2025 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 (262)
3D Urban Modeling using GeoAI
Description:
The objective of this project is to establish a framework for generating Level of Detail 3 (LoD3) building models for digital twins. The development of algorithms for 3D building modeling is a well-explored subject in geomatics, serving as a crucial foundation for a wide range of civil engineering industries, including urban planning and transportation management. Currently, the field faces challenges in advancing models from LoD2 to LoD3, which necessitates the inclusion of detailed features such as openings in buildings. To address this, the project employs a variety of deep learning models to extract semantic information from imagery and integrate it into the existing LoD2 models. Through this project, students will gain insights into the general process of 3D building modeling, leveraging artificial intelligence and photogrammetric principles within the field of geomatics.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Engineering the Built Environment
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Python programming skill
School/Dept.:
Lyles School of Civil Engineering
Professor:
Jinha
Jung
More information: https://gdsl.org
3D printed dome-patterned arrays: Modeling, Fabrication, and Design
Description:
Multistable panels are thin shells capable of achieving several stable shapes owing do prestress fields arising from thermal mismatch during manufacturing in composites, plastic stresses from deformation in metals, stretched and glued polymeric sheets (check supplementary material to see this), or induced thermal stresses during 3D printing. This prestressed multistable shells differ from counterparts in which their multistability arises purely from their unstressed shapes, as for domes and arch-like structures. A combination of shape-based and stress-based multistability can provide a much larger design space for achieving complex reconfiguration with high stiffness, high temperature materials, such as copper-beryllium or steel both of which are 3D printable.
The objective of this project is model the fabrication process and the resulting structural response of bistable dome-shapes structures. The modeling part includes familiarizing with the mechanics of dome-shaped units. In parallel, finite element simulations will be used to model the 3D printing process and predict the resulting stresses from manufacturing. Finally, a complete set of simulations of the printed domes and their stability (i.e., the predicted shapes and the snapping process) will be conducted to build an understanding of how processing (i.e., from printing) and geometrical (i.e., shape) parameters affect the response of such bistable structures.
The objective of this project is model the fabrication process and the resulting structural response of bistable dome-shapes structures. The modeling part includes familiarizing with the mechanics of dome-shaped units. In parallel, finite element simulations will be used to model the 3D printing process and predict the resulting stresses from manufacturing. Finally, a complete set of simulations of the printed domes and their stability (i.e., the predicted shapes and the snapping process) will be conducted to build an understanding of how processing (i.e., from printing) and geometrical (i.e., shape) parameters affect the response of such bistable structures.
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
Desired experience:
Finite elements analysis
Mechanics of materials
3D printing experience
School/Dept.:
School of Mechanical Engineering
Professor:
Andres
Arrieta
More information: https://engineering.purdue.edu/ProgrammableStructures/
3D printing of soil
Description:
Additive manufacturing (3D printing) is finding increasing application in the concrete construction sector thanks to the unique advantages it provides relative to traditional construction methods in terms of efficiency of construction, reduced material waste, and the ability to create geometrically complex structures. 3D printing of earth materials offers additional opportunities in terms of sustainability, particularly if construction can rely on locally available soils. Our research is aimed at the design of soil-based “inks” for 3D printing, with specific focus on understanding how the rheological properties of these mixtures can be optimized depending on the characteristics of locally available soils.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Civil Engineering, Materials Engineering, Mechanical Engineering
Desired experience:
The undergraduate researcher involved in the project will be responsible for a) writing code for 3D printing of simple prototypes, and b) exploring preliminary formulations for clay-based “inks.”.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Marika
Santagata
A Digital Health Solution to Mitigate Overuse Injury Risk in Elite Platform and Springboard Diving
Description:
In springboard and platform diving, overuse injuries are common, especially due to repetitive, high velocity impacts and hyperextensions or flexions of joints. Subjective complaints of pain and coach intuition are the current status quo for injury mitigation, leaving substantial room for improvement. Although effective at lower levels, elite athletes require the most optimization and efficiency in their training to maximize results, to prevent training delays, and to prevent career ending injuries. The lack of objective, quantitative data may prevent both coaches and athletes from making proper or fully informed training decisions, impacting the performance of the athlete from a physical and mental perspective. Wearable sensors could allow for the objective, individualized measurement, and temporal tracking of various facets of athlete performance during training or in competition. For elite divers, such technologies – customized for the application and needs of the athletes – could provide new opportunities to optimize training regimes with quantitative approaches, including algorithms designed to identify or predict early signs of overuse or other injury, algorithms to track limb/joint motion and impact forces, and algorithms to track functional recovery under the guidance of a coach or physician. In this project, a qualified engineering student with experience as a platform/springboard diver or as a college athlete will work with Dr. Ward and doctoral students in his lab to fully define project and application constraints, define the types of sensors and form factor of a prototype wearable that can be used for divers, select suitable components to construct the wearable device, and construct a prototype for pilot testing.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Human Factors, Medical Science and Technology, Mobile Computing
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
- Mechanical Engineering
- Electrical Engineering
- Materials Engineering
Desired experience:
Due to the intended end users of the technology that will come from this project, the most qualified student for this project will have experience as an athlete or coach at the college or professional level. The student should have at least an intermediate level of experience with programming in python, Matlab, or other common programming language.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Matthew
Ward
A microfluidics approach for detecting alpha-synuclein seeds in biospecimens from Parkinsons disease patients
Description:
Seed amplification assays (SAAs) are a powerful method to detect alpha-synuclein (aSyn) aggregates (or seeds) in biospecimens from Parkinson's disease (PD) patients. These assays capitalize on the ability of misfolded aSyn seeds in a biospecimen to induce the aggregation of recombinant aSyn monomers into fibrils, which can then be detected using a fluorescent dye. One of the primary limitations of SAAs is their long duration, resulting from a prolonged lag time before detectable aggregation occurs. To address this challenge, our group aims to optimize several assay parameters to reduce the overall assay time. Specifically, the SURF involved in this project will examine various solution conditions, adjust the amount of input biospecimen, use different extraction steps to clean up the biospecimen, and test different recombinant aSyn variants as the monomeric form added to the assay. Beyond these optimizations, the student (along with graduate students in our lab) will collaborate with Purdue engineers who specialize in injectors and microfluidics to further accelerate the templated aggregation process central to the SAA. By incorporating advanced microfluidic techniques, we aim to enhance the efficiency of seed-monomer interactions and reduce the time required for fibril formation. Our multi-faceted approach is designed to make SAAs a faster and more reliable diagnostic tool, ultimately improving the early detection and monitoring of PD.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Medicinal Chemistry and Molecular Pharmacology
Professor:
Chris
Rochet
More information: https://www.mcmp.purdue.edu/faculty/jrochet
A phantom road approach to understanding anthropogenic effects on frog choruses
Description:
The student will be studying the effects of traffic noise and artificial light at night (ALAN) on frogs and toads in the Greater Lafayette Area. Our previous work has evaluated short term effects of this sensory pollutants on frog choruses and their signals. In this study, we will investigate the effect of creating 'phantom roads' that mimic road conditions in terms of traffic noise and ALAN without an actual road (no mortality or barrier). The student will learn about anuran identification, bioacoustics, and behavioral ecology.
Campus:
West Lafayette
Research categories:
Ecology and Sustainability
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biology
- Ecology Evolution and Environmental Sciences
School/Dept.:
Biological Sciences
Professor:
Ximena
Bernal
AAMP-UP - PERC: Additively Manufactured Reactive Structures
Description:
In this project we are focused on multifunctional energetic materials. The objective of this project is to use fused deposition modeling (FDM) to print and characterize structures that are also reactive. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
Research categories: propellants, combustion, advanced materials
Preferred major(s): No Major Restriction
Desired experience: AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2025. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required. No summer classes are allowed.
Research categories: propellants, combustion, advanced materials
Preferred major(s): No Major Restriction
Desired experience: AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2025. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required. No summer classes are allowed.
Campus:
West Lafayette
Research categories:
Heterogeneous Integration, Material Processing and Characterization, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
- Mechanical Engineering
- Materials Engineering
- Aeronautical and Astronautical Engineering
- Chemical Engineering
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2025. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required. No summer classes are allowed.
School/Dept.:
School of Mechanical Engineering
Professor:
Steven
Son
More information: https://web.ics.purdue.edu/~sson/Son_Webpage/SonWeb_index.html
AAMP-UP! - PERC: Advanced High-Density Fuels in Energetic Materials
Description:
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.
This project is from the AAMP-UP summer program, which provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories: advanced materials, combustion, propellants
This project is from the AAMP-UP summer program, which provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories: advanced materials, combustion, propellants
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Heterogeneous Integration, Material Processing and Characterization, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Desired experience: AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2025. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required. No summer classes are allowed.
School/Dept.:
School of Mechanical Engineering
Professor:
Steven
Son
More information: https://web.ics.purdue.edu/~sson/Son_Webpage/SonWeb_index.html
AAMP-UP! - PERC: Developing and Validating Models to Understand Particle Scale Adhesion
Description:
This project is focused on developing and validating models that quantifying the adhesion between particles and surfaces. The models being developed address the effects of particle and surface roughness, the role of steric repulsion on adhesion and applicability with techniques such as atomic force microscopy and the enhanced centrifuge method. Undergraduate researchers on this project will be tasked with performing experiments, data analysis and code/script writing. Must be a US citizen to work on this project. Please contact Manuel Vazquez (vazque18@purdue.edu) with any questions.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis, Material Modeling and Simulation, Material Processing and Characterization, Other
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemical Engineering
- Chemistry
- Mechanical Engineering
- Materials Engineering
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2025. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required. No summer classes are allowed.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Steve
Beaudoin
More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=11574
AAMP-UP! - PERC: Parametric studies for the simultaneous thermal and high-rate response of granular energetic materials
Description:
Project aims to design a small benchtop-scale local thermal heating device in conjunction with Kolsky bar for simultaneous thermal and high-rate loading. Student will be in charge of designing device based on prior successful iterations, and will perform parametric studies for small-scale experiments.
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Material Processing and Characterization
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Computer aided design (CAD), mechanics of materials, differential equations, Python/MATLAB expertise.
Hands-on machining experience will be highly useful.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Zherui
Martinez-Guo
AAMP-UP! - PERC: Post Detonation Fireballs and Explosive Driven Shock Waves
Description:
This research topic seeks to explore the reactions and induced gas shocks occurring after a detonation. This includes It will involve advanced sample preparation of the explosives and added metal fuels or other additives, and dynamic experiments. This project will focus on applying advanced laser diagnostics to both the induced shock and post-detonation reactions. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
This project is from the AAMP-UP summer program, which provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories: High rate experiments, explosives
This project is from the AAMP-UP summer program, which provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories: High rate experiments, explosives
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
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
- Materials Engineering
- Chemical Engineering
School/Dept.:
School of Mechanical Engineering
Professor:
Steven
Son
More information: https://web.ics.purdue.edu/~sson/Son_Webpage/SonWeb_index.html
AAMP-UP! - PERC: Solid State Environmentally Friendly Fire Extinguisher Propellant
Description:
With the phase-out of Halon usage for aviation fire protection, the aerospace industry needs to identify solutions that will help protect the cargo bay. Halon alternative research to date has not identified a system that can be similarly sized to current systems impacting the weight and fuel consumption. The use of chemically inert gases such as nitrogen or carbon dioxide mitigates regulatory risks such as proposed PFAS (per- and polyfluorinated alkyl substance) restrictions. The weight and size of inert fire suppression systems using relatively heavy gas cylinders are not attractive for aerospace applications. The purpose of this project is to identify, synthesize and conduct proof-of-concept testing for solid-state propellants that generate inert gaseous products such as nitrogen and carbon dioxide (with insignificant levels of toxic or harmful bi-products) for use in aircraft cargo bay fire suppression systems. This research topic explores the development of these propellants. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
This project is from the AAMP-UP summer program, which provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories: advanced materials, combustion, propellants
Preferred major(s): No Major Restriction
Desired experience: AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2025. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required. No summer classes are allowed.
This project is from the AAMP-UP summer program, which provides STEM undergraduates the chance to participate in national defense and military research. The program is sponsored by the U.S. Army Research Laboratory in Aberdeen, MD.
Research categories: advanced materials, combustion, propellants
Preferred major(s): No Major Restriction
Desired experience: AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2025. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required. No summer classes are allowed.
Campus:
West Lafayette
Research categories:
Ecology and Sustainability, Energy and Environment, Heterogeneous Integration, Material Processing and Characterization, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2025. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required. No summer classes are allowed.
School/Dept.:
School of Mechanical Engineering
Professor:
Steven
Son
More information: https://web.ics.purdue.edu/~sson/Son_Webpage/SonWeb_index.html
AAMP-UP! - PERC: Synthesis of high nitrogen and energetic materials
Description:
Chemical synthesis of high-nitrogen and energetic materials (propellants, explosives).
Student will be involved in experimental work in the lab.
Student will be involved in experimental work in the lab.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis, Other
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemistry
- Chemistry
Desired experience:
organic chemistry lab experience
School/Dept.:
School of Materials Engineering
Professor:
Davin
Piercey
More information: www.davinpiercey.com
AAMP-UP! - PERC: Understanding Adhesion of Energetic Particles
Description:
This project is focused on quantifying the adhesion of energetic particles to surfaces of interest through atomic force microscopy. This method uses a single-particle probe which is brought into an out of contact with the surface of interest and the strength of interaction measured. Undergraduate researchers on this project will be tasked with probe preparation, sample characterization, performing experiments and data analysis. Must be a US citizen to work on this project. Please contact Manuel Vazquez (vazque18@purdue.edu) with any questions.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis, Material Processing and Characterization, Other
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemical Engineering
- Chemistry
- Mechanical Engineering
Desired experience:
AAMP-UP asks that each student applicant have finished 1 semester of higher education, be currently enrolled in a college or university, and graduate after August 2025. In addition, students must be U.S. Citizens or U.S. Persons. No prior experience with the U.S. military is required. No summer classes are allowed.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Steve
Beaudoin
More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=11574
AAMP-UP!-PERC High-rate response of layered and functionally-graded granular/energetic materials
Description:
Project aims to perform parametric studies on layered energetic materials, which will be manufactured in-house or 3D-printed with Zucrow lab facilities. Dynamic response will be analyzed using Kolsky bars.
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Material Processing and Characterization
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Computer-aided design, Python/Matlab programming, dynamic response, differential equations, electrical/mechanical work experience
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Zherui
Martinez-Guo
AI assisted advanced sensing for civil infrastructure
Description:
This project aims to explore the application of the electromechanical impedance (EMI) method for structural health monitoring (SHM). The EMI technique relies on the interaction between piezoelectric sensors and host structures, where changes in structural conditions alter the impedance response of the sensors. By analyzing these impedance variations, it is possible to assess structural integrity and detect damage. The project will involve the development and validation of EMI-based monitoring techniques for concrete structures, with a focus on establishing the relationship between EMI signatures and structural integrity.
Type of work:
Learn and investigate the sensor design.
Study the fundamental principles of EMI-based structural health monitoring.
Analyze impedance signals to identify damage indicators.
Develop signal processing to improve damage detection.
Type of work:
Learn and investigate the sensor design.
Study the fundamental principles of EMI-based structural health monitoring.
Analyze impedance signals to identify damage indicators.
Develop signal processing to improve damage detection.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Civil Engineering
- Electrical Engineering
- Data Science
Desired experience:
Basic knowledge of concrete structure, electrical circuits, and signal processing.
Applicants with experience with data analysis and sensor technology are preferred.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Yining
Feng
AI for Urban Digital Twins
Description:
We are developing computational tools to assist with urban gray and green design and planning to create better and more sustainable cities.
Campus:
West Lafayette
Research categories:
Deep Learning, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Computer Science
Professor:
Daniel
Aliaga
More information: www.cs.purdue.edu/homes/aliaga
AI-driven design of sustainable buildings
Description:
The project is focused on employing principles of innovation science to define and guide automated processes for building design and sustainability assessment. Work will focus on defining sustainability metrics and related development of algorithms that capture design decisions that may have relationships to these metrics which could be informed through autonomous data analysis and/or logic.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Knowledge of innovation science principles
School/Dept.:
Lyles School of Civil Engineering
Professor:
Joe
Sinfield
Acoustic and Electric-Fields to Enhance Membrane Filtration in Water and Air
Description:
This interdisciplinary project explores innovative technologies for separation processes, integrating acoustic forces and electric-field interactions with membranes to address critical challenges in air and water purification. The research combines fluid mechanics, catalysis, aerosol transport, and optics principles to develop high-efficiency filtration and separation systems. The technology aims to protect human health and sustainability through new capabilities to remove bioaerosols (e.g. containing viruses), smoke from fires and fossil fuels, and contaminants in the water.
Acoustic Forces for Air Filtration
Acoustic air cleaning leverages acoustic standing waves and streaming forces to manipulate aerosol movement within flowing streams. By harnessing these forces, it becomes possible to control the transport and capture of aerosols, including particulate matter (PM) 2.5 and other airborne pollutants. This project aims to enhance the performance of fiber filters by using acoustic forces to direct aerosols toward filter fibers, addressing challenges associated with the most penetrating particle sizes (MPPS).Currently, a bench-scale experimental setup is under construction to validate these findings. This setup will include:
Generation of realistic aerosols to simulate real-world particle size distributions.
Integration of acoustic filtration devices within a controlled airflow duct.
Measurement of particle concentrations upstream and downstream to evaluate filtration performance.
Electric-Field Interaction for Water Filtration
This component focuses on self-generated electric fields in membranes submerged in aqueous solutions, where catalytic reactions at membrane surfaces drive fluid flow. The project aims to understand and optimize the interactions between electric fields and fluid transport by coupling electrokinetic effects with advanced separation techniques. Particle tracking velocimetry (PTV) will be employed to visualize and quantify the flow behavior near the membranes.
Membrane related technologies
Synthetic membranes are ubiquitous in various fields, such as separation processes, desalination, and reactor systems. We are developing innovative methods for high-efficiency air conditioning, using water vapor-selective membranes for dehumidification. With new process designs and novel material enhancements, such a technology can provide significant energy savings for buildings. Since HVAC operations account for nearly 20% of the primary energy consumed in the US, developing more efficient cooling systems is a significant target for combating global warming and resource depletion. We also investigate whether current membrane distillation (MD) systems can be made more efficient. This includes work on the effect of tilt angle on air gap MD performance, system optimization, new modeling techniques for MD, superhydrophobic condensing for MD, and design of multistage MD systems. The analysis for these systems includes thermodynamic and heat transfer modeling with Engineering Equation Solver, designing and building experimental systems for testing heat transfer enhancements and the efficiency of new configurations.
Acoustic Forces for Air Filtration
Acoustic air cleaning leverages acoustic standing waves and streaming forces to manipulate aerosol movement within flowing streams. By harnessing these forces, it becomes possible to control the transport and capture of aerosols, including particulate matter (PM) 2.5 and other airborne pollutants. This project aims to enhance the performance of fiber filters by using acoustic forces to direct aerosols toward filter fibers, addressing challenges associated with the most penetrating particle sizes (MPPS).Currently, a bench-scale experimental setup is under construction to validate these findings. This setup will include:
Generation of realistic aerosols to simulate real-world particle size distributions.
Integration of acoustic filtration devices within a controlled airflow duct.
Measurement of particle concentrations upstream and downstream to evaluate filtration performance.
Electric-Field Interaction for Water Filtration
This component focuses on self-generated electric fields in membranes submerged in aqueous solutions, where catalytic reactions at membrane surfaces drive fluid flow. The project aims to understand and optimize the interactions between electric fields and fluid transport by coupling electrokinetic effects with advanced separation techniques. Particle tracking velocimetry (PTV) will be employed to visualize and quantify the flow behavior near the membranes.
Membrane related technologies
Synthetic membranes are ubiquitous in various fields, such as separation processes, desalination, and reactor systems. We are developing innovative methods for high-efficiency air conditioning, using water vapor-selective membranes for dehumidification. With new process designs and novel material enhancements, such a technology can provide significant energy savings for buildings. Since HVAC operations account for nearly 20% of the primary energy consumed in the US, developing more efficient cooling systems is a significant target for combating global warming and resource depletion. We also investigate whether current membrane distillation (MD) systems can be made more efficient. This includes work on the effect of tilt angle on air gap MD performance, system optimization, new modeling techniques for MD, superhydrophobic condensing for MD, and design of multistage MD systems. The analysis for these systems includes thermodynamic and heat transfer modeling with Engineering Equation Solver, designing and building experimental systems for testing heat transfer enhancements and the efficiency of new configurations.
Campus:
West Lafayette
Research categories:
Energy and Environment, Engineering the Built Environment, Fluid Modelling and Simulation, Material Modeling and Simulation, Material Processing and Characterization, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Civil Engineering
- Chemical Engineering
- Materials Engineering
Desired experience:
mary Goals and Responsibilities
Design and Execution: Support the grad mentor in developing experimental setups or computational modeling to investigate the interaction of acoustic waves or electric fields with particles in air and water systems.
Preferred Skills:
In addition to a background in fluid mechanics, aerosol transport, catalysis, and optics, experience in the following areas is highly desirable:
CAD software for experimental design.
MATLAB/Python for numerical analysis and data processing.
Adobe Illustrator for data visualization and presentation.
This combined approach aims to advance the understanding and application of acoustic and electric-field interactions in separation processes, addressing pressing challenges in air and water purification.
School/Dept.:
School of Mechanical Engineering
Professor:
David
Warsinger
More information: www.warsinger.com
Advanced AI Applications for Musculoskeletal Imaging Analysis
Description:
Project Description:
This project focuses on the development and application of machine learning, deep learning, and conventional image processing techniques for analyzing medical images acquired through advanced imaging modalities, specifically High-Resolution Peripheral Quantitative Computed Tomography (HR-pQCT) and Ultra-Short Echo-Time MRI (UTE-MRI). The research aims to investigate musculoskeletal changes to address clinically significant questions such as fracture susceptibility, fracture healing stages, and the impact of weight loss on skeletal health in different patient populations.
Student Responsibilities:
Assist in the organization and curation of imaging datasets from HR-pQCT and UTE-MRI scans.
Preprocess and segment musculoskeletal images for feature extraction and AI model training.
Develop or fine-tune machine learning pipelines for musculoskeletal health analysis.
This project focuses on the development and application of machine learning, deep learning, and conventional image processing techniques for analyzing medical images acquired through advanced imaging modalities, specifically High-Resolution Peripheral Quantitative Computed Tomography (HR-pQCT) and Ultra-Short Echo-Time MRI (UTE-MRI). The research aims to investigate musculoskeletal changes to address clinically significant questions such as fracture susceptibility, fracture healing stages, and the impact of weight loss on skeletal health in different patient populations.
