comfortably wear several at once. This enables a distributed on-body sensing network that collects synchronized physiological signals from multiple body locations.
By measuring photoplethysmography (PPG) at distal sites (e.g., fingernails and earlobe) and synchronizing it with ECG recorded at the chest, we can compute Pulse Transit Time (PTT)—the time it takes for the arterial pulse wave to travel from the heart to peripheral sites. PTT is inversely correlated with blood pressure and serves as a key indicator of vascular health. Unlike cuff-based systems, this approach enables continuous, longitudinal blood pressure trend monitoring in everyday settings without user intervention.
The project aims to build and validate a wireless, synchronized smart jewelry network for multi-site physiological sensing and longitudinal vascular monitoring in daily life.
Student Role:
The student will analyze multi-site time-series PPG data to:
• Develop signal processing pipelines for precise synchronization and PTT extraction
• Model relationships between PTT, blood pressure trends, posture, daily activities, and nutritional factors (e.g., caffeine intake)
• Design machine learning models to predict longitudinal cardiovascular patterns
Through this project, the student will gain experience in physiological signal processing, time-series modeling, wearable sensing systems, and machine learning tools and frameworks (e.g., PyTorch or TensorFlow).
By measuring photoplethysmography (PPG) at distal sites (e.g., fingernails and earlobe) and synchronizing it with ECG recorded at the chest, we can compute Pulse Transit Time (PTT)—the time it takes for the arterial pulse wave to travel from the heart to peripheral sites. PTT is inversely correlated with blood pressure and serves as a key indicator of vascular health. Unlike cuff-based systems, this approach enables continuous, longitudinal blood pressure trend monitoring in everyday settings without user intervention.
The project aims to build and validate a wireless, synchronized smart jewelry network for multi-site physiological sensing and longitudinal vascular monitoring in daily life.
Student Role:
The student will analyze multi-site time-series PPG data to:
• Develop signal processing pipelines for precise synchronization and PTT extraction
• Model relationships between PTT, blood pressure trends, posture, daily activities, and nutritional factors (e.g., caffeine intake)
• Design machine learning models to predict longitudinal cardiovascular patterns
Through this project, the student will gain experience in physiological signal processing, time-series modeling, wearable sensing systems, and machine learning tools and frameworks (e.g., PyTorch or TensorFlow).
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biotechnology Data Insights, Cardiovascular Disease Research, Deep Learning, Human Factors, Internet of Things (IoT), Mobile Computing
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
- Computer Engineering
- Electrical Engineering
- Biomedical Engineering
Desired experience:
One of the following:
Machine Learning, Signal Processing, Deep Learning, Human-Computer Interaction
School/Dept.:
Computer Science
Professor:
Qiuyue (Shirley)
Xue
More information: https://xueqiuyue.com/
microfluidic manipulation of biological systems
Description:
this project will use microfluidics, 3d printing, electronics, machine learning, imaging processing to achieve real time classification and sorting of biological systems.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Biotechnology Data Insights, Fluid Modelling and Simulation, System-on-a-Chip
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Li
Zhan
More information: https://www.zhanlab.org/home