Student Responsibilities:
Assist in the organization and curation of imaging datasets from HR-pQCT and UTE-MRI scans.
Preprocess and segment musculoskeletal images for feature extraction and AI model training.
Develop or fine-tune machine learning pipelines for musculoskeletal health analysis.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Some coding experience is preferred. There will be a lot of image processing, so you should be comfortable spending long periods at the computer.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Rachel
Surowiec
Advanced Heat Pumps
Description:
Current HVAC&R industry is facing numerous challenges associated with transitioning to low-GWP refrigerants, enhanced heating mode operations, cold-climate conditions, and future performance ratings. In this project, numerical and experimental techniques will be employed to investigate new low-GWP refrigerants and their impact on component sizing and performance. The student will be involved with psychrometric chamber testing and conduct multi-objective optimizations.
Campus:
West Lafayette
Research categories:
Engineering the Built Environment, Thermal Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
Desired experience:
Good understanding of thermodynamics (first law, second law, cycle analyses, etc.); basic coding skills (e.g., MATLAB, Python).
School/Dept.:
School of Mechanical Engineering
Professor:
Davide
Ziviani
Advancing Space Exploration with Origami-Inspired Robot Design and Prototyping
Description:
The exploration of space environments presents unique challenges that require innovative solutions in robotics. This undergraduate research project aims to leverage the principles of origami to create compact, deployable robots, e.g., robotic arm or end effectors, that are both lightweight and structurally robust, ideal for space missions. The focus will be on the design, simulation, and prototyping of origami-inspired robots that can adapt and perform in the harsh conditions of space.
Type of work required:
(1) Robot Design: Develop origami-based designs that allow for compact storage and deployment in space environments. Explore various folding patterns and materials to enhance the mobility and functionality of space robots.
(2) Control Systems Integration: Design control systems that can effectively manage the deployment and operation of origami robots in space. This includes the integration of sensors and actuators that facilitate autonomous or remote operation.
(3) Prototype Development: Build prototypes to validate the design concepts. Testing will involve evaluating the folding mechanisms, structural stability, and operational capabilities under conditions that mimic space environments.
Type of work required:
(1) Robot Design: Develop origami-based designs that allow for compact storage and deployment in space environments. Explore various folding patterns and materials to enhance the mobility and functionality of space robots.
(2) Control Systems Integration: Design control systems that can effectively manage the deployment and operation of origami robots in space. This includes the integration of sensors and actuators that facilitate autonomous or remote operation.
(3) Prototype Development: Build prototypes to validate the design concepts. Testing will involve evaluating the folding mechanisms, structural stability, and operational capabilities under conditions that mimic space environments.
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
- Computer Science
Desired experience:
Prior involvement in projects or coursework focusing on robotics, mechanical design, or materials science is preferred. Experience with 3D printing, CAD modeling, and robotics programming is required. Applicants are required to indicate relevant experience in the application.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Ran
Dai
More information: https://engineering.purdue.edu/AOL/research
Aerodynamic Tail and Feet design for Legged Robots
Description:
Students can expect to work closely with a PostDoctoral researcher in the lab on designing, fine-tuning, fabricating, and validating aerodynamic tails, feet, and legs for robot dogs to expand their locomotion capabilities. Students should be willing to work with different materials, 3D printing, injection molding, and machining.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics, Material Modeling and Simulation, Material Processing and Characterization, Medical Science and Technology, Microelectronics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Mechanical Engineering
- Materials Engineering
- Industrial Design
- Electrical Engineering
Desired experience:
Experience working with 3D printers is essential. Experience with machines, materials, molding, etc is appreciated.
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Upinder
Kaur
Agent-Based Modeling of NTM Pharmacodynamics
Description:
Background: Nontuberculous mycobacteria lung disease (NTM-LD) has increased in prevalence globally over the past two decades. NTM species exist in the environment and cause can pulmonary infection following inhalation. Failure to clear the bacterial population results in chronic infection and lung damage. Antibiotic treatment for NTM-LD is 40-80% effective, and antibiotic testing on in vitro NTM populations does not always correlate to in vivo efficacy. To improve remission rates following antibiotic treatment for NTM-LD, it is critical to understand the in vivo pharmacodynamics at the pulmonary site of infection.
Student Role: The undergraduate researcher will work on developing an agent-based model of an NTM lung tissue infection coupled to population pharmacokinetic data of antibiotic concentrations in the body.
Student Role: The undergraduate researcher will work on developing an agent-based model of an NTM lung tissue infection coupled to population pharmacokinetic data of antibiotic concentrations in the body.
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Logic, ODEs, proficiency in programming
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Elsje
Pienaar
Airborne Launch and Recovery System (ALARS)
Description:
The aim of this project is to develop advanced algorithms and technical designs for an Airborne Launch and Recovery System (ALARS) tailored for Unmanned Underwater Vehicles (UUVs). Current ship-based launch and recovery methods for small UUVs are operational bottlenecks—often cumbersome and time-intensive, negatively affecting overall mission efficiency. By implementing ALARS, this project seeks to enable the simultaneous deployment and recovery of multiple UUVs, thereby expanding their range and versatility across diverse mission profiles. Furthermore, the ALARS system holds the potential to support the deployment and retrieval of additional assets, including floating sensors and sensor buoys, broadening its applications and impact.
Campus:
West Lafayette
Research categories:
Engineering the Built Environment, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
We are seeking students with a strong background in design and control systems to contribute to the development of this innovative project.
School/Dept.:
School of Mechanical Engineering
Professor:
Nina
Mahmoudian
More information: https://engineering.purdue.edu/mahmoudian/
All-Optical Neural Networks for Artificial Intelligence
Description:
Machine learning techniques, particularly those based on artificial neural networks (ANNs), have significantly advanced fields such as computer vision and natural language processing, as well as fundamental studies in physics and materials science. Despite considerable advancements in AI algorithms and hardware optimizations aimed at reducing model sizes and accelerating inference and training processes, most efforts have thus far concentrated on traditional electronic systems, including CPUs, GPUs, FPGAs, and ASICs. These systems, while powerful, consume substantial amounts of energy and are unable to meet the increasing demands of rapidly growing data volumes, especially as we approach the limits of Moore's Law. Optical implementation of AI modules, such as optical neural networks (ONNs), presents a compelling alternative that leverages inherent parallelism, high-speed computation, and potential for low energy consumption. Notably, recent studies show that the energy cost of optical implementations has the potential to be 2–3 orders of magnitude less than that of state-of-the-art CMOS implementations. The wave nature and superposition principle of light enables natural parallel processing, such as performing matrix-vector multiplications in constant time—a stark contrast to the quadratic time complexity of digital electronic processors. Additionally, ONNs can execute complex-valued arithmetic by encoding information in both the phase and magnitude of light, further exploiting the unrivaled speed of light as an information carrier.
In this project, the SURF undergraduate student will work with a graduate student to develop both opto-electronic hardware and software for implementing an all-optical neural network, and apply it for AI applications. I expect the SURF undergraduate student play the same role as a graduate student, and have opportunities for coauthor-ship and coinventor-ship.
In this project, the SURF undergraduate student will work with a graduate student to develop both opto-electronic hardware and software for implementing an all-optical neural network, and apply it for AI applications. I expect the SURF undergraduate student play the same role as a graduate student, and have opportunities for coauthor-ship and coinventor-ship.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Deep Learning, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering
- Physics
- Computer Engineering
- Computer Science
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Shengwang
Du
More information: https://engineering.purdue.edu/QuantumOptics
An integrative approach to understanding anthropogenic effects on frog choruses
Description:
Urbanization increases background noise levels and artificial light at night (ALAN), sensory pollutants that can affect organisms in that area. Among those, frogs and toads are particularly susceptible given their nocturnal habits and reliance on acoustic communication. We integrate neuroethology, bioacoustics, and behavioral ecology to investigate how traffic noise and ALAN affect frog communication systems in the Greater Lafayette Area. We have previously evaluated short term responses to these pollutants and will examine longer effects on calling behavior and signaling strategies. The student participating in this project will lead a study examining the effects of creating a "phantom road" simulating the noise and light levels generated by a road passing by a frog breeding area. The student will thus learn about field playback experiments, sound analysis, and sustainable approaches to road design.
Campus:
West Lafayette
Research categories:
Ecology and Sustainability
Preferred major(s):
- Biology
- Ecology Evolution and Environmental Sciences
Desired experience:
Basic general Biology, Intro Ecology & Evolution (BIO 286 or equivalent).
School/Dept.:
Biological Sciences
Professor:
Ximena
Bernal
Analysis of thermal systems using environmentally friendly working fluids
Description:
This project will include the design and testing of fluids, components and systems used for decarbonization of heating and cooling of building and industrial processes.
Utilizing waste heat and providing additional heat to buildings and industrial processes without burning fossil fuels is continuing to grow in interest. However, this must be done with working fluids that are also environmentally friendly.
This work will investigate fluid properties, heat exchanger and compressor design, and system (high-temperature heat pump) testing and control for decarbonization efforts.
Utilizing waste heat and providing additional heat to buildings and industrial processes without burning fossil fuels is continuing to grow in interest. However, this must be done with working fluids that are also environmentally friendly.
This work will investigate fluid properties, heat exchanger and compressor design, and system (high-temperature heat pump) testing and control for decarbonization efforts.
Campus:
West Lafayette
Research categories:
Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Thermal Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
- Chemical Engineering
Desired experience:
Thermodynamics (required), Fluid Mechanics (desired), Heat and Mass Transfer (desired).
School/Dept.:
School of Mechanical Engineering
Professor:
Riley
Barta
More information: https://engineering.purdue.edu/BartaGroup/research
Antibiotic peptide encapsulation into nanoparticles for improved drug delivery
Description:
Biologics – peptide, protein, and nucleic acid therapeutics – are highly potent drug molecules that selectively interact with biological markers, thus reducing off-target side effects. Effective biologic therapies (1) reach the site of action intact and (2) sustain therapeutic concentrations at the site of action. Accomplishing these objectives is challenging due to potential degradation under physiological conditions, specifically pH, enzymes, and surfactants, which can limit therapeutic efficacy.
Processing biologics into nanoparticles (NPs) is an attractive approach to overcoming these delivery challenges, with demonstrated commercial success for mRNA in the COVID-19 lipid nanoparticle vaccines. The Ristroph Lab at Purdue University has developed an effective method for efficiently encapsulating peptides and proteins into polymeric NPs at large scales that relies on hydrophobic ion pairing (HIP). HIP is a solubility engineering technique that electrostatically complexes a charged hydrophilic molecule with a hydrophobic counterion to produce a water-insoluble salt that can then be encapsulated via Flash Nanoprecipitation (FNP). When this technique is used to encapsulate peptides and proteins, the biologic-counterion complex precipitated in NP cores forms liquid crystalline structures (LCs) that depend on the counterion and environmental pH. Previous work demonstrated different LC phases control peptide/protein release in vitro. The assembly kinetics and dynamics of LC structures in the cores of polymeric NPs and the design rules governing these different LC phases and, thus, peptide/protein release remain to be elucidated.
Sustained release NP formulations for proteins and peptides produced using this technique have several advantages over the delivery of unencapsulated biologics. First, controlled-release formulations increase the residence time of the biologic in the body, potentially decreasing the amount of injected biologic required to achieve a therapeutic effect. Additionally, the biocompatible block co-polymer shell can (1) protect the core material from harsh environmental conditions and (2) be modified to sustain further the release of the biologic from the core. The combination of HIP and FNP can uniquely enable the co-delivery of biologics and hydrophobic small-molecule drugs to have a synergistic effect. To capitalize on the advantages listed above and generate optimized formulations for biologic therapies, a deeper understanding of the relationship between HIP parameters, liquid crystal structure, and release of the biologic is required.
This project will focus primarily on quantifying the release rate of a model peptide antibiotic therapeutic from polymeric nanocarriers that have been measured for internal structural information already. The researcher will then work with a graduate mentor to develop correlations between drug release and internal structure.
Processing biologics into nanoparticles (NPs) is an attractive approach to overcoming these delivery challenges, with demonstrated commercial success for mRNA in the COVID-19 lipid nanoparticle vaccines. The Ristroph Lab at Purdue University has developed an effective method for efficiently encapsulating peptides and proteins into polymeric NPs at large scales that relies on hydrophobic ion pairing (HIP). HIP is a solubility engineering technique that electrostatically complexes a charged hydrophilic molecule with a hydrophobic counterion to produce a water-insoluble salt that can then be encapsulated via Flash Nanoprecipitation (FNP). When this technique is used to encapsulate peptides and proteins, the biologic-counterion complex precipitated in NP cores forms liquid crystalline structures (LCs) that depend on the counterion and environmental pH. Previous work demonstrated different LC phases control peptide/protein release in vitro. The assembly kinetics and dynamics of LC structures in the cores of polymeric NPs and the design rules governing these different LC phases and, thus, peptide/protein release remain to be elucidated.
Sustained release NP formulations for proteins and peptides produced using this technique have several advantages over the delivery of unencapsulated biologics. First, controlled-release formulations increase the residence time of the biologic in the body, potentially decreasing the amount of injected biologic required to achieve a therapeutic effect. Additionally, the biocompatible block co-polymer shell can (1) protect the core material from harsh environmental conditions and (2) be modified to sustain further the release of the biologic from the core. The combination of HIP and FNP can uniquely enable the co-delivery of biologics and hydrophobic small-molecule drugs to have a synergistic effect. To capitalize on the advantages listed above and generate optimized formulations for biologic therapies, a deeper understanding of the relationship between HIP parameters, liquid crystal structure, and release of the biologic is required.
This project will focus primarily on quantifying the release rate of a model peptide antibiotic therapeutic from polymeric nanocarriers that have been measured for internal structural information already. The researcher will then work with a graduate mentor to develop correlations between drug release and internal structure.
Campus:
West Lafayette
Research categories:
Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Kurt
Ristroph
More information: https://www.ristrophlab.com/
Applying two-dimensional materials for biosensing
Description:
The undergraduate students will fabricate atomic thin sensors composed of two-dimensional materials and perform bioimaging experiment using these next-gen sensors.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Medical Science and Technology, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Physics
Professor:
Yihang
Zeng
More information: https://sites.google.com/view/zenglab2024/home
Arc Welding Metal 3D Printing for Composite Tooling
Description:
The use of fiber-reinforced composites in various industries has significantly increased. In composites manufacturing, tooling plays a critical role in providing accurate shapes and maintaining dimensional precision. The rapid iteration of composite part designs poses challenges for traditional tooling fabrication methods, which often require months to years to complete. As a result, more advanced tooling fabrication technologies are essential.
In this study, additively manufactured composite tooling technology will be investigated. To enable large-scale tooling, an arc welding robot was implemented as an additive manufacturing system. The microstructure of the printed beads, thermal residual stresses, and shape deformations in the printed parts will be analyzed. The printed metal tool will undergo post-machining using a CNC milling machine to achieve dimensional accuracy and will subsequently be used as a tool in the fiber-reinforced composite part manufacturing process. The study will include establishing a robotic arc welding additive manufacturing platform, characterizing the printed material and conducting simulations, optimizing the print path, and demonstrating the composite part manufacturing process.
The project will be held in the Composite Manufacturing and Simulation Center (CMSC), an interdisciplinary group within the Purdue School of Engineering and Purdue Polytechnic Institute that leads in advanced composite science and technology. There is a well-developed training program for the knowledge and skills they need to learn. Additionally, the students will be closely supervised by the staff and graduate students to ensure effective teaching.
In this study, additively manufactured composite tooling technology will be investigated. To enable large-scale tooling, an arc welding robot was implemented as an additive manufacturing system. The microstructure of the printed beads, thermal residual stresses, and shape deformations in the printed parts will be analyzed. The printed metal tool will undergo post-machining using a CNC milling machine to achieve dimensional accuracy and will subsequently be used as a tool in the fiber-reinforced composite part manufacturing process. The study will include establishing a robotic arc welding additive manufacturing platform, characterizing the printed material and conducting simulations, optimizing the print path, and demonstrating the composite part manufacturing process.
The project will be held in the Composite Manufacturing and Simulation Center (CMSC), an interdisciplinary group within the Purdue School of Engineering and Purdue Polytechnic Institute that leads in advanced composite science and technology. There is a well-developed training program for the knowledge and skills they need to learn. Additionally, the students will be closely supervised by the staff and graduate students to ensure effective teaching.
Campus:
West Lafayette
Research categories:
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:
Preferred experience includes CAD, robotic control, 3D printing, material characterization, FEA simulation, CAM, and CNC machining.
School/Dept.:
School of Aviation and Transportation Technology
Professor:
Garam
Kim
More information: https://www.purdue.edu/cmsc/
Assessments of high-power microwave effects -- theory and experiment
Description:
High-power microwaves and related physics are important for national security and biomedical applications. This project consists of two potential parts:
1) Derive first principles theories and apply simulations to assess the behavior of electrons in a gap.
2) Perform experiments assessing the interactions of high-power microwave fields with biological cells.
1) Derive first principles theories and apply simulations to assess the behavior of electrons in a gap.
2) Perform experiments assessing the interactions of high-power microwave fields with biological cells.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Energy and Environment, Microelectronics
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
Desired experience:
Completion of mathematics through differential equations for theoretical work. Knowledge of cell culture and cell handling for the biological project.
School/Dept.:
School of Nuclear Engineering
Professor:
Allen
Garner
More information: https://sites.google.com/site/garnerresearchgroup/publications
Automation Methods for Gamma Ray Spectroscopy and Data Analysis
Description:
This summer research program offers undergraduate students the opportunity to enhance gamma-ray spectroscopy analysis using machine learning. Traditional methods struggle with complex data interpretation due to limited theoretical predictions. By integrating machine learning into Radware, a widely used spectroscopy analysis program, we aim to accelerate the identification of isotope level schemes and uncover transitions that may have been previously overlooked. The project involves training algorithms with synthetic spectral data generated through Monte Carlo techniques, ultimately improving the efficiency and accuracy of nuclear spectroscopy analysis.
Students will work in a programming and data-analysis intensive environment, contributing to the development of machine learning algorithms in Python and C. Their tasks include modifying Radware’s code, analyzing spectral data, and refining models for transition identification. By collaborating with experienced researchers, students will gain hands-on experience in scientific computing, data processing, and the application of Machine learning in experimental physics.
Students will work in a programming and data-analysis intensive environment, contributing to the development of machine learning algorithms in Python and C. Their tasks include modifying Radware’s code, analyzing spectral data, and refining models for transition identification. By collaborating with experienced researchers, students will gain hands-on experience in scientific computing, data processing, and the application of Machine learning in experimental physics.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Other
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
- Physics
- Computer Science
- Nuclear Engineering
Desired experience:
Ideal candidates for this research program should have coursework in physics, computer science, or a related field, with a strong foundation in programming. Experience with data analysis, machine learning, or Monte Carlo simulations is highly desirable. Familiarity with scientific computing, numerical methods, or nuclear physics concepts will be beneficial. Prior research experience, especially in computational modeling or spectroscopy, is a plus but not required. Strong analytical skills, problem-solving abilities, and the ability to work collaboratively in a research setting are essential.
School/Dept.:
Physics and Astronomy
Professor:
Andreas
Jung
More information: https://www.physics.purdue.edu/jung/
Battery Fire Safety
Description:
This research topic seeks to explore the reaction dyanamics involved in battery fires. This includes It will involve experimental design, experiments and analysis. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
Research categories: dynamic experimentation, batteries
Research categories: dynamic experimentation, batteries
Campus:
West Lafayette
Research categories:
Energy and Environment, 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://web.ics.purdue.edu/~sson/Son_Webpage/SonWeb_index.html
Bioinformatics analyses in organoid models
Description:
Project Description: This project focuses on bioinformatics analyses in organoid-based models, aiming to identify key biological pathways and interactions within engineered human tissues. The research integrates multi-omics datasets, such as transcriptomics, proteomics, and epigenomics, to uncover insights into organoid development, disease modeling, and therapeutic responses.
Student Role: The student will actively contribute to the project by performing bioinformatics analyses, including data preprocessing, statistical analysis, and visualization of multi-omics datasets. They will gain hands-on experience in using programming languages such as R for data analysis and identifying key biomarkers. The student will also collaborate with wet lab researchers to integrate bioinformatics findings with experimental data. Additional responsibilities include presenting findings in lab meetings and contributing to the preparation of research manuscripts.
Student Role: The student will actively contribute to the project by performing bioinformatics analyses, including data preprocessing, statistical analysis, and visualization of multi-omics datasets. They will gain hands-on experience in using programming languages such as R for data analysis and identifying key biomarkers. The student will also collaborate with wet lab researchers to integrate bioinformatics findings with experimental data. Additional responsibilities include presenting findings in lab meetings and contributing to the preparation of research manuscripts.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Biotechnology Data Insights, Cellular Biology, Genetics, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
- Computer Engineering
- Computer and Information Technology
- Computer Engineering Technology
- Computer Science
- Cell Molecular and Developmental Biology
- Biochemistry
Desired experience:
Proficiency in programming languages such as Python, R, or MATLAB, with experience in data analysis and visualization. Basic knowledge of cell biology, stem cells, or organoid models is a plus but not required. Excellent problem-solving skills, attention to detail, and the ability to work independently and collaboratively within a multidisciplinary team. Prior research experience involving computational biology or bioinformatics is a strong advantage.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Estelle
Park
More information: https://engineering.purdue.edu/PARKLAB/
Bioinspired origami aerial robotics
Description:
Transformation between locomotion modes in multimodal robots poses key design challenges to meet the growing demand for exploration in various environments. For example, design constraints involved for an aerial-aquatic robot must account for air and water maneuverability efficiently while minimizing the amount of energy expenditure. Recently, flying fish have gained increasing attention for their ability to switch locomotion modes. Flying fish wings are thin elastic membranes with no muscles present, rather the flexible structure is supported by stiffening rays. Other aerial robots have been based on insect wings with origami topologies.
This project aims to design, manufacture, and test a bioinspired deployable wing and its integration into an aerial-aquatic robotic architecture. A key outcome of this project is the trade-off exploration between origami-based membranes6 stiffness, deployment actuation, and weight.
This project aims to design, manufacture, and test a bioinspired deployable wing and its integration into an aerial-aquatic robotic architecture. A key outcome of this project is the trade-off exploration between origami-based membranes6 stiffness, deployment actuation, and weight.
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Fabrication and Robotics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Experience with either structures, aerodynamics, robotics, and fabrication
School/Dept.:
School of Mechanical Engineering
Professor:
Andres
Arrieta
More information: https://engineering.purdue.edu/ProgrammableStructures/
Bone Biomechanics Research
Description:
With this project we seek to advance understanding of bone biomechanics and to contribute to solving the major healthcare issue of osteoporosis. Students will work on one of the following topics
Whole bone strength
Bone finite element modeling
X ray scattering
Whole bone strength
Bone finite element modeling
X ray scattering
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Material Modeling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Thomas
Siegmund
Building Next-Generation Agricultural IoTs using Drones and LoRA
Description:
This project aims to leverage the integration of drones with IoT network infrastructure, especially Long Range (LoRa) to create next-generation agricultural IoT systems. LoRa technology provides long-range, low-power, and cost-effective solutions for scalable communication in rural and agricultural environments. Integrating drones offers enhanced mobility and flexibility that overcome the limitations of fixed gateways, providing dynamic deployment, extended coverage, and real-time data collection. This combination opens up opportunities for innovative communication methods and intelligent data-driven decision-making. The project’s outcome will be innovations in agricultural applications such as resource management, localization, and sensing. Furthermore, the integration of AI and ML can be applied in the following ways: optimizing network operations and communication protocols to enhance efficiency and reliability, and improving data processing from distributed IoT sensors to enable effective agricultural monitoring and informed decision-making.
Campus:
West Lafayette
Research categories:
Internet of Things (IoT), IoT for Precision Agriculture, Mobile Computing
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
- Computer Engineering
- Computer and Information Technology
Desired experience:
Prior experience with LoRa, Software Defined Radio, and Machine Learning.
School/Dept.:
Computer Science
Professor:
Chunyi
Peng
CISTAR - Chemical Process Design Using Physics-informed Machine Learning
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue. Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies. Participants must be US Citizens who attend other universities. Purdue students are not eligible.
This project endeavors to revolutionize the approach to chemical engineering design through the construction of surrogate models based on Physics-Informed Neural Networks (PINNs). Initially, the project will leverage Aspen simulations to train PINNs that encapsulate both data-driven insights and physical laws governing chemical processes. Subsequently, these PINN models will be translated into comprehensive optimization frameworks.
The research will then focus on applying these frameworks to refine and enhance process designs, achieving optimal configurations for efficiency, cost-effectiveness, and sustainability. The project’s ultimate goal is to validate the effectiveness of PINN-based optimization against traditional methods, thereby demonstrating its potential as a powerful tool in process engineering. Through this research, we aim to set a new standard for integrating machine learning in the chemical process design field.
Requirements: Some experience with programming in python, and the Aspen Plus simulation is required. Knowing machine learning, mathematical modeling, and optimization is a plus. Preferably UG in ChemE, IE, CS, MechE.
This project endeavors to revolutionize the approach to chemical engineering design through the construction of surrogate models based on Physics-Informed Neural Networks (PINNs). Initially, the project will leverage Aspen simulations to train PINNs that encapsulate both data-driven insights and physical laws governing chemical processes. Subsequently, these PINN models will be translated into comprehensive optimization frameworks.
The research will then focus on applying these frameworks to refine and enhance process designs, achieving optimal configurations for efficiency, cost-effectiveness, and sustainability. The project’s ultimate goal is to validate the effectiveness of PINN-based optimization against traditional methods, thereby demonstrating its potential as a powerful tool in process engineering. Through this research, we aim to set a new standard for integrating machine learning in the chemical process design field.
Requirements: Some experience with programming in python, and the Aspen Plus simulation is required. Knowing machine learning, mathematical modeling, and optimization is a plus. Preferably UG in ChemE, IE, CS, MechE.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Chemical Unit Operations, Energy and Environment, Material Modeling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Computer Science
- Chemical Engineering
- Industrial Engineering
- Mechanical Engineering
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Can
Li
CISTAR - Decarbonization of the High-Carbon Intensive and High-Volume Commodity Chemicals Production through Renewable Electrification
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue. Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Electrification of industrial processes is being frequently mentioned as an option to reduce greenhouse gas emissions from energy-intensive industries. Electricity is a versatile energy carrier which presents a variety of electrification options. The increasing availability of cheap renewable electricity provides an opportunity to decarbonize energy intensive processes. As part of this decarbonization effort, the commodity chemical industry is an important target due to its large energy requirements and greenhouse gas emissions. One potential paradigm for electrification involves replacing the use of steam, generated by burning fossil fuels, as a source of heat in chemical processes to processes with direct electrical heating using renewable energy sources. This project aims to identify and quantify areas where energy is currently transferred by steam can be efficiently transferred by renewable electrification. The target commodity chemicals are ammonia, ethylene, propylene, and methanol.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens.
More information: https://cistar.us/
Electrification of industrial processes is being frequently mentioned as an option to reduce greenhouse gas emissions from energy-intensive industries. Electricity is a versatile energy carrier which presents a variety of electrification options. The increasing availability of cheap renewable electricity provides an opportunity to decarbonize energy intensive processes. As part of this decarbonization effort, the commodity chemical industry is an important target due to its large energy requirements and greenhouse gas emissions. One potential paradigm for electrification involves replacing the use of steam, generated by burning fossil fuels, as a source of heat in chemical processes to processes with direct electrical heating using renewable energy sources. This project aims to identify and quantify areas where energy is currently transferred by steam can be efficiently transferred by renewable electrification. The target commodity chemicals are ammonia, ethylene, propylene, and methanol.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens.
More information: https://cistar.us/
Campus:
West Lafayette
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Energy and Environment
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemical Engineering
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Cornelius
Masuku
More information: https://cistar.us/ewd/undergrad_overview/research-experience-for-undergraduates-reu-program
CISTAR - Examining how residual H2O (from CO2 conversion chemistry) influences methanol upgrading processes in zeolite catalysts
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue. Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies. Participants must be US Citizens who attend other universities. Purdue students are not eligible.
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.
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.
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:
David
Hibbitts
More information: https://cistar.us/
CISTAR - Gaining insights into polymer upcycling reactions using through model gas alkane hydrogenolysis
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue. Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies. Participants must be US Citizens who attend other universities. Purdue students are not eligible.
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.
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.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemical Engineering
School/Dept.:
Davidson School of Chemical Engineering
Professor:
David
Hibbitts
More information: https://cistar.us/
CISTAR - High temperature catalysts for conversion of ethylene and propylene to gasoline and diesel fuel
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
CISTAR's vision is to convert natural gas liquids, for example, ethane and propane, to fuels and chemicals by two catalytic steps. The first requires dehydrogenation of alkanes to olefins, which are subsequently converted to final products. This project investigates a new class of catalyst for conversion of ethylene and propylene to higher molecular weight hydrocarbons suitable for blending into gasoline or diesel fuels. These reactions occur at high temperature and pressure in a fixed bed reactor. The research plan is to synthesize catalysts and test these to determine the rates, selectivity and stability.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens.
CISTAR's vision is to convert natural gas liquids, for example, ethane and propane, to fuels and chemicals by two catalytic steps. The first requires dehydrogenation of alkanes to olefins, which are subsequently converted to final products. This project investigates a new class of catalyst for conversion of ethylene and propylene to higher molecular weight hydrocarbons suitable for blending into gasoline or diesel fuels. These reactions occur at high temperature and pressure in a fixed bed reactor. The research plan is to synthesize catalysts and test these to determine the rates, selectivity and stability.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens.
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:
Jeff
Miller
More information: https://cistar.us/
CISTAR - Modeling diffusion-limited reactions in zeolite crystals to advance crystallite development
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue. Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies. Participants must be US Citizens who attend other universities. Purdue students are not eligible.
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.
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.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Chemical Engineering
School/Dept.:
Davidson School of Chemical Engineering
Professor:
David
Hibbitts
More information: https://cistar.us/
CISTAR - Synthesis of tailored carbon supports for non-oxidative methane conversion
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
Methane is the major component of shale gas, and more research is needed to develop non-oxidative conversion routes to higher olefins and aromatics. Carbon-based catalysts have been shown to be effective at non-oxidative methane conversion to these products, but the active sites and reaction mechanisms remain unclear. This project will work on developing synthesis methods to alter the surface areas and active sites in porous carbon materials for methane conversion, and studying their catalytic reactivity and selectivity.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens.
Methane is the major component of shale gas, and more research is needed to develop non-oxidative conversion routes to higher olefins and aromatics. Carbon-based catalysts have been shown to be effective at non-oxidative methane conversion to these products, but the active sites and reaction mechanisms remain unclear. This project will work on developing synthesis methods to alter the surface areas and active sites in porous carbon materials for methane conversion, and studying their catalytic reactivity and selectivity.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens.
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://cistar.us/
CISTAR - Synthesis of zeolite catalysts with tailored diffusion properties
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
Olefin oligomerization is a key step in shale 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. 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.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens.
Olefin oligomerization is a key step in shale 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. 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.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens.
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://cistar.us/
Canvas3D + : Empowering Interaction of GenAI with User for Instruction Authoring in XR
Description:
Canvas3D for Instruction Authoring with Assemblify
While Generative AI has made image and animation generation more accessible, it still struggles with precise spatial reasoning—critical for creating instructional content. To address this, we introduce Canvas3D for Assemblify: an interactive 3D system that enables users to create, manipulate, and sequence parts and actions with fine-grained spatial control for instruction authoring.
Using the Assemblify graph as a semantic backbone, Canvas3D transforms user prompts into interactive 3D assemblies where users can directly position, align, and animate components. These spatial configurations form the basis for step-by-step instructional sequences, guiding generative models to produce consistent verbal, visual, and animated outputs.
While Generative AI has made image and animation generation more accessible, it still struggles with precise spatial reasoning—critical for creating instructional content. To address this, we introduce Canvas3D for Assemblify: an interactive 3D system that enables users to create, manipulate, and sequence parts and actions with fine-grained spatial control for instruction authoring.
Using the Assemblify graph as a semantic backbone, Canvas3D transforms user prompts into interactive 3D assemblies where users can directly position, align, and animate components. These spatial configurations form the basis for step-by-step instructional sequences, guiding generative models to produce consistent verbal, visual, and animated outputs.
Campus:
West Lafayette
Research categories:
Human Factors
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- computer
Desired experience:
Research in 3D content and GenAI tools.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Karthik
Ramani
More information: https://engineering.purdue.edu/cdesign/wp/
Cell-material interactions in synthetic hydrogels
Description:
This project will involve the characterization of hydrogel scaffolds that are designed to direct cell motility. The student will be responsible for collection of data in the laboratory and analysis of data. They will also meet with the PI and be responsible for communicating their work during these meetings.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Kelly
Schultz
More information: schultzlab.io
Cellulose Cement Composite (C3) for Carbon Negative Construction
Description:
C3, a cellulose cement composite, is being developed for use in residential and light commercial construction as an alternative to dimensional lumber and sheet products (e.g., plywood/drywall). C3 consists of cellulose excelsior (CE), a wood fiber materials, and low-carbon cement binders. C3 will serve as a sink of carbon dioxide from the atmosphere because of its carbonatable cementitious content. C3 will be durable and resistant to rot and fungal growth, be fire resistant, have thermal resistance (R-values) that limit thermal bridging, and be quickly implemented due to compatibility with current construction processes. This project will involve research to understand and further develop CE-binder interactions by chemical modification of the interface, characterization and mechanical testing.
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Ecology and Sustainability, Engineering the Built Environment, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Materials Engineering
- Civil Engineering
- Chemical Engineering
- Mechanical Engineering
- Chemistry
Desired experience:
desire for hard but safe work and trying sequester carbon.
School/Dept.:
School of Materials Engineering
Professor:
Jeffrey
Youngblood
Characterization of Surface Shear Stress in Pulsatile Flows
Description:
The SURF student will join a research team with Purdue professors and graduate students to study the evolution of surface shear stress at different phases in a cycle of pulsatile flows. He/she will work on setting up the experiment (test rig, lasers, cameras, controls), take data, and analyze the data. The project will bring in a new understanding of cardiovascular flows and serve as a good opportunity for the student to understand the advanced flow diagnostics technologies.
Campus:
West Lafayette
Research categories:
Fluid Modelling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
Desired experience:
Mechanical design (CAD), prototyping development (basic machine/fabrication skills), data analysis (Python or Matlab), strong hands-on skills, good team spirit, good GPA.
School/Dept.:
School of Mechanical Engineering
Professor:
Jun
Chen
Characterizing Infant Exposure to Chemical Contaminants in Indoor Dust
Description:
Our project is funded by the U.S. Environmental Protection Agency (EPA) and involves an interdisciplinary collaboration between engineers, chemists, and psychologists at Purdue University and New York University (NYU). We will elucidate determinants of indoor dust ingestion in 6- to 24-month-old infants (age range for major postural and locomotor milestones). Specific objectives are to test: (1) whether the frequency and characteristics of indoor dust and non-dust mouthing events change with age and motor development stage for different micro-environments; (2) how home characteristics and demographic factors affect indoor dust mass loading and dust toxicant concentration; (3) how dust transfer between surfaces is influenced by dust properties, surface features, and contact dynamics; and (4) contributions of developmental, behavioral, and socio-environmental factors to dust and toxicant-resolved dust ingestion rates. In addition, the project will (5) create a shared corpus of video, dust, toxicant, and ingestion rate data to increase scientific transparency and speed progress through data reuse by the broader exposure science community.
Our transdisciplinary work will involve: (1) parent report questionnaires and detailed video coding of home observations of infant mouthing and hand-to-floor/object behaviors; (2) physical and chemical analyses of indoor dust collected through home visits and a citizen-science campaign; (3) surface-to-surface dust transfer experiments with a robotic platform; (4) dust mass balance modeling to determine distributions in and determinants of dust and toxicant-resolved dust ingestion rates; and (5) open sharing of curated research videos and processed data in the Databrary digital library and a public website with geographic and behavioral information for participating families.
The project will provide improved estimates of indoor dust ingestion rates in pre-sitting to independently walking infants and characterize inter-individual variability based on infant age, developmental stage, home environment, and parent behaviors. Dust transport experiments and modeling will provide new mechanistic insights into the factors that affect the migration of dust from the floor to mouthed objects to an infant’s mouth. The shared corpus will enable data reuse to inform future research on how dust ingestion contributes to infants’ total exposure to environmental toxicants.
U.S. EPA project overview: https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract_id/11194
Our transdisciplinary work will involve: (1) parent report questionnaires and detailed video coding of home observations of infant mouthing and hand-to-floor/object behaviors; (2) physical and chemical analyses of indoor dust collected through home visits and a citizen-science campaign; (3) surface-to-surface dust transfer experiments with a robotic platform; (4) dust mass balance modeling to determine distributions in and determinants of dust and toxicant-resolved dust ingestion rates; and (5) open sharing of curated research videos and processed data in the Databrary digital library and a public website with geographic and behavioral information for participating families.
The project will provide improved estimates of indoor dust ingestion rates in pre-sitting to independently walking infants and characterize inter-individual variability based on infant age, developmental stage, home environment, and parent behaviors. Dust transport experiments and modeling will provide new mechanistic insights into the factors that affect the migration of dust from the floor to mouthed objects to an infant’s mouth. The shared corpus will enable data reuse to inform future research on how dust ingestion contributes to infants’ total exposure to environmental toxicants.
U.S. EPA project overview: https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract_id/11194
Campus:
West Lafayette
Research categories:
Energy and Environment, Environmental Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
We are seeking students passionate about studying environmental contaminants and infant exposure to chemicals in the indoor environment. Preferred skills: experience with MATLAB, Python, or R. Coursework: environmental science and chemistry, microbiology, physics, thermodynamics, heat/mass transfer, fluid mechanics, developmental psychology.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Brandon
Boor
Characterizing Proteoform-Drug Interactions by Reactivity-Driven Top-down Mass Spectrometry
Description:
Understanding the molecular recognition that occurs during drug-protein binding is a key step in drug design and development. Structural biology approaches such as protein crystallography and cryo-EM provide critical information that serve the basis of structure-based drug design. However, protein structure is dynamic and undergo myriad post-translational modifications that regulate their subcellular location, conformation, and reactivity. These alternative post-translationally modified forms of proteins (proteoforms) can differentially engage small-molecule drugs and can be the underlying basis of resistance or sensitivity. We seek to use reactivity-based top-down mass spectrometry to identify these proteoform-dependent interactions. SURF trainees have opportunities to conduct probe chemical synthesis, mammalian cell culture experiments, biochemical characterization, and protein mass spectrometry – depending on the research interests of the student.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology, Chemical Catalysis and Synthesis
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Introductory organic chemistry
School/Dept.:
Chemistry
Professor:
Bryon
Drown
More information: https://drownlab.com/
Classification of Triangular and other frustrated lattice materials for quantum computing studies
Description:
Frustrated magnets are difficult to understand because of a high degree of quantum coherence. In this project, we search for chiral magnets and chiral quantum spin liquids, which are highly sought after for their potential applications in quantum sensing technologies. The project involves the thermal and magnetic characterization of quantum magnets which are grown in our laboratory, and attempting to understand their properties - whether their properties are amenable to be understood using the current state of quantum computers offered by Rydberg atoms (QuEra) and Fluxmons (D-Wave)
The project has a strong component of chemical and bulk analysis and the student has to learn to perform stringent bulk measurements using PPMS, MPMS, Raman and other low-temperature tools. Besides he/she will work with the other students working with quantum computing in the group to ensure that we get a consistent result which helps us validate quantum computing results with real materials and vice-versa.
The project has a strong component of chemical and bulk analysis and the student has to learn to perform stringent bulk measurements using PPMS, MPMS, Raman and other low-temperature tools. Besides he/she will work with the other students working with quantum computing in the group to ensure that we get a consistent result which helps us validate quantum computing results with real materials and vice-versa.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
The person should have prior laboratory experience for magnetometry measurements.
School/Dept.:
Physics and Astronomy
Professor:
Arnab
Banerjee
Co-designing Student VR Experiences for Geology Course Fieldtrips
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. To accomplish this, we will complete a co-design process with instructors and previous students of EAPS 353. For this undergraduate research, the student will have the opportunity to pilot and facilitate co-design workshop activities, analyze co-design results, and inform the VR design. If the student is interested, the student can also continue in the Fall term to facilitate the VR implementation with the students and instructors.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Earth, Atmospheric and Planetary Sciences
Professor:
Cauê
Borlina
CogniPilot Autopilot Development
Description:
The student will help develop and refine a Kalman Filter for the CogniPilot autopilot system. This will require integration of IMU(gyro/ acceleorometer), magnetometer, and GPS data (if time allows). The system will be prototyped and developed inside the Purdue UAS Research and Test Facility.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Computer Architecture, Cybersecurity, Deep Learning, Fluid Modelling and Simulation, Microelectronics, Mobile Computing, System-on-a-Chip, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Aeronautical and Astronautical Engineering
- Computer Science
- Mathematics
- Computer Engineering
Desired experience:
C/ Python programming, estimation and control courses are a benefit
School/Dept.:
School of Aeronautics and Astronautics
Professor:
James
Goppert
More information: engineering.purdue.edu/PURT
Colloidal stability of double transition metal MXenes
Description:
The goal of this project is to test the colloidal stability of DTM MXenes and identify the
characteristics of degradation over time. The colloidal stability of this sub-family of nano materials is directly related to its practicality in various real-world applications. Work by the student will include pre-cursor and nanomaterial synthesis, in addition to data collection and analysis using a UV-vis NIR spectrophotometer.
characteristics of degradation over time. The colloidal stability of this sub-family of nano materials is directly related to its practicality in various real-world applications. Work by the student will include pre-cursor and nanomaterial synthesis, in addition to data collection and analysis using a UV-vis NIR spectrophotometer.
Campus:
Indianapolis
Research categories:
Material Processing and Characterization, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Babak
Anasori
More information: https://www.babakanasori.com
Comparative neurotoxicity of PFAS chemicals
Description:
We wukk 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. We will use derived human dopaminergic neuronal cells to compare the neurotoxicity of selected PFAS and identify key pathogenic pathways driving the neurodegeneration. Participating students will learn to differentiate dopaminergic neurons from hiPSC, perform biological characterizations and functional assessments.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Cellular Biology, Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Prior experience with mammalian cell culture preferred.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Chongli
Yuan
More information: https://cyuangroup.com/
Comparing 2-D and 3-D AI models for molecular property predictions
Description:
SMILES strings only contain a 2-D representation of the atomic connectivity (excluding unique implementations that implement 3-D information). Models built on graphs generated from this representation therefore do not contain 3-D information and have shown poor accuracy for capturing properties that are inherently dependent on 3-D structure (i.e., dipole moment). Graph-based ML algorithms exist for acting on 3-D coordinates of molecules and datasets like QM9 have SDF files readily available with full 3-D coordinates for molecular structures. Also, RDKit has generalized force fields that can be used to give a rough estimate of 3-D atomic coordinates for datasets that do not have 3-D structure already.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Energy and Environment, Material Modeling and Simulation
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Intro programming, physics, and chemistry and a willingness to learn more are required.
School/Dept.:
School of Materials Engineering
Professor:
Alejandro
Strachan
More information: https://www.strachanlab.org
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
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biology
- Biomedical Engineering
- Biological Engineering - multiple concentrations
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Taeyoon
Kim
More information: https://engineering.purdue.edu/mct
Computational modeling to assess spatial calcium signaling patterns and mechanisms in plant defense.
Description:
The undergraduate researcher would work on developing and refining a partial differential equation model that describes calcium induced calcium responses in plant tissues in response to pathogen signals.
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Partial differential equations.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Elsje
Pienaar
Confined Direct Two-phase Jet Impingement Cooling with Topology Optimized Surface Engineering and Phase Separation Using Additive Manufacturing
Description:
Advanced semiconductor packaging is playing a crucial role in enhancing system performance and functionality. As computing demands continue to rise, particularly in emerging technologies, heterogeneous three-dimensional (3D) integration with fine-pitch, high-density interconnections, and multi-chip stacks offers significant promise for the future. 3D metal interconnects, such as through-silicon vias (TSVs), through-glass vias (TGVs), hybrid bonding and micro bumps, have enabled the development of several generations of high-bandwidth memory (HBM), which is critical for high-end computation applications, including graphics accelerators, network devices, datacenter AI ASICs, and FPGAs.For future memory-on-logic and logic-on-logic 3D integration systems, innovative semiconductor metal interconnect technologies will be essential to achieve ultra-high 3D interconnect densities ranging from 1E+6/mm² to 1E+8/mm². However, aggressive scaling of interconnect pitches and the use of nanoscale via interconnections present significant challenges in terms of process development and reliability. Furthermore, these high-density 3D integration systems lead to substantial increases in heat flux and power density (W/cm³), which generate thermal crosstalk and hotspots, causing non-uniform temperature distributions. High-performance, energy-efficient thermal management solutions are needed to tackle this thermal challenge.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Energy and Environment, Heterogeneous Integration, Thermal Technology
Citizenship requirements:
No citizenship requirements, U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Tiwei
Wei
More information: https://s-pack.org/?_ga=2.134222001.1552090868.1738072977-1791249841.1736579698
Constructing Spacecraft Trajectories in the Earth-Moon Region
Description:
The project is focused upon a network of spacecraft trajectories throughout Earth-Moon space. The methodology is delivering software to introduce trajectories the are initially constructed in a basic gravitational model and shift them to a model that represents the actual solar system dynamics for actual flight. The student would be aiding in producing the pathways and then testing the software to deliver the appropriate trajectories for flight.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Aerospace Engineering
Desired experience:
Some dynamics courses already completed. Experience with matlab is beneficial. Some familiarity with visualization to deliver images.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Kathleen
Howell
Control of networked spreading processes
Description:
This project will study how to model, analyze and control dynamical processes spreading on networks. The student will leverage control theory, optimization, and machine learning to identify ways to choose optimal parameters to control such processes, and report on their findings.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Solid aptitude for math, background/experience with control theory, programming (MATLAB, Python), ability to work both independently and as part of a team.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Shreyas
Sundaram
Conversion of alcohol and carboxylic acids building blocks to aldehydes for DNA-encoded chemical library synthesis
Description:
The project will involve the construction of libraries of drug-like compounds in DNA-encoded chemical libraries. The component for the student will be to focus on particular chemistry to expand the number and utility of building blocks to include in library synthetic schemes. The student will focus on the reductive alkylation of amine-containing DNA-linked compounds with aldehydes. Due to the limited stability of aldehydes, they are expensive compounds, and few are commercially available. The student will work to optimize chemistry to convert alcohol and carboxylic acid building blocks to aldehydes. Alcohol and carboxylic acid building blocks are very cheap and abundant. They will then screen large collections of these compounds for on-DNA reaction efficiency.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Students should have taken introductory chemistry. Organic chemistry would also be helpful but not required. No prior lab experience is necessary.
School/Dept.:
Department of Medicinal Chemistry and Molecular Pharmacology
Professor:
Casey
Krusemark
Could lightning have magnetized the surface of Mars
Description:
The Martian surface is characterized by the presence of a strongly magnetized crust, albeit Mars currently does not have a dynamo. One of the main hypotheses for describing such magnetization is that the crust was magnetized when the Martian dynamo was active, likely between 4.5-3.7 billion years (Ga) ago, with any crust that formed after this time recording no Martian dynamo. This is further supported by the presence of strong magnetization in the ancient (~4 Ga old) southern hemisphere of Mars in contrast with the weakly magnetized and younger northern hemisphere. This hypothesis depends on Mars having a short-lived dynamo, a consideration that has been contested based on core evolution models. Thus, the mechanism that partially magnetized the Martian crust remains undetermined. In this project, we propose to investigate the hypothesis that lightning on the surface of Mars could have magnetized regions of the planet. This hypothesis depends on lightning striking the surface of Mars and increasing the remanent magnetization of rocks. This hypothesis will be explored by the student in the project by integrating models of lightning discharge along with models of remanent magnetization and atmospheric lightning over the history of Mars. While this project is directed towards Mars, there are several other implications for determining the potential of lightning magnetizing terrestrial planets. For instance, the Moon also contains magnetic anomalies that seem to be localized to specific regions. If we show that lightning-induced magnetization could explain the Martian crust, a similar explanation could be applied to the Moon and motivate future studies.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Earth, Atmospheric and Planetary Sciences
Professor:
Cauê
Borlina
DNA Tetrahedra Cellular Uptake for In Vitro Drug Delivery Study
Description:
One of the challenges posed by miRNA delivery is transfusing the oligos into the cells. Nanomaterials have been proven to decorate cells efficiently via DNA and protein aptamers, increasing the local drug concentration at the cellular membrane. A novel method of delivering miRNA via DNA-cholesterol anchoring improved the delivery efficiency in vitro in HeLa and HAT pathogenic cells.
Recently, we have designed multiple DNA tetrahedra for miRNA drug delivery and would like to see if cationic lipids or polymers would change the cellular uptake mechanism or dynamics. In essense, the student would decorate the DNA tetrahedra with cationic lipids or polymers and characterize the uptake of the structures and / or miRNA into the cells. Simultaneously, we hope that the student can work with the mentor to explain the uptake mechanisms qualitatively.
The student will be trained in tissue cell culturing and confocal microscopy.
Recently, we have designed multiple DNA tetrahedra for miRNA drug delivery and would like to see if cationic lipids or polymers would change the cellular uptake mechanism or dynamics. In essense, the student would decorate the DNA tetrahedra with cationic lipids or polymers and characterize the uptake of the structures and / or miRNA into the cells. Simultaneously, we hope that the student can work with the mentor to explain the uptake mechanisms qualitatively.
The student will be trained in tissue cell culturing and confocal microscopy.
Campus:
West Lafayette
Research categories:
Medical Science and Technology, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biology
- Biochemistry
- Chemistry
- Biomedical Engineering
- Biological Engineering - multiple concentrations
- Agricultural Engineering
- Cell Molecular and Developmental Biology
- Chemical Engineering
Desired experience:
Prior experience with cell or bacteria culturing is welcome, but no prior experience in the lab is necessary.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Tamara
Kinzer-Ursem
Data Pipeline Engineering and Integration with models
Description:
Digital Agriculture, at its best, builds upon decades of discipline research with some integration of new IoT sensors and communication pathways as well as public resource data such as weather, soil, and topography. One challenge to be addressed is to more fully document the backstory or fuller context of situations so that artificial intelligence and machine learning can be more complete and robust. Another is the integration of mechanistic (descriptive of the fundamental science) models that might be biological, physical, chemical, logistical, economic, etc. in origin. The better parameterization of these models and even auto-population of initial conditions can stem from data sets and data streams. In this project, the student will assist in the work to “serve up/present” and curate as much data as we possibly can for part of the cropping system testbed. This data will then meld with biophysical model(s). It will require interoperability focus and that involves wise choices of data architecture and an integration with data pipelines (often based on open source tools). The end game is to provide better insight (including probabilities, when applicable) for tactical and strategic cropping decisions while preserving security and privacy.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Simulation and Technology, Internet of Things (IoT), IoT for Precision Agriculture
Citizenship requirements:
U.S. Citizen
Desired experience:
Genuine interest in agricultural production systems. Familiarity or willingness to learn coding in Python.
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Dennis
Buckmaster
More information: https://iot4ag.us
Data Pipeline Engineering and Integration with models
Description:
Digital Agriculture, at its best, builds upon decades of discipline research with some integration of new IoT sensors and communication pathways as well as public resource data such as weather, soil, and topography. One challenge to be addressed is to more fully document the backstory or fuller context of situations so that artificial intelligence and machine learning can be more complete and robust. Another is the integration of mechanistic (descriptive of the fundamental science) models that might be biological, physical, chemical, logistical, economic, etc. in origin. The better parameterization of these models and even auto-population of initial conditions can stem from data sets and data streams. In this project, the student will assist in the work to “serve up/present” and curate as much data as we possibly can for part of the cropping system testbed. This data will then meld with biophysical model(s). It will require interoperability focus and that involves wise choices of data architecture and an integration with data pipelines (often based on open source tools). The end game is to provide better insight (including probabilities, when applicable) for tactical and strategic cropping decisions while preserving security and privacy.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Simulation and Technology, Internet of Things (IoT), IoT for Precision Agriculture
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
Desired experience:
Genuine interest in agricultural production systems. Familiarity or willingness to learn coding in Python.
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Dennis
Buckmaster
More information: https://iot4ag.us
Data-Driven Physical Law Discovery
Description:
In this project, the student will work closely with the research mentor to learn interpretable machine learning and how to conduct data-driven physical law discovery.
Discovering governing physical laws from noisy data is a grand challenge in many science and engineering research areas. We present a new approach to data-driven discovery of ordinary differential equations (ODEs) and partial differential equations (PDEs), in explicit or implicit form. The student will develop data-driven methods on a wide range of problems, including shallow water equations and Navier–Stokes equations. The key idea is to select candidate terms for the underlying equations using dimensional analysis, and to approximate the weights of the terms with error bars using our threshold sparse Bayesian regression.
Discovering governing physical laws from noisy data is a grand challenge in many science and engineering research areas. We present a new approach to data-driven discovery of ordinary differential equations (ODEs) and partial differential equations (PDEs), in explicit or implicit form. The student will develop data-driven methods on a wide range of problems, including shallow water equations and Navier–Stokes equations. The key idea is to select candidate terms for the underlying equations using dimensional analysis, and to approximate the weights of the terms with error bars using our threshold sparse Bayesian regression.
Campus:
West Lafayette
Research categories:
Deep Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Familiar with machine learning, Python programming and ODE, linear algebra.
School/Dept.:
School of Mechanical Engineering
Professor:
Guang
Lin
Degradable hydrogels for tissue engineering
Description:
Hydrogels are a class of soft, hydrated, and elastic materials made up of hydrophilic polymer chains that can retain large amounts of water or biological fluids. Biodegradable biomaterials have numerous applications in biomedical engineering, such as in tissue regeneration and drug delivery. Hydrogel polymers can be designed with various chemistries to achieve variable modes and rates of degradation. Degradable hydrogels allow them to mimic the conditions cells are exposed to in its natural tissue. Non-degradable hydrogels can hinder tissue regeneration by restricting cell infiltration and proliferation into the polymer matrix. The need for controlled changes in mechanical properties of hydrogels over time, and controlled release of encapsulated molecules for drug release, further highlights the need for such biodegradable materials.
In this project, the SURF student will fabricate and characterize hydrolytically degradable hydrogels by using existing and new approaches for the chemical modification of hyaluronic acid biopolymer. The student will be trained in techniques related to hyaluronic acid synthesis, fabrication, processing, characterization, and application in cell culture. The SURF student will gain hands-on experience with various instruments and approaches used in the lab, including microscopy, microfluidics, quantitative analysis, and mechanical characterization of the biomaterials.
In this project, the SURF student will fabricate and characterize hydrolytically degradable hydrogels by using existing and new approaches for the chemical modification of hyaluronic acid biopolymer. The student will be trained in techniques related to hyaluronic acid synthesis, fabrication, processing, characterization, and application in cell culture. The SURF student will gain hands-on experience with various instruments and approaches used in the lab, including microscopy, microfluidics, quantitative analysis, and mechanical characterization of the biomaterials.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Cellular Biology, Material Processing and Characterization, Medical Science and Technology
Citizenship requirements:
No citizenship requirements, U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Biological Engineering - multiple concentrations
- Biomedical Engineering
Desired experience:
Prior experience with fabricating and characterizing biomaterials is a must. Experience in cell culture, ELISA assays, and immunohistochemistry is a big plus.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Taimoor
Qazi
Design and Development an Autonomous System for a Wearable Device Assembly
Description:
This project aims to develop an autonomous assembly system that can assemble wearable devices automatically. The wearable devices include delicate components (such as glasses) and rigid materials (such as metals). The delicate components must be protected from breaking, scratching, twisting, during the assembly process where contact force must be applied to complete the assembly process. The assembly systems may be driven by a few servo motors with precise control and coordination.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Industrial Engineering
Professor:
Yu
She
More information: https://www.purduemars.com/
Design and Optimization of Residential HVAC systems
Description:
We are looking for an undergraduate researcher to help with a department of energy (DOE) funded project that aims to implement a cold-climate heat pump as one of the prime movers within a residential heating ventilation and air conditioning (HVAC) system. When compared to gas furnaces and electrical resistive heating, a heat pump has the potential to provide the same amount of heating, but with fewer carbon emissions/electricity. The goal is to develop a system sizing tool and a control algorithm to design and control the proposed system. This 3-year project officially started January 2025 and will culminate in a demonstration of the work.
Depending on interest and background, you can expect to contribute to one or more of the following tasks:
1. Construct models and controllers of the 24 different system operating modes.
2. Write algorithms that efficiently check safety properties of the closed-loop HVAC system.
3. Develop heuristic logic that can correctly pick one of the 24 operating modes in real time.
4. Develop benchmark models of cost, energy and carbon emission savings of the proposed system.
5. Aid in software development of component and system level simulation and design.
All tasks will be conducted by programming in python, and all software is version controlled via github. You will be expected to become proficient with both tools, and in general to write simple and elegant code.
What you will learn: Algorithms, Logic, Robust Control, Divergent Thinking
Depending on interest and background, you can expect to contribute to one or more of the following tasks:
1. Construct models and controllers of the 24 different system operating modes.
2. Write algorithms that efficiently check safety properties of the closed-loop HVAC system.
3. Develop heuristic logic that can correctly pick one of the 24 operating modes in real time.
4. Develop benchmark models of cost, energy and carbon emission savings of the proposed system.
5. Aid in software development of component and system level simulation and design.
All tasks will be conducted by programming in python, and all software is version controlled via github. You will be expected to become proficient with both tools, and in general to write simple and elegant code.
What you will learn: Algorithms, Logic, Robust Control, Divergent Thinking
Campus:
West Lafayette
Research categories:
Energy and Environment, Engineering the Built Environment, Thermal Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
Desired experience:
Proficiency in Python programming.
School/Dept.:
School of Mechanical Engineering
Professor:
Neera
Jain
More information: https://engineering.purdue.edu/JainResearchLab/
Design and Testing of an Automated Vial Handling System for Use with a New Method for Evaluating Freeze-Dried Product Quality
Description:
Our lab has designed a new method for non-destructively analyzing freeze-dried product quality based on solid-state Nuclear Magnetic Resonance spectroscopy. We would like to create an automated system for moving a batch of vials from a tray, into the analysis location, and back onto a belt that carries to a holding location.
The primary goals of the research project are to:
- Design an automated system for vial movement
- Develop a code based in arduino or python (Raspberry Pi) for controlling the system
- Assemble, test, and validate the system
- Run and evaluate samples with ssNMR spectroscopy using the automated system to review with PI and Mentors
The researcher will work with a Post-Doctoral Research Associate to create and implement the design. The researcher will learn how to operate the probe and analyze data from the ssNMR spectrometer to develop and understanding of the system as a whole and in order to create an intuitive automated system.
The primary goals of the research project are to:
- Design an automated system for vial movement
- Develop a code based in arduino or python (Raspberry Pi) for controlling the system
- Assemble, test, and validate the system
- Run and evaluate samples with ssNMR spectroscopy using the automated system to review with PI and Mentors
The researcher will work with a Post-Doctoral Research Associate to create and implement the design. The researcher will learn how to operate the probe and analyze data from the ssNMR spectrometer to develop and understanding of the system as a whole and in order to create an intuitive automated system.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics, Material Processing and Characterization, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Engineering
- Electrical Engineering
- Mechanical Engineering
Desired experience:
Arduino and/or Python controller programming
Experience with assembling electrical systems
Experience with 3D modeling and printing is helpful
School/Dept.:
Department of Industrial and Physical Pharmacy
Professor:
Eric
Munson
Design of a Mobile Robot Deployment System
Description:
The goal of this project is to design and prototype and mobile robot deployment system for transporting a team of agricultural robots to a field of interest. It must also house a charging station for the robots and serve as a communication base station for the team. The platform must be designed to be integrated with an autonomous Polaris Ranger utility task vehicle (UTV). The fleet of robots must be secure when being transported to the field of interest and then be automatically deployed once there. This project will require the mechanical design of the deployment system, mechatronic system design for the charging station and communication system, and integration and interfacing with the Polaris Ranger UTV. Field tests will be conducted at the Purdue University Agronomy Center for Research and Education (ACRE) facility.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics, IoT for Precision Agriculture
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Mechanical Engineering
- Electrical Engineering
- Computer Engineering
School/Dept.:
School of Mechanical Engineering
Professor:
David
Cappelleri
More information: https://iot4ag.us/
Design of an IoT4Ag Robotic Sensor Deployment System
Description:
The goal of this project is to design an IoT4Ag sensor deployment system for autonomous agricultural ground robots. The IoT sensors need to be spread about the field and are required to be inserted into the soil at a depth of approximately 6” deep. Thus, the developed sensor deployment system should be able to 1. Store the sensors that need to be deployed; 2. Distribute sensors at a designated spacing above the soil; and 3. Insert the sensors into the ground at a designated spacing in the soil; and 4. Bury the deployed sensor in the soil. This project will require the mechanical design of the deployment systems, mechatronic system design for operating and controlling the systems, and integration and interfacing with the agricultural ground robot for execution and tracking of sensor deployment locations. Field tests will be conducted at the Purdue University Agronomy Center for Research and Education (ACRE) facility.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics, IoT for Precision Agriculture
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Mechanical Engineering
- Electrical Engineering
- Computer Engineering
School/Dept.:
School of Mechanical Engineering
Professor:
David
Cappelleri
More information: https://iot4ag.us/
Design, build and flight testing of avian-inspired unmanned aerial vehicles
Description:
The objective of this project is to design, test, build, and fly prototype bird-like morphable, flapping-wing unmanned air vehicles. In particular, the objective is to create vehicles that mimic the flying characteristics of medium-sized birds (e.g., the Peregrine falcons). A goal of this project is to demonstrate controlled gliding and powered flights. The undergraduate students will perform conceptual design of the vehicles using CAD. Wind tunnel models will be created in the AAE build lab in ARMS and tested in the Boeing subsonic wind tunnel at the Aerospace Sciences Laboratory. Flight test models will be created in the AAE build lab and tested in-doors in PURT or similar.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Aeronautical and Astronautical Engineering
- Mechanical Engineering
Desired experience:
Working knowledge of aerodynamics, dynamics and control, CAD, wind tunnel testing, r/c airplane prototyping and electronics, and autopilot systems.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Leifur
Leifsson
Designing cognitively-aware intelligent tutoring systems with generative AI
Description:
We are looking for an undergraduate researcher to support development of a cognitively aware intelligent tutoring system for a psychomotor task. The task itself is learning how to land a quadrotor manually in a 2D quadrotor simulator module. The student will gain experience in the following areas:
- Sensor fusion
- Classification algorithms for human behavior
- System identification of human cognitive behavior models
- Synthesizing optimal control policies or algorithms that assist humans by responding to human behavior
- Improving generated formative feedback to assist in human learning
- Sensor fusion
- Classification algorithms for human behavior
- System identification of human cognitive behavior models
- Synthesizing optimal control policies or algorithms that assist humans by responding to human behavior
- Improving generated formative feedback to assist in human learning
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Human Factors, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
- Industrial Engineering
- Electrical Engineering
Desired experience:
This project requires experience with Python and MATLAB. While not required, prior experience with the OpenAI API and psycho-physiological/behavioral sensors (E.g. fNIRS, ECG, eyetracking) would be beneficial.
School/Dept.:
School of Mechanical Engineering
Professor:
Neera
Jain
More information: https://engineering.purdue.edu/JainResearchLab/
Developing a humanized CRISPR-inspired RNA-targeting system to treat neurodegenerative diseases
Description:
Develop translational methods to target dysregulated RNAs in neurodegenerative diseases. The project involves -
1. Construct expression plasmids for the humanized CRISPR-inspired RNA-targeting system
2. Test the feasibility of the system in easy-growing cancer cell
3. Culture ALS and FTD patient-derived iPS stem cells, differentiate into motor neuron
4. Deliver the humanized CRISPR-inspired RNA-targeting system into diseased neurons and test efficacy and phenotypic rescues
Students with long-term interests in neuroscience and neurodegenerative diseases are preferred.
1. Construct expression plasmids for the humanized CRISPR-inspired RNA-targeting system
2. Test the feasibility of the system in easy-growing cancer cell
3. Culture ALS and FTD patient-derived iPS stem cells, differentiate into motor neuron
4. Deliver the humanized CRISPR-inspired RNA-targeting system into diseased neurons and test efficacy and phenotypic rescues
Students with long-term interests in neuroscience and neurodegenerative diseases are preferred.
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
School/Dept.:
Department of Medicinal Chemistry and Molecular Pharmacology
Professor:
Yini
Li
More information: https://www.yinililab.com/
Development of Biomimetic, 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 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 a controlled-release nanocarrier formulation against opioid exposure
Description:
A majority of the drug overdose fatalities in the United States are due to synthetic opioids such as fentanyl and heroin. There is at present only one FDA-approved treatment for opioid overdose: naloxone, an opioid receptor antagonist currently marketed as a nasal spray or as an intramuscular injection. The low half-life of naloxone in the body significantly limits the therapeutic windows of these formulations, particularly against long-lasting synthetic opioids such as fentanyl. This in turn necessitates multiple consecutive doses of naloxone per patient over a sustained period of time, which leads to undesirable side effects and is logistically more difficult than a single-dose treatment would be.
The goal of this proposal is to demonstrate a sustained-release respiratory (nasal or pulmonary) naloxone nanocarrier formulation with a protective therapeutic window longer than the existing marketed intranasal formulation. The student will develop nanocarrier formulations and evaluate their stability, naloxone encapsulation efficiency, and in vitro naloxone release kinetics that will inform future work with collaborators to evaluate efficacy in vivo.
The goal of this proposal is to demonstrate a sustained-release respiratory (nasal or pulmonary) naloxone nanocarrier formulation with a protective therapeutic window longer than the existing marketed intranasal formulation. The student will develop nanocarrier formulations and evaluate their stability, naloxone encapsulation efficiency, and in vitro naloxone release kinetics that will inform future work with collaborators to evaluate efficacy in vivo.
Campus:
West Lafayette
Research categories:
Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Kurt
Ristroph
More information: https://www.ristrophlab.com/
Digital Twin and Sensing Integration for Smart Workzone
Description:
The key goal of this smart workzone project is to integrate intrusion detection sensors and digital twin to (a) improve safety for drivers, pedestrians, and other road users on urban streets and highways; and (b) provide a safe and efficient working environment for roadway maintenance workers by collecting real-time data focusing on Intelligent, Sensor-Based Infrastructure, and Systems Integration technology areas.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Human Factors, Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Understanding of Programming, Deep Learning, Sensor Fusion, Digital Twin
School/Dept.:
Lyles School of Civil and Construction Engineering
Professor:
Sogand
Hasanzadeh
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, 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
Digital twins and Industrial robots for smart manufacturing
Description:
The project is for development of deep learning models for remote operation of industrial and collaborative robots in the digital environment.
Campus:
West Lafayette
Research categories:
Deep Learning, Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Computer Engineering
School/Dept.:
School of Mechanical Engineering
Professor:
Martin
Jun
Does bacterial exopolysaccharide elicit plant immune responses
Description:
Throughout this 10-week program, the REU student will learn techniques integral to the field of molecular plant pathology through the study of virulence proteins (effectors) and canonical pattern-triggered immune (PTI) signaling pathways. Specifically, the REU student will assist in characterizing how the effector RipU from Ralstonia solanacearum strain K60 influences calcium signaling during plant immune responses. This work will include generating transgenic Arabidopsis thaliana lines via floral dipping and crossing, and testing the effect of RipU on canonical signaling pathways such as calcium and reactive oxygen species (ROS) signaling. Performing experiments to analyze host immune signaling will require the student to learn plant transformation techniques, confocal microscopy, image analysis/quantification, and a variety of supplemental techniques that are standard across the field of molecular biology. Additionally, the student will gain an understanding of canonical host immune responses in plants and how to quantify and observe them. Overall, the goal of this project is to teach the REU student standard techniques used in molecular plant pathology and provide a preliminary understanding of plant-pathogen interactions.
Learning outcomes:
Throughout this 10-week program we anticipate that the REU student will…
1. Listen, be curious, and ask questions!
2. Read all provided materials.
3. Attend weekly/bi-weekly meetings with PI and graduate student mentor.
4. Attend weekly/bi-weekly lab meetings.
5. Clearly communicate with graduate student mentor and PI.
6. Contribute to a positive workplace environment.
By the end of this 10-week program we expect that the REU student will…
1. Be familiar with basic concepts of plant pathology and plant-pathogen interactions.
2. Know canonical host immune responses and the assays used to measure these responses.
3. Be familiar with Ralstonia solancearum (e.g., lifecycle and lifestyle, virulence factors).
4. Understand how to generate robust experimental designs and research questions.
5. Think critically about given project and contribute intellectually to discussions.
6. Learn techniques and skills integral to the study of molecular plant pathology.
7. Present findings during a weekly lab meeting
Learning outcomes:
Throughout this 10-week program we anticipate that the REU student will…
1. Listen, be curious, and ask questions!
2. Read all provided materials.
3. Attend weekly/bi-weekly meetings with PI and graduate student mentor.
4. Attend weekly/bi-weekly lab meetings.
5. Clearly communicate with graduate student mentor and PI.
6. Contribute to a positive workplace environment.
By the end of this 10-week program we expect that the REU student will…
1. Be familiar with basic concepts of plant pathology and plant-pathogen interactions.
2. Know canonical host immune responses and the assays used to measure these responses.
3. Be familiar with Ralstonia solancearum (e.g., lifecycle and lifestyle, virulence factors).
4. Understand how to generate robust experimental designs and research questions.
5. Think critically about given project and contribute intellectually to discussions.
6. Learn techniques and skills integral to the study of molecular plant pathology.
7. Present findings during a weekly lab meeting
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biotechnology Data Insights, Cellular Biology, Ecology and Sustainability
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
No specific course work, but at least one college-level biology or microbiology course or AP Bio is recommended
School/Dept.:
Department of Botany and Plant Pathology
Professor:
Anjali
Iyer-Pascuzzi
Dont bother the driver: Sensor-scheduling for cognitive state estimation during automated driving.
Description:
During safety-critical human-automation interactions such as automated driving, knowledge of cognitive states such as the driver’s trust in the automation can help prevent automation misuse (over-reliance). However, unlike estimating the speed of a car, estimating a human’s cognitive state is not straightforward; it needs to be inferred from their behavior (usage of automation, eye-gaze), involuntary physiological responses (heart-rate), and what they self-report (to questions like “How much do you trust the automation?”). Unlike recording data-streams from sensors such as a heart-rate monitor, it is not reasonable to ask the user to report their trust too frequently. The goal of this project is to enable development and testing of algorithms that schedule solicitation of self-reports only when absolutely required to reduce uncertainty in cognitive state estimates.
The student will gain experience in:
- Driving simulation for human-centric research
- Conducting human subject experiments
- State estimation algorithms
The student will gain experience in:
- Driving simulation for human-centric research
- Conducting human subject experiments
- State estimation algorithms
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Human Factors, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering
- Computer Science
- Game Development and Design
Desired experience:
This project requires experience with Unreal Engine 5. Some knowledge of probability would help understand the scheduling algorithm and estimation in general.
School/Dept.:
School of Mechanical Engineering
Professor:
Neera
Jain
Effect of shock-induced compressive and shear work on the reactivity of hotspots in polymer-bonded explosives
Description:
- The shock-driven expansion of a soft polymer (ex. polystyrene) in a planar gap within a polymer-HE (high-explosive) material produces greater pressure-volume work upon recompression, leading to higher initial hotspot temperatures. This accelerates the initiation of chemical reactions in the collapsing HE, resulting in a faster transition to criticality (see Macatangay et al. 2024). However, the pore collapse process is known to additionally involve work due to shear. The role of a shear component in shock-induced collapse of porosity at polymer-HE interfaces remains unclear. This project will investigate the effect of shearing in a polymer on shock-induced pore collapse. Reactive molecular dynamics simulations will be performed on a quasi-1D polymer-HE system with a planar gap. Simulations will be performed at a variety of impact velocities to determine the onset of criticality relative to simulations without polymer. These results will be used to enhance MD-informed continuum models and provide guidance on choosing binders in polymer-bonded explosives.
- Macatangay, J., Li, C., & Strachan, A. (2024). Influence of Polymer on Shock-Induced Pore Collapse: Hotspot Criticality through Reactive Molecular Dynamics. The Journal of Physical Chemistry C, 128(39), 16619-16627.
- Islam, M. M., & Strachan, A. (2020). Role of dynamical compressive and shear loading on hotspot criticality in RDX via reactive molecular dynamics. Journal of Applied Physics, 128(6).
- Macatangay, J., Li, C., & Strachan, A. (2024). Influence of Polymer on Shock-Induced Pore Collapse: Hotspot Criticality through Reactive Molecular Dynamics. The Journal of Physical Chemistry C, 128(39), 16619-16627.
- Islam, M. M., & Strachan, A. (2020). Role of dynamical compressive and shear loading on hotspot criticality in RDX via reactive molecular dynamics. Journal of Applied Physics, 128(6).
Campus:
West Lafayette
Research categories:
Energy and Environment, Material Modeling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
Desired experience:
Intro programming, physics, and chemistry and a willingness to learn more are required.
School/Dept.:
School of Materials Engineering
Professor:
Alejandro
Strachan
More information: https://www.strachanlab.org
Effects of colliding deflagration waves on the post-shock system state in energetic materials: a molecular dynamics study
Description:
- Shock simulations on RDX that induce critical hotpots to the point that the deflagration waves collide. Deflagration waves show potential for local temperature spikes at the point of collision. It is expected that these spikes in temperature can influence the post-shock equilibrium temperature. This can be investigated quickly using continuum solvers, or simple geometries could be evaluated with molecular dynamics directly.
- [Macatangay, J., Li, C., Strachan, A. (2024). Influence of polymer on shock-induced pore collapse: Hotspot criticality through reactive molecular dynamics. The Journal of Physical Chemistry C]
- [Macatangay, J., Li, C., Strachan, A. (2024). Influence of polymer on shock-induced pore collapse: Hotspot criticality through reactive molecular dynamics. The Journal of Physical Chemistry C]
Campus:
West Lafayette
Research categories:
Energy and Environment, Material Modeling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
Desired experience:
Intro programming, physics, and chemistry and a willingness to learn more are required.
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
All students will be required to read relevant, peer-reviewed literature and keep a notebook or log of weekly research progress. At the end of the semester or term, each student will present a talk or poster on their results. Student efforts will contribute data, graphics, and efforts towards scientific publications.
All students will be required to read relevant, peer-reviewed literature and keep a notebook or log of weekly research progress. At the end of the semester or term, each student will present a talk or poster on their results. Student efforts will contribute data, graphics, and efforts towards scientific publications.
Campus:
West Lafayette
Research categories:
Energy and Environment, Engineering the Built Environment, Environmental Characterization, Fluid Modelling and Simulation, Material Modeling and Simulation, Material Processing and Characterization, Nanotechnology, Thermal Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Civil Engineering
- Environmental and Ecological Engineering
- Materials 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, 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
Electrochemical impedance spectroscopy of voltage biased micro and ultramicro scale neural interfaces
Description:
The student will participate in electrochemical impedance spectroscopy (EIS) data collection from implanted devices in the brain. The devices will contain micro- and ultramicroscale electrodes for electrical stimulation and recording. After approximately two-four weeks of implantation the electrodes will be voltage biased for short durations to modulate the neural interface. EIS will be used to characterize the safety and efficacy of the voltage biasing.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Medical Science and Technology, Microelectronics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Kevin
Otto
More information: https://nprlab.org/
Energy-Efficient Compute-In-Memory AI Chips: Algorithm-Hardware Co-Design and System Demonstrators
Description:
I. Project Vision and Background:
The rapid growth of artificial intelligence (AI), especially Large Language Models (LLMs) like GPT, is revolutionizing the way people work, learn, communicate, and access healthcare. Due to the complexity of AI workloads and the limitations of today’s semiconductor hardware, computing with powerful LLMs incurs enormous energy costs and generates significant carbon footprints. This project will develop a holistic computing solution to provide reliable, private, and energy-efficient computing capabilities directly to end users’ devices, thereby democratizing access to advanced AI for broader society. Our team is synergizing semiconductor material advancements with novel chip designs and algorithms, to create a new computing platform that will run AI workloads more efficiently than existing silicon-based platforms.
II. SURF Student's role in the project:
- The SURF researcher will be mentored by faculty and graduate students to study, investigate, identify, and explore new algorithm-hardware co-design opportunities for the new AI hardware platform under development by the team.
- Develop and iterate new application-specific, system-level demonstrators using the software artifacts and hardware prototypes that the team generated so far.
- Connect software and hardware demos to broader, interesting real-world applications.
- Software/hardware "integrated live demo", using FPGA-PCB test boards.
- Document research results with a target for IEEE/ACM conference publications (student's travel will be sponsored by the faculty).
The rapid growth of artificial intelligence (AI), especially Large Language Models (LLMs) like GPT, is revolutionizing the way people work, learn, communicate, and access healthcare. Due to the complexity of AI workloads and the limitations of today’s semiconductor hardware, computing with powerful LLMs incurs enormous energy costs and generates significant carbon footprints. This project will develop a holistic computing solution to provide reliable, private, and energy-efficient computing capabilities directly to end users’ devices, thereby democratizing access to advanced AI for broader society. Our team is synergizing semiconductor material advancements with novel chip designs and algorithms, to create a new computing platform that will run AI workloads more efficiently than existing silicon-based platforms.
II. SURF Student's role in the project:
- The SURF researcher will be mentored by faculty and graduate students to study, investigate, identify, and explore new algorithm-hardware co-design opportunities for the new AI hardware platform under development by the team.
- Develop and iterate new application-specific, system-level demonstrators using the software artifacts and hardware prototypes that the team generated so far.
- Connect software and hardware demos to broader, interesting real-world applications.
- Software/hardware "integrated live demo", using FPGA-PCB test boards.
- Document research results with a target for IEEE/ACM conference publications (student's travel will be sponsored by the faculty).
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Computer Architecture, Deep Learning, Heterogeneous Integration, Internet of Things (IoT), Microelectronics, Mobile Computing, Nanotechnology, System-on-a-Chip
Preferred major(s):
- No Major Restriction
Desired experience:
Students with various backgrounds from software, machine learning, computer architecture, to VLSI and semiconductors are all welcome to apply, and will have a unique opportunity through this project to get exposed to a truly cross-cutting research topic – "AI hardware co-designs". Some specific skills/experience may be able to provide a jumpstart and boost the productivity during the summer:
- Programming skills (Python, popular ML frameworks)
- Prototyping experience with FPGA boards
- PCB board design and testing
- Software development
- Basic knowledge about VLSI circuits and semiconductor devices
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Haitong
Li
More information: https://engineering.purdue.edu/NanoX/research/
Engineer a synthetic neuron using a bottom-up approach
Description:
Neurons convert biochemical information (through binding of a neurotransmitter) to electrical signal (via action potential) and back to biochemical signal (through the release of neurotransmitters). These distinct and separable processes can be reconstituted in a synthetic neuron by using natural and engineered proteins, and a synthetic neuron platform can be used to understand the rules governing the emergence of the present morphology of a neuron and the architecture of the neuronal system. This project thus aims to construct a synthetic neuron with a modular design and a programmable synthetic neuronal network capable of recapitulating basic functions of a natural neuronal system (e.g., action potential, synaptic communication, and basic computation) and with a long-term vision of incorporating more advanced computation and potentiation.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biotechnology Data Insights, Cellular Biology, Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Chemical Engineering
- Neurobiology and Physiology
- Biological Engineering - multiple concentrations
Desired experience:
Molecular cloning skills required
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Chongli
Yuan
More information: https://cyuangroup.com/
Engineering Materials for Thermal Transport for Semiconductor Packaging
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. 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.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Energy and Environment, Heterogeneous Integration, Material Processing and Characterization, Microelectronics, Thermal Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
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.
School/Dept.:
School of Mechanical Engineering
Professor:
Amy
Marconnet
More information: https://engineering.purdue.edu/MTEC
Engineering the Metastatic Niche
Description:
Metastasis is the single greatest driver of cancer-related mortalities, regardless of the tumor’s tissue of origin. This is particularly true for breast cancer, where the five-year survival rate is exceptional if the disease remains local. However, once breast cancer has metastasized, patient survival drops almost 75%. A defining hallmark of metastasis is the ability of primary tumor cells to modulate the local environment to facilitate tissue invasion. These microenvironmental cues can elicit reciprocal phenotypic transitions, giving rise to heterogenous populations of tumor cells, facilitating metastatic initiation and drug resistance. Additionally, tumor cells can also modify the microenvironment of future metastatic sites well before they colonize these tissues. Key features of these premetastatic niches (PMN), including a unique extracellular matrix (ECM) and cellular composition, help facilitate crucial steps of the metastatic cascade. Given the complexities of these tumor-host interactions, there is a critical need to engineer in vitro platforms to study PMN formation. Use of these controlled model systems will aid in ability to devise strategies capable of altering the soil to restrict growth of the cancerous seeds.
Extracellular vesicles (EVs) play an important and newly-identified role in establishing the PMN. It has recently been observed that in murine models, pre-treatment with tumor derived EVs can not only enhance metastasis, but they also influence which tissues are colonized through an integrin-mediated pathway. Despite these advances, there remain fundamental gaps in understanding the mechanism through which the PMN facilitates metastatic seeding, how the PMN gives rise to heterogenous populations of cells capable of colonizing the newly invaded tissue, and how the components within the niche reduce therapeutic efficacy. To address these gaps, we have established a 3D culture model consisting of human pulmonary fibroblasts that can be pretreated with EVs prior to coculture with tumor cells. Using this reductionist model of the PMN, we have demonstrated that EVs produced by metastatic tumor cells enhance colonization. Furthermore, we have demonstrated the matrix modifying enzyme transglutaminase 2 (TG2) is loaded onto EVs and that genetic deletion of TG2 drastically inhibits the metastasis of mammary tumors and prevents the ability of EVs to promote pulmonary tumor formation. Given these findings, we seek to address the hypothesis that disrupting premetastatic niche formation can be used to mitigate metastatic outgrowth and improve patient response to therapy.
Students will learn advanced 3D culture techniques, molecular biology, experimental design, develop presentation skills, data analysis, and team work.
Extracellular vesicles (EVs) play an important and newly-identified role in establishing the PMN. It has recently been observed that in murine models, pre-treatment with tumor derived EVs can not only enhance metastasis, but they also influence which tissues are colonized through an integrin-mediated pathway. Despite these advances, there remain fundamental gaps in understanding the mechanism through which the PMN facilitates metastatic seeding, how the PMN gives rise to heterogenous populations of cells capable of colonizing the newly invaded tissue, and how the components within the niche reduce therapeutic efficacy. To address these gaps, we have established a 3D culture model consisting of human pulmonary fibroblasts that can be pretreated with EVs prior to coculture with tumor cells. Using this reductionist model of the PMN, we have demonstrated that EVs produced by metastatic tumor cells enhance colonization. Furthermore, we have demonstrated the matrix modifying enzyme transglutaminase 2 (TG2) is loaded onto EVs and that genetic deletion of TG2 drastically inhibits the metastasis of mammary tumors and prevents the ability of EVs to promote pulmonary tumor formation. Given these findings, we seek to address the hypothesis that disrupting premetastatic niche formation can be used to mitigate metastatic outgrowth and improve patient response to therapy.
Students will learn advanced 3D culture techniques, molecular biology, experimental design, develop presentation skills, data analysis, and team work.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Cellular Biology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
biology, chemistry, tissue engineering, microscopy, and design of experiments.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Luis
Solorio
More information: https://soloriolab.wixsite.com/tmet
Enhancing Pavement Instrumentation and Monitoring
Description:
This project is to contribute to an edge-computing-based system for pavement instrumentation and monitoring to improve data collection and visualization for the Indiana Department of Transportation (INDOT). The system will integrate in-pavement sensors with road-side data acquisition, a camera package for vehicle tracking, and cloud-based storage for real-time access and analysis. The undergraduate researcher will assist with tasks such as hardware integration, sensor data acquisition, software development for data visualization, and field deployment at test sites including INDOT’s Accelerated Pavement Testing facility and I-65. This work supports improving pavement health monitoring and long-term infrastructure management.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Computer Architecture, Cybersecurity, Engineering the Built Environment, Internet of Things (IoT), Mobile Computing
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
James
Krogmeier
Environmental Impact Assessment of Noise Generated During Offshore Wind Foundation Installation
Description:
Monopile foundations for offshore wind turbines are typically installed with large hydraulic hammers, generating very loud sounds that propagate through the ocean via the water and seafloor. This noise is known to induce behavioral change and (in some cases) auditory injury in marine animals up to 50 km away, including whales and other cetaceans that communicate at low frequencies. Our multidisciplinary research team is developing a new noise control technology for monopile foundation installation that combines active and passive noise abatement technologies. Lab-scale experiments show that our novel technology has the technical potential to reduce noise by an order of magnitude or more compared to state-of-the-art commercial solutions. If successfully scaled up and commercialized, this technology could reduce noise-related risks for ocean wildlife near offshore wind development sites.
In this SURF project, the student researcher will collaborate in a research team that spans four engineering departments at Purdue (Mechanical Engineering, Civil & Construction Engineering, Materials Science and Engineering, and Environmental & Ecological Engineering). This specific SURF opportunity is for a member of our Environmental Impact Assessment research sub-team. Under the guidance of faculty and graduate student research mentors, the SURF student will perform a holistic environmental impact assessment of the noises generated during pile-driving of monopile wind turbine foundations, and will assess the potential for these impacts to be mitigated through noise-abatement technologies (commercially available and our novel solution). This project will involve the use of environmental life cycle assessment (LCA) and other impact assessment techniques. Previous coursework or exposure to LCA or carbon accounting would be helpful but is not required. The successful applicant will have a relevant engineering background combined with clearly expressed interest in environmental sustainability, renewable energy, biodiversity, and/or ocean ecosystems.
In this SURF project, the student researcher will collaborate in a research team that spans four engineering departments at Purdue (Mechanical Engineering, Civil & Construction Engineering, Materials Science and Engineering, and Environmental & Ecological Engineering). This specific SURF opportunity is for a member of our Environmental Impact Assessment research sub-team. Under the guidance of faculty and graduate student research mentors, the SURF student will perform a holistic environmental impact assessment of the noises generated during pile-driving of monopile wind turbine foundations, and will assess the potential for these impacts to be mitigated through noise-abatement technologies (commercially available and our novel solution). This project will involve the use of environmental life cycle assessment (LCA) and other impact assessment techniques. Previous coursework or exposure to LCA or carbon accounting would be helpful but is not required. The successful applicant will have a relevant engineering background combined with clearly expressed interest in environmental sustainability, renewable energy, biodiversity, and/or ocean ecosystems.
Campus:
West Lafayette
Research categories:
Ecology and Sustainability, Energy and Environment, Environmental Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Previous coursework or exposure to LCA or carbon accounting would be helpful but is not required. The successful applicant will have a relevant engineering background combined with an interest in environmental sustainability, renewable energy, biodiversity, and/or ocean ecosystems.
School/Dept.:
School of Mechanical Engineering
Professor:
Heather
Liddell
Estimating wild bird communities around commercial poultry facilities to inform risk from Highly Pathogenic Avian Influenza
Description:
Wild animal populations will be characterized at 12 commercial laying hen facilities located throughout the Mississippi flyway using an Unmanned Aerial System (drone) equipped with color video and thermal cameras will be used for the characterization of wildlife present around the facility. Drone surveillance will be conducted using a Skydio X-10 unit. The unit has six Samsung 1/2.8” 32 mp color CMOS navigational cameras on the top and bottom of the drone. The unit also features the VT300-Z Sensor package, which contains three cameras: a ½” 48mp CMOS telephoto camera (13◦ Field of View (FOV)), a 1/1.7” 64mp CMOS narrow camera (50◦ FOV), and a FLIR Boson+ Uncooled Vox Microbolometer radiometric thermal camera featuring a 41◦ FOV and a 640 x 512 resolution. The multiple cameras allow thermal and RGB videos to be recorded simultaneously and stored in an on-board 256 GB Micro SD card. Sampling will follow an exhaustive lawn mower pattern where the drone flies back and forth over a facility and up to a 500 m surrounding buffer exhaustively counting animals and measuring the distance of each from the laying hen facility. Post processing of thermal imagery combined with field observations of animals during drone flights will be used to identify observed species. Additionally, using the drone surveillance and visual observations, we will document the environment around the facility looking for existing habitats or potential attractants, quantify congregations of wildlife in specific areas and measure distances of those areas from facility or other environmental features. General linear models will be used to statistically test if densities of wild bird species identified around facilities change significantly between temporally repeated measures with season and between seasons as well as across sites that represent a gradient of proximities to migratory fly ways used by wild birds.
Automatic Recording Units (ARU) will be set up at each facility and programmed to record singing birds from an hour pre-dawn until three hours after dawn each sampling day. The local placement of ARUs will be based on preliminary surveys, historical data, and habitat suitability to maximize detection probabilities (Beauregard, 2024). Data collection will consist of programming ARUs to record during peak vocalization times and allowing the devices to run over the course of the study period. Subsequently, the automated acoustic data will be integrated with the drone surveillance observations to better assess bird abundance and activity patterns (Doser, 2020). Algorithms designed for call classification will be supplemented by machine learning and manual verification to analyze the recordings using established indices (Towsey et al., 2014). Occupancy modeling (MacKenzie et al., 2002; Pauli et al., 2017) will be used with the resulting wild bird species detection files to estimate probability of occurrence for each species as a function of spatial proximity to fly way and facility characteristics. The interpretation of results will focus on understanding habitat use, species abundance and population dynamics, cross referencing visual data collected from drones and acoustic detections from ARUs to gain insight into bird behavior and movement over time across fine and broad geospatial scales
Automatic Recording Units (ARU) will be set up at each facility and programmed to record singing birds from an hour pre-dawn until three hours after dawn each sampling day. The local placement of ARUs will be based on preliminary surveys, historical data, and habitat suitability to maximize detection probabilities (Beauregard, 2024). Data collection will consist of programming ARUs to record during peak vocalization times and allowing the devices to run over the course of the study period. Subsequently, the automated acoustic data will be integrated with the drone surveillance observations to better assess bird abundance and activity patterns (Doser, 2020). Algorithms designed for call classification will be supplemented by machine learning and manual verification to analyze the recordings using established indices (Towsey et al., 2014). Occupancy modeling (MacKenzie et al., 2002; Pauli et al., 2017) will be used with the resulting wild bird species detection files to estimate probability of occurrence for each species as a function of spatial proximity to fly way and facility characteristics. The interpretation of results will focus on understanding habitat use, species abundance and population dynamics, cross referencing visual data collected from drones and acoustic detections from ARUs to gain insight into bird behavior and movement over time across fine and broad geospatial scales
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology, Ecology and Sustainability
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Student should be prepared for field work including long days and extensive travel.
Student should have experience with GIS tools and using R for statistical analysis.
Student should have skills in identifying birds species visually and acoustically.
Students with part 107 licensees for drone operation would be great but not required.
School/Dept.:
Department of Forestry and Natural Resources
Professor:
Patrick
Zollner
More information: n/a
Evaluation of Propellant Burning Characteristics in High-Speed Flows (AAMP-UP-PERC)
Description:
Novel propellants are being investigated for a range of potential applications, such as high-speed propulsion systems. It is of interest to evaluate the ignition and combustion of these propellants at relevant flow conditions. The goal of this project is to perform in-situ optical diagnostics to assess the heat release and reaction progress as a function of the propellant composition. The student will gain a basic understanding of the current state of the art in prior research efforts and participate in the preparation, set-up, testing, and evaluation of these propellants. The effort will include regular project meetings and progress updates with the research team. The work will culminate in a technical presentation and/or written report.
Campus:
West Lafayette
Research categories:
Thermal Technology, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
Desired experience:
Experience with thermodynamics, fluid mechanics, basic physics, basic chemistry, and engineering design is desired. Training on specific skills will be provided.
School/Dept.:
School of Mechanical Engineering
Professor:
Terrence
Meyer
More information: engineering.purdue.edu/trmeyer
Event-based SLAM and Navigation for Legged Robots
Description:
The project explores using event cameras in simultaneous localization and mapping austere, unstructured environments. Event cameras are different from conventional RGB cameras in how they capture information. Using the event-based approach, we will create neuromorphic algorithms to do SLAM and path planning for the navigation of a legged robot.
Campus:
West Lafayette
Research categories:
Computer Architecture, Deep Learning, Fabrication and Robotics, Mobile Computing
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Computer Engineering
- Computer Science
- Mechanical Engineering
- Electrical Engineering
Desired experience:
Experience in ROS, Python, and C/C++. Hands-on work experience with embedded hardware.
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Upinder
Kaur
Expediting Household Recovery After Disasters with Evidence Driven Decisions
Description:
The research team is actively helping households in the Los Angeles area better understand how to recover from the 2025 Palisades Fire and Eaton Fire. The team is conducting a household survey, supporting soil testing and testing of contaminated swimming pools. This work supports our goal of developing a “playbook” that can be provided to households so they can better navigate the post-disaster environmental health and safety questions.
The summer undergraduate will assist the research team analyze data and also conduct laboratory experiments related to this work. At the time this project was proposed (February 2025), the exact scope of the project for the SURF student was further being defined. Past SURF projects on this team have involved chemical contamination and decontamination of plumbing components, baby items, and exposure assessments. Prior study results have been adopted in post-disaster protocols. More information about prior and ongoing studies can be found on our website.
The summer undergraduate will assist the research team analyze data and also conduct laboratory experiments related to this work. At the time this project was proposed (February 2025), the exact scope of the project for the SURF student was further being defined. Past SURF projects on this team have involved chemical contamination and decontamination of plumbing components, baby items, and exposure assessments. Prior study results have been adopted in post-disaster protocols. More information about prior and ongoing studies can be found on our website.
Campus:
West Lafayette
Research categories:
Energy and Environment, Engineering the Built Environment, Environmental Characterization, Human Factors, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Students with chemistry, materials, environmental, engineering, and/or social-behavioral human interactions interests are encouraged to apply. Undergraduate students from the inside and outside the areas impacted by the fires will be considered.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Andrew
Whelton
More information: www.PlumbingSafety.org
FLORA: Field and Landscape Observation via Robotic Automation
Description:
FLORA: Field and Landscape Observation via Robotic Automation initiative aims to utilize a combination of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs), integrating low-cost, sustainable sensors like paper-based lightweight wearables. These technologies will monitor agricultural lands and forests to enhance productivity, mitigate diseases, control pests, and preserve ecosystems. The project incorporates artificial intelligence (AI) and custom-designed robotics, including quadrupole robotics, to enable real-time data collection and analysis for extended periods with efficient spherical solar cell technology.
Objectives: Our primary objective is to expand access to precision agriculture technologies, given that only approximately 30% of U.S. farmers currently utilize them, despite their significant economic benefits.
Hypotheses: 1) Can integrating low-cost sensors with UGV and UAV platforms achieve accurate real-time monitoring of agricultural lands and forests? 2) Will AI algorithms identify patterns and anomalies for early detection of diseases, pests, and environmental risks? 3) How do custom-designed robotics enhance mobility, operating time, and adaptability for comprehensive coverage of diverse terrains?
Methodology: We will design UGV and UAV platforms equipped with sustainable wearable sensors for data collection and solar cells. AI algorithms will analyze data in real-time, including pattern recognition and anomaly detection. Field trials across diverse landscapes will evaluate system performance.
Objectives: Our primary objective is to expand access to precision agriculture technologies, given that only approximately 30% of U.S. farmers currently utilize them, despite their significant economic benefits.
Hypotheses: 1) Can integrating low-cost sensors with UGV and UAV platforms achieve accurate real-time monitoring of agricultural lands and forests? 2) Will AI algorithms identify patterns and anomalies for early detection of diseases, pests, and environmental risks? 3) How do custom-designed robotics enhance mobility, operating time, and adaptability for comprehensive coverage of diverse terrains?
Methodology: We will design UGV and UAV platforms equipped with sustainable wearable sensors for data collection and solar cells. AI algorithms will analyze data in real-time, including pattern recognition and anomaly detection. Field trials across diverse landscapes will evaluate system performance.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Fabrication and Robotics, Heterogeneous Integration, Internet of Things (IoT), IoT for Precision Agriculture, Microelectronics, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Computing programming
Hands on lab experience in physics and chemistry
Electronic circuitry and systems are plus but not a must
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
MUHAMMAD
HUSSAIN
More information: http://mmhlabs.org
Fecal microbiota transplantation: A novel biotherapeutic to reduce the burden of antimicrobial resistance, virulence, and antimicrobial resistance mobilization
Description:
Fecal Microbiota Transplantation (FMT) is a treatment in which immunocompromised patients receive a sample of fecal material to alter disruptions of gastrointestinal microbiome (i.e. dysbiosis). FMT treatments are being used to treat dysbiosis stemming from life-threatening metabolic, immune, and neoplastic conditions. In addition, antimicrobial resistance genes (ARGs) are genes that confer a resistance to antimicrobial treatments and
are a global public health threat. While FMTs show great clinical promise to restore microbiome health, it is unclear whether FMTs are a potentially onorous source of novel ARGs that can be inadvertently introduced to immunocompromised patients, or conversely, whether this biological therapy can be leveraged to reduce the burden of antimicrobial resistance overall.
This summer research project endeavors to perform metagenomic fingerprinting to determine if FMTs can be a source of ARG introduction to vulnerable populations. Leveraging network analysis and machine learning approaches, the objective for this project will be to identify high-risk mechanisms of AMR, virulence, and horizontal gene transfer that can be introduced or ameliorated from the gastrointestinal microbiome. This project will leverage our extensive dataset of fecal microbiomes collected longitudinally from patients receiving FMT therapies, as well as our extensive bioinformatic applications and computing tools. Students will be responsible for collaborating with bioinformaticians and scientists to perform data analysis, visualization, and communication.
are a global public health threat. While FMTs show great clinical promise to restore microbiome health, it is unclear whether FMTs are a potentially onorous source of novel ARGs that can be inadvertently introduced to immunocompromised patients, or conversely, whether this biological therapy can be leveraged to reduce the burden of antimicrobial resistance overall.
This summer research project endeavors to perform metagenomic fingerprinting to determine if FMTs can be a source of ARG introduction to vulnerable populations. Leveraging network analysis and machine learning approaches, the objective for this project will be to identify high-risk mechanisms of AMR, virulence, and horizontal gene transfer that can be introduced or ameliorated from the gastrointestinal microbiome. This project will leverage our extensive dataset of fecal microbiomes collected longitudinally from patients receiving FMT therapies, as well as our extensive bioinformatic applications and computing tools. Students will be responsible for collaborating with bioinformaticians and scientists to perform data analysis, visualization, and communication.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biotechnology Data Insights
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Desired experience in skill: Computer science, bioinformatics, algorithmics, biostatistics. Students should have adequate exposure and experience using R, Python, Rust, Perl, SQL, or related languages. Working diligently with a large interdisciplinary team is a must.
School/Dept.:
Department of Veterinary Clinical Sciences
Professor:
Ilya
Slizovskiy
More information: slizovskiy.com
Generate tissue-specific transgenic zebrafish lines for cellular voltage and calcium signaling.
Description:
This project is to generate new transgenic fish lines and characterize and study existing fish lines for cellular voltage and calcium signaling in fish embryos. Students will have a chance to lead and participate in genetic research with a zebrafish model system.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology, Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Molecular and cellular biology background are required for this project.
School/Dept.:
Department of Comparative Pathobiology
Professor:
GuangJun
Zhang
More information: http://www.vet.purdue.edu/discovery/zhang/index.php
Geometry of Secure Computation
Description:
Title: Geometry of Secure Computation
Mentors: Hemanta K. Maji (Computer Science) and Saugata Basu (Mathematics)
Aim: This project will automate secure computation construction within budgeted computation resources. This research requires building new mathematics, a research direction pioneered by Basu and Maji.
Background: Secure computation (MPC) allows parties to compute using their private data, revealing only the output. This cryptographic primitive was introduced in the early 1980s, and currently, this technology is getting close to reality with a broad spectrum of real-world applications. Still, foundational questions at the intersection of MPC and complexity theory remain open nearly 4-and-a-half decades later, directly affecting MPC efficiency and its societal impact.
Research Question: Can we securely compute a function? If so, what is the protocol requiring the least resources? Otherwise, what is the obstruction to security?
In 2022, Basu and Maji (with their Ph.D. students) made a significant breakthrough. They connected these complexity-theoretic questions in MPC with membership queries into lamination hulls—a generalization of convex hulls motivated by the hydrodynamics literature. In 2024, Basu and Maji solved an open problem in geometry to answer some of these questions vis-a-vis MPC’s communication complexity.
These results show that determining the feasibility and communication complexity of secure computation is decidable.
Proposed research in the Summer Internship: The time complexity of computing the lamination hulls in the current works by Basu and Maji is prohibitively high—a tower of height five in the description complexity of the MPC function.
The summer intern will explore improving the representation of these lamination hulls and the complexity of computing them, as well as write code (in a high-level mathematical system like sagemath) implementing these algorithms.
We envision the following outcomes of this research:
* Computer-assisted MPC protocol design. Typically, MPC protocols are human-designed, and most of them are accompanied by no proof of optimality. Our computer-assisted search algorithms will identify optimal MPC protocols. Our computer-assisted search will enable automated MPC protocol design and search MPC protocols beyond human intuition/ingenuity.
* Computer-assisted obstruction detection. Our automated techniques also generate succinct certificates proving the impossibility of designing MPC protocols within budgeted resources. This is a computer-assisted technique, which is in contrast to proving challenging information lower bounds using highly ingenious information-theoretic invariants.
The intern will begin by reading the research papers on this topic (including ones by Basu and Maji) and, in parallel, research these open research problems.
These research outcomes (along with Basu and Maji’s papers) will be used as preliminary results to write future proposals for funding to NSF.
Broader context: The connection between distributed computation and topology is well-known due to the 2004 Goedel prize-winning works. Similarly, connections between mechanism design and tropical algebra are also well-established. Our research direction is similar in spirit, with the additional exciting feature that the geometry research motivated by the cryptography questions is open!
Relevant papers by Basu and Maji:
1. Solving Linear Inequalities over Convex Sets & its Applications to Cryptography and Hydrodynamics (https://www.cs.purdue.edu/homes/hmaji/papers/BKMN24.pdf)
2. Randomized Functions with High Round Complexity (https://www.cs.purdue.edu/homes/hmaji/papers/BKMN23.pdf)
3. Geometry of Secure Two-party Computation (https://www.cs.purdue.edu/homes/hmaji/papers/BKMN22.pdf)
Mentors: Hemanta K. Maji (Computer Science) and Saugata Basu (Mathematics)
Aim: This project will automate secure computation construction within budgeted computation resources. This research requires building new mathematics, a research direction pioneered by Basu and Maji.
Background: Secure computation (MPC) allows parties to compute using their private data, revealing only the output. This cryptographic primitive was introduced in the early 1980s, and currently, this technology is getting close to reality with a broad spectrum of real-world applications. Still, foundational questions at the intersection of MPC and complexity theory remain open nearly 4-and-a-half decades later, directly affecting MPC efficiency and its societal impact.
Research Question: Can we securely compute a function? If so, what is the protocol requiring the least resources? Otherwise, what is the obstruction to security?
In 2022, Basu and Maji (with their Ph.D. students) made a significant breakthrough. They connected these complexity-theoretic questions in MPC with membership queries into lamination hulls—a generalization of convex hulls motivated by the hydrodynamics literature. In 2024, Basu and Maji solved an open problem in geometry to answer some of these questions vis-a-vis MPC’s communication complexity.
These results show that determining the feasibility and communication complexity of secure computation is decidable.
Proposed research in the Summer Internship: The time complexity of computing the lamination hulls in the current works by Basu and Maji is prohibitively high—a tower of height five in the description complexity of the MPC function.
The summer intern will explore improving the representation of these lamination hulls and the complexity of computing them, as well as write code (in a high-level mathematical system like sagemath) implementing these algorithms.
We envision the following outcomes of this research:
* Computer-assisted MPC protocol design. Typically, MPC protocols are human-designed, and most of them are accompanied by no proof of optimality. Our computer-assisted search algorithms will identify optimal MPC protocols. Our computer-assisted search will enable automated MPC protocol design and search MPC protocols beyond human intuition/ingenuity.
* Computer-assisted obstruction detection. Our automated techniques also generate succinct certificates proving the impossibility of designing MPC protocols within budgeted resources. This is a computer-assisted technique, which is in contrast to proving challenging information lower bounds using highly ingenious information-theoretic invariants.
The intern will begin by reading the research papers on this topic (including ones by Basu and Maji) and, in parallel, research these open research problems.
These research outcomes (along with Basu and Maji’s papers) will be used as preliminary results to write future proposals for funding to NSF.
Broader context: The connection between distributed computation and topology is well-known due to the 2004 Goedel prize-winning works. Similarly, connections between mechanism design and tropical algebra are also well-established. Our research direction is similar in spirit, with the additional exciting feature that the geometry research motivated by the cryptography questions is open!
Relevant papers by Basu and Maji:
1. Solving Linear Inequalities over Convex Sets & its Applications to Cryptography and Hydrodynamics (https://www.cs.purdue.edu/homes/hmaji/papers/BKMN24.pdf)
2. Randomized Functions with High Round Complexity (https://www.cs.purdue.edu/homes/hmaji/papers/BKMN23.pdf)
3. Geometry of Secure Two-party Computation (https://www.cs.purdue.edu/homes/hmaji/papers/BKMN22.pdf)
Campus:
West Lafayette
Research categories:
Cybersecurity
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Computer Science
Desired experience:
Background in cryptography and geometry
School/Dept.:
Computer Science
Professor:
Hemanta
Maji
More information: https://www.cs.purdue.edu/homes/hmaji/papers.html
Gradual Verification: Software Assurance 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, Hemant Gouni, Jacob Gorenburg, Mona Zhang, Conrad Zimmerman, Joshua Sunshine, Éric Tanter, and Jonathan Aldrich. "Gradual C0: Symbolic Execution for Efficient Gradual Verification." arXiv preprint arXiv:2210.02428 (2022).
[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, Hemant Gouni, Jacob Gorenburg, Mona Zhang, Conrad Zimmerman, Joshua Sunshine, Éric Tanter, and Jonathan Aldrich. "Gradual C0: Symbolic Execution for Efficient Gradual Verification." arXiv preprint arXiv:2210.02428 (2022).
[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:
Applicants will have preferably completed 2 programming courses by the time the Summer program starts; or have equivalent programming experience.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Jenna
DiVincenzo
More information: https://jennalwise.github.io/projects/
Great Lakes Remote Sensing: Satellite Characterization of Coastal Changes and Processes
Description:
This NASA-funded project aims to leverage high-resolution "SmallSat" sensing platforms to quantify coastal changes and dynamics in the Great Lakes. Students will work with satellite imagery to quantify coastal changes in the Great Lakes that result from hydrodynamic and hydrologic conditions. Work will involve work with GIS, python scripting, and actual field visits to coastal sites for environmental characterization. Students will also gain exposure to other Great Lakes research in the Troy Lab, including marine robotics, hydrodynamic modelling, water quality measurements, and lots more.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Civil Engineering
- Aeronautical and Astronautical Engineering
- Mechanical Engineering
- Agricultural Engineering
- Environmental and Ecological Engineering
Desired experience:
Familiarity with GIS is helpful but not required
Familiarity with Python / Matlab scripting is helpful but not required
Interest in the environment
Comfortable working outdoors for fieldwork occasionally
Good communication skills and initiative
School/Dept.:
Lyles School of Civil Engineering
Professor:
Cary
Troy
More information: https://troylabpurdue.org/
Here is the twist: fabricating van der Waals heterostructures with precise angle alignment
Description:
When stacking two atomically-thin materials onto each other, a simple twist between the lattice alignment can make a big difference. The long-wavelength moiré superlattice potential can significantly modify the electronic structure of the individual layers, and create highly tunable "flat bands" which can give rise to exotic states of matter such as magnetism, superconductivity, and topological non-trivial states. With the help of scanning tunneling microscopy (STM), one can study these exciting electronic and magnetic states of matter with atomic precision.
In this project, the student will assist with the fabrication of twisted bilayer graphene and transition metal dichalcogenides for STM characterization. If successful, the student will also have the opportunity to learn about advanced electron microscopy with STM.
In this project, the student will assist with the fabrication of twisted bilayer graphene and transition metal dichalcogenides for STM characterization. If successful, the student will also have the opportunity to learn about advanced electron microscopy with STM.
Campus:
West Lafayette
Research categories:
Material Processing and Characterization, Microelectronics, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Physics
- Electrical Engineering
- Materials Engineering
School/Dept.:
Physics and Astronomy
Professor:
Tiancong
Zhu
More information: https://sites.google.com/view/zhulab/home
Heterogeneous Integration/Advanced packaging
Description:
The rapid increase in chip performance associated with Moore’s law has also raised interest and expectations around creating packaging devices with improved size, weight, and power. To keep sizes manageable while improving functionality, complex packaged electronics like iPhones require similar components to be compressed together horizontally and vertically, and combined with dissimilar components providing complementary functions. Significant challenges in heterogeneous integration to be addressed in research include maintaining the reliability of connections such as solder bumps, managing thermal cycling, and limiting damage from mechanical stress that can cause failures.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Ganesh
Subbarayan
High Performance Concrete from Hydrogel-Based Superabsorbent Materials
Description:
Concrete that is internally cured by water-swollen superabsorbent polymer (SAP) particles has improved strength and durability. This project will investigate new SAP formulations that have increased absorption capacity in a wider variety of low-carbon concrete materials. The student will conduct swelling tests and optical microscopy of the SAP particles and then perform optical microscopy and mechanical measurements of the SAP-cured concrete. Improvements in concrete strength and durability is a step towards reducing the carbon footprint of our civil infrastructure materials, as production of new cement results in 7-9% of the global CO2 emissions each year.
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Enthusiasm for chemistry and an interest in materials research. Prior experiences with cement and concrete would be a benefit to the project but are not required.
School/Dept.:
School of Materials Engineering
Professor:
Kendra
Erk
More information: https://soft-material-mechanics.squarespace.com/home/
High-Speed Hypersonic Turbulence Simulations
Description:
Advancing research in high-speed flight directly addresses (the ever-increasing) concerns regarding the competitiveness of our national defense resources in the international landscape. The proposed project specifically tackles the problem of hypersonic boundary layer transition to turbulence by adopting an interdisciplinary approach requiring not only knowledge of computational and experimental aerodynamics, but also material science and acoustics. Particular focus is on high-enthalpy flows, that pose numerous numerical challenges due to the very steep thermodynamic gradients at the wall. A good trade-off between artificial dissipation, needed to stabilize the calculations, and accuracy is hard to achieve. Commonly used numerical scheme such as WENO guarantee robust results at the expense of loss of accuracy near the cut-off wavenumber and lack a mathematical foundation for compressible subgrid-scale turbulence modeling. Recent breakthrough in compressible LES modeling in Scalo’s group allows the simulation of transitional and turbulent hypersonic flow, with and without shocks, with a common numerical approach. The current proposal aims to extend this approach to incorporate aerothermochemistry effects and seek validation against experiments.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Fluid Modelling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
Desired experience:
Computational skills, Python, C++, GPU architectures (Kokkos)
School/Dept.:
School of Mechanical Engineering
Professor:
Carlo
Scalo
Home-based heat therapy in older adults with type 2 diabetes
Description:
Our research project focuses on developing non-invasive, practical therapies to improve glycemic control, vascular function, and physical function in older adults with type 2 diabetes (T2D), who are at elevated risk for cardiovascular disease and disability. Current therapeutic options, such as exercise, are underutilized by this population due to poor adherence. Therefore, we are exploring the potential of home-based heat therapy as a novel approach.
Heat therapy has shown promise in improving metabolic health, blood glucose regulation, and skeletal muscle function in both animal models and humans. In this project, we aim to establish the feasibility, safety, and efficacy of 12 weeks of home-based leg heat therapy using water-circulating trousers in older adults with T2D. Participants will be randomized to either the heat therapy group (90 minutes daily at 42ºC) or a sham control group (33ºC). We will evaluate compliance, blood glucose regulation (HbA1c, insulin sensitivity), body composition, leg strength, and physical function. Additionally, we will assess endothelial function using flow-mediated dilation (FMD) of the popliteal artery. Endothelial dysfunction is an early indicator of atherosclerosis and is strongly correlated with cardiovascular risk factors, including T2D.
This project offers undergraduate students the opportunity to contribute to cutting-edge research related to cardiovascular disease and diabetes, gain hands-on experience with clinical assessments, and work alongside a multidisciplinary team. The findings from this pilot study will provide critical insights into the therapeutic potential of HT and support future larger-scale trials aimed at improving health outcomes for older adults with T2D.
Heat therapy has shown promise in improving metabolic health, blood glucose regulation, and skeletal muscle function in both animal models and humans. In this project, we aim to establish the feasibility, safety, and efficacy of 12 weeks of home-based leg heat therapy using water-circulating trousers in older adults with T2D. Participants will be randomized to either the heat therapy group (90 minutes daily at 42ºC) or a sham control group (33ºC). We will evaluate compliance, blood glucose regulation (HbA1c, insulin sensitivity), body composition, leg strength, and physical function. Additionally, we will assess endothelial function using flow-mediated dilation (FMD) of the popliteal artery. Endothelial dysfunction is an early indicator of atherosclerosis and is strongly correlated with cardiovascular risk factors, including T2D.
This project offers undergraduate students the opportunity to contribute to cutting-edge research related to cardiovascular disease and diabetes, gain hands-on experience with clinical assessments, and work alongside a multidisciplinary team. The findings from this pilot study will provide critical insights into the therapeutic potential of HT and support future larger-scale trials aimed at improving health outcomes for older adults with T2D.
Campus:
West Lafayette
Research categories:
Cardiovascular Disease Research, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
The undergraduate mentee will be expected to actively contribute to various aspects of the project, gaining exposure to cardiovascular and metabolic research. Key responsibilities include:
1. Engagement in Research Design and Protocols:
o The mentee will be introduced to study design, randomization, and regulatory aspects, including ethical considerations.
2. Participant Interaction and Data Collection:
o The mentee will assist in participant recruitment, scheduling, and data collection, including measurements of blood pressure, muscle strength, vascular function, and functional tests (e.g., 6-minute walk test).
3. Laboratory and Clinical Techniques:
o The mentee will gain hands-on experience with flow-mediated dilation (FMD) assessments, isokinetic dynamometry, and dual-energy X-ray absorptiometry (DEXA) for body composition analysis.
4. Data Management and Analysis:
o Data entry, cleaning, and preliminary analysis will be performed under supervision, contributing to the interpretation of outcomes related to glycemic control, vascular function, and physical performance.
5. Collaboration and Communication:
o Active participation in lab meetings, presentation of findings, and collaboration with the research team.
School/Dept.:
Health and Kinesiology
Professor:
Bruno
Tesini Roseguini
More information: https://hhs.purdue.edu/directory/bruno-roseguini/
How does a bacterial virulence protein manipulates the plant cytoskeleton
Description:
Description of project:
Throughout this 10-week program, the REU student will learn techniques integral to the field of molecular plant pathology through the study of virulence proteins (effectors) and canonical pattern-triggered immune (PTI) signaling pathways. Specifically, the REU student will assist in characterizing how the effector RipU from Ralstonia solanacearum strain K60 influences calcium signaling during plant immune responses. This work will include generating transgenic Arabidopsis thaliana lines via floral dipping and crossing, and testing the effect of RipU on canonical signaling pathways such as calcium and reactive oxygen species (ROS) signaling. Performing experiments to analyze host immune signaling will require the student to learn plant transformation techniques, confocal microscopy, image analysis/quantification, and a variety of supplemental techniques that are standard across the field of molecular biology. Additionally, the student will gain an understanding of canonical host immune responses in plants and how to quantify and observe them. Overall, the goal of this project is to teach the REU student standard techniques used in molecular plant pathology and provide a preliminary understanding of plant-pathogen interactions.
Learning outcomes:
Throughout this 10-week program we anticipate that the REU student will…
1. Listen, be curious, and ask questions!
2. Read all provided materials.
3. Attend weekly/bi-weekly meetings with PI and graduate student mentor.
4. Attend weekly/bi-weekly lab meetings.
5. Clearly communicate with graduate student mentor and PI.
6. Contribute to a positive workplace environment.
By the end of this 10-week program we expect that the REU student will…
1. Be familiar with basic concepts of plant pathology and plant-pathogen interactions.
2. Know canonical host immune responses and the assays used to measure these responses.
3. Be familiar with Ralstonia solancearum (e.g., lifecycle and lifestyle, virulence factors).
4. Understand how to generate robust experimental designs and research questions.
5. Think critically about given project and contribute intellectually to discussions.
6. Learn techniques and skills integral to the study of molecular plant pathology.
7. Present findings during a weekly lab meeting
Throughout this 10-week program, the REU student will learn techniques integral to the field of molecular plant pathology through the study of virulence proteins (effectors) and canonical pattern-triggered immune (PTI) signaling pathways. Specifically, the REU student will assist in characterizing how the effector RipU from Ralstonia solanacearum strain K60 influences calcium signaling during plant immune responses. This work will include generating transgenic Arabidopsis thaliana lines via floral dipping and crossing, and testing the effect of RipU on canonical signaling pathways such as calcium and reactive oxygen species (ROS) signaling. Performing experiments to analyze host immune signaling will require the student to learn plant transformation techniques, confocal microscopy, image analysis/quantification, and a variety of supplemental techniques that are standard across the field of molecular biology. Additionally, the student will gain an understanding of canonical host immune responses in plants and how to quantify and observe them. Overall, the goal of this project is to teach the REU student standard techniques used in molecular plant pathology and provide a preliminary understanding of plant-pathogen interactions.
Learning outcomes:
Throughout this 10-week program we anticipate that the REU student will…
1. Listen, be curious, and ask questions!
2. Read all provided materials.
3. Attend weekly/bi-weekly meetings with PI and graduate student mentor.
4. Attend weekly/bi-weekly lab meetings.
5. Clearly communicate with graduate student mentor and PI.
6. Contribute to a positive workplace environment.
By the end of this 10-week program we expect that the REU student will…
1. Be familiar with basic concepts of plant pathology and plant-pathogen interactions.
2. Know canonical host immune responses and the assays used to measure these responses.
3. Be familiar with Ralstonia solancearum (e.g., lifecycle and lifestyle, virulence factors).
4. Understand how to generate robust experimental designs and research questions.
5. Think critically about given project and contribute intellectually to discussions.
6. Learn techniques and skills integral to the study of molecular plant pathology.
7. Present findings during a weekly lab meeting
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biotechnology Data Insights, Cellular Biology, Ecology and Sustainability, Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
No specific courses, but at least one college-level course in biology or microbiology, or AP Bio in high school, is recommended
School/Dept.:
Department of Botany and Plant Pathology
Professor:
Anjali
Iyer-Pascuzzi
How does the assembly of PFAS affect its transport in soil
Description:
Per- and polyfluoroalkyl substances (PFAS) continue to present a management challenge, particularly at locations where aqueous film-forming foams (AFFFs) were used heavily. To improve the long-term management of AFFF-impacted sites, it is critical to understand the role of PFAS self-assembly in the source zones and the factors that impact this self-assembly on the long-term release and transport of PFAS to groundwater. This project aims to evaluate relevant environmental factors such as PFAS concentration and type, hydrocarbon co-surfactants, ionic matrix, and soil chemical/physical properties that affect the formation and stability of supramolecular systems within AFFF-source zones. We aim to understand the self-assembly of PFAS mixtures in solution and at the air/water and water/soil interfaces in the presence of hydrocarbon co-surfactants, co-solvents, and inorganic ions during wetting and drying cycles. We are seeking a highly motivated undergraduate student to work on this project. The primary responsibilities of the student will be to characterize the PFAS assembly in solution and surfaces using dynamic light scattering, tensiometry, and optical microscopy. Students with a background in chemistry, physics, environmental science/engineering, and chemical engineering are well-suited to work on the project. MSE students with a strong interest in solving environmental issues are also encouraged.
Campus:
West Lafayette
Research categories:
Ecology and Sustainability, Environmental Characterization, Fluid Modelling and Simulation, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Chemistry
- Physics
- Materials Engineering
- Chemical Engineering
School/Dept.:
School of Materials Engineering
Professor:
Carlos
Martinez
Human Body Communication IC & System Design
Description:
The students will work on developing state-of-the-art systems to characterize and do research on and demonstrate communication and powering capabilities using human body.
Campus:
West Lafayette
Research categories:
Human Factors, Internet of Things (IoT), Medical Science and Technology, Microelectronics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering
- Computer Engineering
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Shreyas
Sen
More information: https://www.youtube.com/watch?v=fRfdrkzlCwI
Hyaluronic Acid Gels for Tissue Mimics
Description:
Hyaluronic acid is one of the predominant structural components of the extracellular matrix of brain tissue. This project will work on developing crosslinking chemistries to produce uniform hydrogels utilizing hyaluronic acid to serve as tissue mimics. These mimics will serve as robust platforms to understand drug transport.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Julie
Liu
More information: https://engineering.purdue.edu/LiuGroup
Hypersonic wind tunnel and constrained ballistic model design and analysis
Description:
Design a small-scale test article model with instrumentation (and data logging, as needed) in order to provide aerodynamic and aerothermal data on a simple, easily repeatable shape with analytical and/or numerical solutions. This small test article will be suitable to be manufactured, instrumented, and tested in the Boeing/AFOSR Mach 6 Quiet Tunnel, and analyzed for suitability for use in a high-G-load range environment. The student(s) will work relatively independently on this project, which will involve sustained use of CAD and Finite Element Analysis (FEA) tools, as well as beginning to learn/apply some CFD tools.
Campus:
West Lafayette
Research categories:
Fluid Modelling and Simulation, Material Modeling and Simulation
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Aeronautical and Astronautical Engineering
- Mechanical Engineering
Desired experience:
Required: Good performance in AAE or ME classes as well as math and physics. Experience with MATLAB. Experience with CAD software. Experience with Finite Element Analysis tools. Ability to work towards goals independently. Willingness to learn CFD tools. Desired: Experience with Labview or other data acquisition systems, pressure transducers, high speed cameras, Overleaf/LaTeX, vacuum pumps and high-pressure systems.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Joseph
Jewell
More information: https://engineering.purdue.edu/AAE/people/ptProfile?resource_id=221718
Ice Stability in the Mid-Latitudes of Mars
Description:
There are numerous lines of evidence for massive ice deposits in the mid-latitudes of Mars. In this project, we will estimate the stability of this ice over time to try to find conditions that match the geologic signatures of the ice. The student will use a computer code to run simulations of Mars temperatures at different orbital and axial conditions at present and in the past. Using these temperatures, they will then calculate ice sublimation rates to investigate the timescales that the ice could be retreating. We will also calculate the subsequent thickness of a lag deposit of the dust that gets left behind when the ice retreats. We will work to match the scales of modeled ice loss with the geologic evidence for this retreat. The project will shed light on the interplay between Mars' orbit, climate, and geology.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Geology and Geophysics
- Aeronautical and Astronautical Engineering
- Physics
- Planetary Sciences
Desired experience:
Experience and comfort with computer coding, (astro)physics coursework, geology coursework
School/Dept.:
Earth, Atmospheric and Planetary Sciences
Professor:
Ali
Bramson
More information: https://www.eaps.purdue.edu/bramson/index.html
Identification of enzymes capable of cyclizing small peptides
Description:
Develop biocatalysts to cyclize small peptides
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology, Chemical Catalysis and Synthesis
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Elizabeth
Parkinson
Identification of ligands for TetR repressors of natural product biosynthetic gene clusters
Description:
We are identifying ligands for bioinformatically predicted repressors of natural product biosynthetic gene clusters. The student will develop GFP-plasmids for the study of the repressors and potential ligands
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Betsy
Parkinson
Identifying novel drugs for the treatment of spinal cord injuries
Description:
Spinal cord injury (SCI) is a significant health problem affecting over 250,000 people in the USA. There are no effective treatments that enable full recovery of lost functions. The goal of this project will be to use zebrafish larvae to identify FDA-approved drugs that improve functional recovery and axonal regeneration after SCI. The project has two major goals: 1. Validation of drug candidates identified in our previous research. 2. Continuation of the drug library screen to identify additional drugs. The SURF student will learn how to perform injuries, drug treatments, behavioral assay, immunolabeling, and imaging. We expect to identify novel compounds that ultimately could be used to treat human SCI patients.
Campus:
West Lafayette
Research categories:
Cellular Biology, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biology
- Neurobiology and Physiology
- Cell Molecular and Developmental Biology
- Biomedical Engineering
- Biochemistry
Desired experience:
basic biology courses, cell biology, if possible experience working with zebrafish
School/Dept.:
Biological Sciences
Professor:
Daniel
Suter
More information: https://suterlab.bio.purdue.edu/
Imaging and Machine Learning of Defects in Semiconductors
Description:
The student will work on using x-ray imaging to detect defects in through silicon vias (TSVs) and other times of interconnect in 3D heterogeneously integrated packages. Analysis of the data, using machine learning algorithms, will be conducted to accurately and efficiently identify defects in advanced packages.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Big Data/Machine Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Materials Engineering
- Mechanical Engineering
- Computer Engineering
School/Dept.:
School of Materials Engineering
Professor:
Nik
Chawla
Impact of Magnesium L-threonate on neuronal gene expression
Description:
This project will explore the gene expression changes resulting from magnesium threonate exposure in stem cell derived neurons.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Biological Sciences
Professor:
Matthew
Tegtmeyer
Improving Engineering Education through the use of Large Language Models (LLMs)
Description:
Description:
Objective: To develop effective LLMs platforms to enhance engineering education through understanding of complex concepts.
Key Tasks:
1. Compile a comprehensive Biological Engineering Process Design related knowledge base
2. Use LLM to develop a curriculum tutor learning approach
3. Evaluate model performance and task implementation
4. Compare general-purpose LLMs with traditional teaching methods
Skills Required: Background in Biological Engineering and interest in Machine Learning, Natural Language Models: Exposure to LLMs and interest in advancing their applications in educational settings 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.
Outcome: Specialized LLMs 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
Objective: To develop effective LLMs platforms to enhance engineering education through understanding of complex concepts.
Key Tasks:
1. Compile a comprehensive Biological Engineering Process Design related knowledge base
2. Use LLM to develop a curriculum tutor learning approach
3. Evaluate model performance and task implementation
4. Compare general-purpose LLMs with traditional teaching methods
Skills Required: Background in Biological Engineering and interest in Machine Learning, Natural Language Models: Exposure to LLMs and interest in advancing their applications in educational settings 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.
Outcome: Specialized LLMs 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
Campus:
West Lafayette
Research categories:
Chemical Unit Operations, Learning and Evaluation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biological Engineering - multiple concentrations
- Chemical Engineering
Desired experience:
Completion of Sophomore, Junior, or Senior year
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Martin
Okos
Improving intelligent tutoring system responsivity to humans through haptic feedback
Description:
We are looking for an undergraduate researcher to support development of a cognitively-aware intelligent tutoring system (ITS) for a psychomotor task by incorporating haptic feedback. The psychomotor task is learning how to land a quadrotor manually in a 2D quadrotor simulator module. The student will gain experience in the following areas:
- Sensor fusion
- Synthesizing optimal control policies or algorithms that assist humans by responding to human behavior
- Improving generated feedback to assist in human learning
This project requires experience with Python and MATLAB. While not required, prior experience with psycho-physiological/behavioral sensors (E.g. fNIRS, ECG, eye gaze tracking) would be beneficial.
- Sensor fusion
- Synthesizing optimal control policies or algorithms that assist humans by responding to human behavior
- Improving generated feedback to assist in human learning
This project requires experience with Python and MATLAB. While not required, prior experience with psycho-physiological/behavioral sensors (E.g. fNIRS, ECG, eye gaze tracking) would be beneficial.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Human Factors, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
- Mechanical Engineering
- Electrical Engineering
- Industrial Engineering
- Aeronautical and Astronautical Engineering
School/Dept.:
School of Mechanical Engineering
Professor:
Neera
Jain
More information: https://engineering.purdue.edu/JainResearchLab/
Improving small molecule drug oral dissolution kinetics via drug-polymer salts
Description:
The poor water solubility of many small-molecule drugs restricts their use as therapeutics because these molecules exhibit low oral bioavailability when taken by mouth. This project will explore the preparation of amorphous drug-polymer salts (ADPS) formed from ionic interactions between weakly basic drugs and polyanionic polymers as a strategy to reduce drug crystallinity and improve oral dissolution kinetics. An emphasis will be placed on process development, material characterization, and continuous production of ADPS.
Campus:
West Lafayette
Research categories:
Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Kurt
Ristroph
More information: https://www.ristrophlab.com/
In vitro MRI phantoms for MRI data harmonization
Description:
Four-dimensional Flow Magnetic Resonance Imaging (4D flow MRI) is an emerging non-invasive tool for measuring blood flow in the cardiovascular system. It provides velocity measurements that can be processed to quantify secondary flow aspects such as pressure and wall shear stress. Understanding these aspects is crucial for studying the progression of various cardiovascular diseases. However, its implementation across different MRI vendors poses a challenge due to intervendor differences in image resolution and signal-to-noise ratio (SNR), hindering longitudinal analysis. Data harmonization of MRI measurements can help overcome this limitation, enabling intervendor comparison and analysis of 4D flow MRI data. Selected student will contribute to a multidisciplinary project focused on developing flow phantoms for installation into flow loops. These phantoms will be imaged during the development of a data harmonization method. The goal is to create phantoms that are usable both in MRI and in benchtop flow loops for particle image velocimetry (PIV), a flow visualization method that can validate the MRI results. Selected student will gain research skills in multiple research areas related to experiment design, model fabrication, experimental fluid mechanics, and non-invasive medical imaging.
Campus:
West Lafayette
Research categories:
Cardiovascular Disease Research, Fluid Modelling and Simulation, Medical Science and Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
- Mechanical Engineering
Desired experience:
The indicated majors are preferred.
School/Dept.:
School of Mechanical Engineering
Professor:
Pavlos
Vlachos
More information: https://vlachosresearch.org
In-vivo testing of multichannel low frequency alternating current stimulation for slowing/blocking, and activating peripheral nerves
Description:
This project aims to determine whether two channel stimulation using Low Frequency Alternating Currents to elicit nerve conduction slowing/block or activation can be used to overcome the episodic block/activation seen with single channel LFAC. The student will develop and validate software to enable independent control of the two channels of stimulation and coordinate the stimulation with recording. If possible, an OpenEphys compliant plug-in will be developed to enable experimental software control and recording within the OpenEphys environment.
Campus:
Indianapolis
Research categories:
Biological Characterization and Imaging, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
Desired experience:
Computer programming
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Ken
Yoshida
More information: https://engineering.purdue.edu/Bioellab/
Indirect Water Reuse: Understanding How Drinking Water is Affected by Upstream Activities
Description:
When we release treated wastewater into a river, we are indirectly affecting the drinking water intake downstream. For many emerging water quality constituents, including PFAS and antibiotic resistance genes, we do not yet understand their transport through our natural water systems. In this project, we will explore these dynamics, pairing water quality analysis with watershed modeling. The student will assist graduate students with validating biological and analytical chemistry methods to quantify how indirect water reuse may affect drinking water quality and exposures. This project will require collaboration across environmental and ecological engineering, environmental and natural resources engineering, agricultural and biological engineering, and agronomy.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Ecology and Sustainability, Engineering the Built Environment, Environmental Characterization, Fluid Modelling and Simulation
Preferred major(s):
- No Major Restriction
Desired experience:
Pipetting skills and wet lab experience is preferred.
School/Dept.:
Division of Environmental and Ecological Engineering
Professor:
Caitlin
Proctor
Injectable biomaterials for cardiac tissue engineering applications
Description:
Reinforcement of the myocardial wall with biomaterials can attenuate left-ventricular wall thinning and preserve cardiac function after myocardial infarction. However, introducing biomaterials into the myocardial wall is a critical challenge, since most naturally-derived and synthetic biomaterials are incompatible with delivery devices such as catheters. Similarly, injectable biomaterials including hydrogels and infusible grafts can disperse away after injection, reducing their protective and reparative effects. The goal of this project is to develop shear-thinning and self-healing biomaterials that are easy to inject, have suitable mechanical properties to support their retention at the site of injury, and have a porous structure to promote cell and tissue ingrowth during repair.
Leveraging the current expertise in the Qazi lab, the student will use flow-focusing droplet-microfluidics and a photocurable polymer to develop injectable hydrogels. Mechanical properties will be controlled by varying crosslinker ratio, polymer concentration, and will be characterized using a dynamic mechanical analyzer. Hydrogel microparticles will be fabricated using a water-in-oil microfluidics device with downstream exposure to UV light. The particles will be assembled into injectable hydrogels via packing at high centrifugation speeds. Oscillatory shear rheology will be used to characterize shear-thinning and self-healing properties. Confocal microscopy will be used to characterize structural features such as porosity and inter-particle pore size. Cells will be mixed with granular hydrogels and injected into a custom-made mold to investigate cell adhesion, proliferation, and differentiation. Cells will be fixed and stained with markers for cytoskeletal proteins. Proliferation will be quantitatively characterized using metabolic assays.
This project will result in injectable hydrogels with rigorously characterized mechanical, biophysical, and cell-instructive properties for applications in cardiac tissue engineering.
Leveraging the current expertise in the Qazi lab, the student will use flow-focusing droplet-microfluidics and a photocurable polymer to develop injectable hydrogels. Mechanical properties will be controlled by varying crosslinker ratio, polymer concentration, and will be characterized using a dynamic mechanical analyzer. Hydrogel microparticles will be fabricated using a water-in-oil microfluidics device with downstream exposure to UV light. The particles will be assembled into injectable hydrogels via packing at high centrifugation speeds. Oscillatory shear rheology will be used to characterize shear-thinning and self-healing properties. Confocal microscopy will be used to characterize structural features such as porosity and inter-particle pore size. Cells will be mixed with granular hydrogels and injected into a custom-made mold to investigate cell adhesion, proliferation, and differentiation. Cells will be fixed and stained with markers for cytoskeletal proteins. Proliferation will be quantitatively characterized using metabolic assays.
This project will result in injectable hydrogels with rigorously characterized mechanical, biophysical, and cell-instructive properties for applications in cardiac tissue engineering.
Campus:
West Lafayette
Research categories:
Cardiovascular Disease Research
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Be curious, professional, and excited to learn!
Background in materials science, microfluidics, microscopy, immunostaining, or cell culture would be a bonus but are not required.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Taimoor
Qazi
More information: https://engineering.purdue.edu/BME/People/ptProfile?resource_id=273232
Integrated Biochemical and Physicochemical Processes to Recover Critical Metals from Municipal Solid Waste in Landfills
Description:
In the United States, landfills containing municipal solid waste (MSW) represent a large repository of critical metals, particularly originating from discarded electronic waste (e-waste). To date, no known technologies have successfully recovered critical metals from landfills, which is uniquely challenged by the inability to directly access deposited materials. Landfill leachates present an opportunity to capture critical metals from the aqueous stream. This SURF project will support a larger, integrated effort by addressing these specific research objectives: i) characterize the currently unknown critical metal content in existing US landfill leachates, and ii) elucidate the complex chemistry of metals in landfill leachates. No known studies have reported valued metal concentrations beyond those of heavy metals , , in US landfill leachates. Additionally, metal speciation is highly dynamic, since the landfill redox, pH, and ligand composition change over time as they mature and MSW undergoes biological, chemical, and physical transformations. The lack of knowledge of chemical speciation of each metal is a critical technical gap that will inhibit separation and recovery efforts.
The SURF student will work with graduate student and faculty mentors on the following objectives and tasks:
Elemental analysis: landfill leachate samples will be processed (including microwave assisted digestion) and elements will be detected and quantified with Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES).
Water quality parameters: Total organic and inorganic carbon (HCO3- and CO32-) will be measured by a TOC analyzer, and dominant organic ligands (carboxylic acids and phenolic groups), measured via acid-base titration. These water quality parameters strongly influence the speciation of the elements.
Modeling chemical speciation:. The typical composition of landfill leachate will include inorganic ligands, such as hydroxide (OH^-), chloride (Cl^-), bicarbonate 〖(HCO〗_3^-), carbonate (CO_3^(2-)), ammonia (NH_3), sulfate (SO_4^(2-)) and phosphate (PO_4^(3-)), along with many organic ligands, particularly short chain carboxylic acids. We will model metal speciation with tools such as Visual MINTEQ or MINEQL+ and compare: greatest to least MT among the seven metals, comparisons of chemical speciation (uncomplexed versus complexed), and ranges of conditions (pH, redox, ionic strength). Analytical results from Thrusts 1 and 2, including measurement of various ligand concentrations, the pH, the ionic strength, and the MT, will enable detailed predictions of species and concentrations.
The outcomes from this research will help with assessing the technical and economic feasibility of recovering critical elements and precious metals from MSW landfills.
The SURF student will work with graduate student and faculty mentors on the following objectives and tasks:
Elemental analysis: landfill leachate samples will be processed (including microwave assisted digestion) and elements will be detected and quantified with Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES).
Water quality parameters: Total organic and inorganic carbon (HCO3- and CO32-) will be measured by a TOC analyzer, and dominant organic ligands (carboxylic acids and phenolic groups), measured via acid-base titration. These water quality parameters strongly influence the speciation of the elements.
Modeling chemical speciation:. The typical composition of landfill leachate will include inorganic ligands, such as hydroxide (OH^-), chloride (Cl^-), bicarbonate 〖(HCO〗_3^-), carbonate (CO_3^(2-)), ammonia (NH_3), sulfate (SO_4^(2-)) and phosphate (PO_4^(3-)), along with many organic ligands, particularly short chain carboxylic acids. We will model metal speciation with tools such as Visual MINTEQ or MINEQL+ and compare: greatest to least MT among the seven metals, comparisons of chemical speciation (uncomplexed versus complexed), and ranges of conditions (pH, redox, ionic strength). Analytical results from Thrusts 1 and 2, including measurement of various ligand concentrations, the pH, the ionic strength, and the MT, will enable detailed predictions of species and concentrations.
The outcomes from this research will help with assessing the technical and economic feasibility of recovering critical elements and precious metals from MSW landfills.
Campus:
West Lafayette
Research categories:
Ecology and Sustainability, Engineering the Built Environment, Environmental Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Division of Environmental and Ecological Engineering
Professor:
Caitlin
Proctor
Integrated Biochemical and Physicochemical Processes to Recover Critical Metals from Municipal Solid Waste in Landfills
Description:
In the United States, landfills containing municipal solid waste (MSW) represent a large repository of critical metals, particularly originating from discarded electronic waste (e-waste). To date, no known technologies have successfully recovered critical metals from landfills, which is uniquely challenged by the inability to directly access deposited materials. Landfill leachates present an opportunity to capture critical metals from the aqueous stream. This SURF project will support a larger, integrated effort by addressing these specific research objectives: i) characterize the currently unknown critical metal content in existing US landfill leachates, and ii) elucidate the complex chemistry of metals in landfill leachates. No known studies have reported valued metal concentrations beyond those of heavy metals , , in US landfill leachates. Additionally, metal speciation is highly dynamic, since the landfill redox, pH, and ligand composition change over time as they mature and MSW undergoes biological, chemical, and physical transformations. The lack of knowledge of chemical speciation of each metal is a critical technical gap that will inhibit separation and recovery efforts.
The SURF student will work with graduate student and faculty mentors on the following objectives and tasks:
Elemental analysis: landfill leachate samples will be processed (including microwave assisted digestion) and elements will be detected and quantified with Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES).
Water quality parameters: Total organic and inorganic carbon (HCO3- and CO32-) will be measured by a TOC analyzer, and dominant organic ligands (carboxylic acids and phenolic groups), measured via acid-base titration. These water quality parameters strongly influence the speciation of the elements.
Modeling chemical speciation:. The typical composition of landfill leachate will include inorganic ligands, such as hydroxide (OH^-), chloride (Cl^-), bicarbonate 〖(HCO〗_3^-), carbonate (CO_3^(2-)), ammonia (NH_3), sulfate (SO_4^(2-)) and phosphate (PO_4^(3-)), along with many organic ligands, particularly short chain carboxylic acids. We will model metal speciation with tools such as Visual MINTEQ or MINEQL+ and compare: greatest to least MT among the seven metals, comparisons of chemical speciation (uncomplexed versus complexed), and ranges of conditions (pH, redox, ionic strength). Analytical results from including measurement of various ligand concentrations, the pH, the ionic strength, and the MT, will enable detailed predictions of species and concentrations.
The outcomes from this research will help with assessing the technical and economic feasibility of recovering critical elements and precious metals from MSW landfills.
The SURF student will work with graduate student and faculty mentors on the following objectives and tasks:
Elemental analysis: landfill leachate samples will be processed (including microwave assisted digestion) and elements will be detected and quantified with Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES).
Water quality parameters: Total organic and inorganic carbon (HCO3- and CO32-) will be measured by a TOC analyzer, and dominant organic ligands (carboxylic acids and phenolic groups), measured via acid-base titration. These water quality parameters strongly influence the speciation of the elements.
Modeling chemical speciation:. The typical composition of landfill leachate will include inorganic ligands, such as hydroxide (OH^-), chloride (Cl^-), bicarbonate 〖(HCO〗_3^-), carbonate (CO_3^(2-)), ammonia (NH_3), sulfate (SO_4^(2-)) and phosphate (PO_4^(3-)), along with many organic ligands, particularly short chain carboxylic acids. We will model metal speciation with tools such as Visual MINTEQ or MINEQL+ and compare: greatest to least MT among the seven metals, comparisons of chemical speciation (uncomplexed versus complexed), and ranges of conditions (pH, redox, ionic strength). Analytical results from including measurement of various ligand concentrations, the pH, the ionic strength, and the MT, will enable detailed predictions of species and concentrations.
The outcomes from this research will help with assessing the technical and economic feasibility of recovering critical elements and precious metals from MSW landfills.
Campus:
West Lafayette
Research categories:
Chemical Unit Operations, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Environmental and Ecological Engineering
- Chemical Engineering
- Civil Engineering
- Biological Engineering - multiple concentrations
- Environmental and Natural Resources Engineering
- Chemistry
- Natural Resources and Environmental Science (multiple concentrations)
Desired experience:
Preferred, but not required: course work in environmental engineering, analytical chemistry, water chemistry.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Amisha
Shah
More information: https://engineering.purdue.edu/EEE/AboutUs/News/2024/NSFCollaborativeResearchAward
Intelligent Undersea Network Systems
Description:
The focus of this project is on developing algorithms for networked intelligence, surveillance and reconnaissance (ISR) systems that are specifically tailored for the challenges posted by adversarial undersea environments. Students participating in the program will work with a team of graduate students, postdocs, and professors towards achieving these goals. This includes investigating algorithms for (1) path planning and communications/networking among autonomous underwater vehicles (AUVs) and surface stations, and (2) distributed computation to allow the individual agents to parse locally gathered information into situational awareness. The student will gain a concrete understanding important contemporary undersea ISR applications, including monitoring choke points in maritime settings.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Internet of Things (IoT), Mobile Computing
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Electrical Engineering
- Computer Engineering
- Mathematics
- Computer Science
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Christopher
Brinton
More information: https://www.cbrinton.net/
Investigating the effects of statins on cholesterol transport in neurons and astrocytes
Description:
This project will explore how exposure to common cholesterol lowering medications impact cross-talk between crucial neural cell types. Our goal is to identify sources of well-established side effects of chronic statin use including brain fog.
Campus:
West Lafayette
Research categories:
Cellular Biology, Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Biological Sciences
Professor:
Matthew
Tegtmeyer
IoT sensor development for trailer monitoring
Description:
This project involves working with a partnering Korean company for IoT sensor development for temperature and sound monitoring of a trailer.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics, Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Electrical Engineering
School/Dept.:
School of Mechanical Engineering
Professor:
Martin
Jun
Kagome embeddings on quantum hardware in search of new quantum phases of matter
Description:
Kagome and Triangular lattices form the basis of a set of geometrically frustrated spin lattices where all the constraints of the Hamiltonian are not satisfied. This leads to unusual ground and excited states which show unique features such as magnetization plateaus and spin glass states. The problem becomes much more interesting in the quantum realm where quantum fluctuations adds to the frustration to lead to several new and exotic phases which are purely 'quantum', i.e. with no classical analog. These phases not only are of great interest to the condensed matter community as they are realized in real materials, but also in the quantum computing community as they help to benchmark the quantum hardware. In this project, we attempt to understand such interesting phases of matter using quantum computers accessible at Purdue University. We test the results against classical simulations to validate the result for small number of spins, and seek to unearth newer phases when the number of spins are larger. The goal of the project is to come up with predictions for Ising triangular lattices in a transverse field and deduce their phase transitions using quantum annealing and quenching in the pseudospin basis.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Must be good and passionate in programming. Knowledge of Julia or Python is preferred. Graph theory or condensed matter or Quantum coursework are preferred, but not necessary.
School/Dept.:
Physics and Astronomy
Professor:
Arnab
Banerjee
Large Language Models (LLMs) Fine-Tuning for Biological Engineering Education
Description:
Objective: Fine-tune LLMs to enhance understanding of complex concepts.
Key Tasks:
1. Compile a comprehensive Biological Engineering Process Design related knowledge base
2. Fine-tune open-source LLM using a curriculum learning approach
3. Evaluate model performance on advanced evaluation tasks
4. Compare with general-purpose LLMs and traditional teaching methods
Key Tasks:
1. Compile a comprehensive Biological Engineering Process Design related knowledge base
2. Fine-tune open-source LLM using a curriculum learning approach
3. Evaluate model performance on advanced evaluation tasks
4. Compare with general-purpose LLMs and traditional teaching methods
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology, Learning and Evaluation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Agricultural Engineering
- Biological Engineering - multiple concentrations
- Food Science
- Pharmacy
- Biology
Desired experience:
Skills Required: Machine Learning, Natural Language Processing
Desired: Exposure to LLMs and interest in advancing their applications in educational settings
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.
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Dharmendra
Saraswat
Laser Ignition Imaging using an IR Spectrometer
Description:
The student will learn about safe laboratory operations in a large propulsion laboratory including the use of pressure regulators, flowmeters. broadband transducers, multiwavelength spectrometers and cameras. The student will learn high speed data acquisition and processing, noise reduction, advanced filtering, and machine learning, image analysis and image enhancement. The student will analyze the data utilizing fundamentals of thermal sciences, statistics, computations, and Artificial Intelligence.
Campus:
West Lafayette
Research categories:
Deep Learning, Energy and Environment, Thermal Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- AAE or ME
Desired experience:
Basic Instrumentation, Laser Operations and Laser Safety, Handling of High Pressure Gases, Leak Testing, Maintenance of Flow Instrumentation and Plumbing.
School/Dept.:
School of Mechanical Engineering
Professor:
Jay
Gore
Light scattering simulation from periodic diffraction grating utilizing SCATMECH library
Description:
UG students will work with research sponsor on light scattering simulation from diffraction grating utilizing an open source library such as SCATMECH library.
Campus:
West Lafayette
Research categories:
Microelectronics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
Desired experience:
Visual studio + UNIX\LINUX experience, Python coding experience
School/Dept.:
School of Mechanical Engineering
Professor:
Euiwon
Bae
Light-Sheet Imaging and Imaging Processing of Protein and Ca Signaling in Developing Tissues
Description:
This project focuses on biological imaging and image processing, using cutting-edge light-sheet microscopy to generate large 3D datasets of protein and Ca2+ signaling in developing tissues (Drosophila wing discs and zebrafish embryos). To address memory limitations posed by the large file sizes generated by light-sheet microscopes, the data will be downsampled and processed by a trained Neural network model for nuclei segmentation and subsequently upscaled using separated neural network-based models, thus restoring high-resolution details. Students will gain hands-on experience in the experimental aspects of light-sheet imaging and the computational tasks involved in training and applying deep-learning models for image reconstruction and analysis. As part of the EMBRIO Institute’s collaborative research efforts, participants will also be required to report on progress and take part in EMBRIO activities, including the annual meeting and project poster presentations.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Deep Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
David
Umulis
Light-assisted fabrication of biomaterials
Description:
Our lab is interested in developing new biomaterials for applications in regenerative medicine. We work with synthetic and natural materials including hyaluronic acid, alginate, and decellularized tissues. To enable their rapid and user-defined fabrication into biomaterials, the materials are chemically modified to enable light-mediated crosslinking. The fabricated biomaterials can be tailored towards various applications. Examples of these include injectable biomaterials for tissue regeneration and in vitro platforms to probe cell behavior.
The SURF projects will explore new directions in the light-assisted fabrication of biomaterials with a special emphasis on controlling physical, mechanical, and biochemical properties to guide cell and tissue function. SURF students will be trained on material synthesis, fabrication techniques, characterization methods, quantitative analysis of biomaterial properties, and in the design of experiments to probe specific hypotheses. These activities will involve a range of instruments, methodologies, and approaches including chemical modification, microscopy, cell culture, and animal experiments.
We are seeking motivated students who are interested in gaining hands-on skills in biomaterials development and applications in regenerative medicine. Those with prior experience in a wet-lab environment are especially encouraged to apply.
The SURF projects will explore new directions in the light-assisted fabrication of biomaterials with a special emphasis on controlling physical, mechanical, and biochemical properties to guide cell and tissue function. SURF students will be trained on material synthesis, fabrication techniques, characterization methods, quantitative analysis of biomaterial properties, and in the design of experiments to probe specific hypotheses. These activities will involve a range of instruments, methodologies, and approaches including chemical modification, microscopy, cell culture, and animal experiments.
We are seeking motivated students who are interested in gaining hands-on skills in biomaterials development and applications in regenerative medicine. Those with prior experience in a wet-lab environment are especially encouraged to apply.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Cellular Biology, Material Processing and Characterization, Medical Science and Technology
Citizenship requirements:
No citizenship requirements, U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Biomedical Engineering
- Mechanical Engineering
- Chemical Engineering
- Biological Engineering - multiple concentrations
Desired experience:
A minimum of 6 months of prior laboratory experience working with biomaterials, materials fabrication and/or characterization, cell culture, or related areas is preferred.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Taimoor
Qazi
More information: www.qazi-lab.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
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
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/
Lilly-Purdue Research Alliance Center project: nanoparticle formulation and process engineering
Description:
The Lilly-Purdue Research Alliance Center (LPRC) serves as an essential hub for collaborative exploration of new technologies that enhance every phase of pharmaceutical research, from discovery to delivery. The LPRC’s mission is to push the boundaries of science through collective learning and active engagement between Purdue and Lilly researchers. Currently, LPRC researchers at Purdue and Lilly are focused on genetic medicine, intrathecal delivery and nanoparticle drug delivery. This project is related to nanoparticle formulation techniques. The student will become familiar with industrially-relevant formulation strategies and evaluate their utilization as one unit operation that is part of larger processes.
Campus:
West Lafayette
Research categories:
Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Kurt
Ristroph
More information: https://www.ristrophlab.com/
Localized Deep Learning for Decentralized and Dynamic Environments
Description:
Despite being widely used, global end-to-end learning has several key limitations. It requires centralized computation, making it feasible only on a single device or a carefully synchronized cluster. This restricts its use on unreliable or resource-constrained devices, such as commodity hardware clusters or edge computing networks. Localized deep learning has the potential to develop highly decentralized, parallel, asynchronous, and fault-tolerant algorithms that can learn on heterogeneous hardware devices under dynamic conditions while maintaining comparable model performance. The long-term vision would be an "Internet of AI" where devices can continuously learn in any conditions.
REU participants will be part of a collaborative team focused on developing novel localized deep learning approaches. The goal is to create a learning algorithm with local objectives that learns rich unsupervised representations in a highly decentralized and fault-tolerant way. As one specific context, suppose a sensor network should be trained to detect a complex or global event such as anomalous activity over a large area of the wilderness. Each sensor has a very incomplete picture of the situation and can communicate with nearby sensors but cannot communicate with a global centralized server. The goal is to implement both width-parallel and depth-parallel learning on an unreliable set of sensor devices that have limited compute power. This project will focus on the fundamental aspects of novel local learning mechanisms in this highly decentralized environment.
REU participants will be part of a collaborative team focused on developing novel localized deep learning approaches. The goal is to create a learning algorithm with local objectives that learns rich unsupervised representations in a highly decentralized and fault-tolerant way. As one specific context, suppose a sensor network should be trained to detect a complex or global event such as anomalous activity over a large area of the wilderness. Each sensor has a very incomplete picture of the situation and can communicate with nearby sensors but cannot communicate with a global centralized server. The goal is to implement both width-parallel and depth-parallel learning on an unreliable set of sensor devices that have limited compute power. This project will focus on the fundamental aspects of novel local learning mechanisms in this highly decentralized environment.
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:
Passion for learning, Tenacity to persevere through great uncertainty, Strong experience with PyTorch, Ability to work for at least 2 semesters
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
David
Inouye
Low Temperature Electrocatalytic Manufacturing of Essential Chemicals: Understanding electrocatalytic interactions of propane in aqueous environments
Description:
This project will use unique electroanalytical equipment to characterize molecular interactions of propane at electrodes. It will explore the mechanistic, kinetic, and electrolyte effects that influence propane activation and reaction.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis, Energy and Environment
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Brian
Tackett
MD Simulation to Understand Mammalian AC1 Protein Activation
Description:
Adenylyl cyclases (ACs) are enzymes that convert ATP into the secondary messenger cAMP, essential for cell signaling. This research focuses on mammalian transmembrane AC1, significantly affecting brain functions like memory and pain perception. Understanding the mechanisms controlling AC1 could enhance drug development for neurological diseases. AC1 activation depends on calmodulin (CaM) and Gαs, but their structural effects on AC1 are unclear. The project will use molecular dynamics (MD) simulations and enhanced sampling to examine how CaM and Gαs influence AC1 structure, both independently and collectively. Given the AC1-CaM-Gαs system includes 1,624 residues, examining AC1 in a transmembrane context poses challenges. We will simulate four conditions: AC1 alone, with CaM, with Gαs, and with both. This research may identify new targets for modulating AC1 via small molecules and peptides, aiding drug development for neurological conditions.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Simulation and Technology, Chemical Catalysis and Synthesis, Deep Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Medicinal Chemistry and Molecular Pharmacology
Professor:
Jianing
Li
More information: https://web.ics.purdue.edu/~li4578/
Mantle Mineralogy on Venus
Description:
Silicate minerals are the building blocks of rocks on Earth and other rocky planets. Pressure increases as we dive deeper into a planet, past the surface, through the crust, and into the mantle. The silicate minerals that comprise these planetary layers have distinct chemistry and structure, and there are specific mineralogical changes with increasing pressure. These changes have been particularly well calibrated for Earth, Mars, and the Moon, and also relate to seismic observations for each planet. Students working on this project will conduct laboratory investigations into the mineralogical changes with pressure (depth) as it relates to the crust and mantle layering on Venus. Students will gain valuable experience with high-pressure experimental laboratory techniques, as well as methods of chemical and mineralogical analysis including Scanning Electron Microscopy (SEM) and X-Ray Diffraction (XRD).
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis, Composite Materials and Alloys, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Geology and Geophysics
- Planetary Sciences
- Chemistry
Desired experience:
The most competitive applicants will have relevant experience in geoscience and/or chemistry courses, particularly EAPS 243: Mineralogy, though this is not a requirement.
School/Dept.:
EAPS
Professor:
Kelsey
Prissel
Manufacturing studies in advanced composites
Description:
Manufacturing science of advanced composite materials involves the simulation of physics-based phenomena central to successful manufacturing of composite structures appropriate to the domain of fiber-reinforced, polymeric advanced composite systems and to the product forms wherein manufacturing rate, automation and self-weight minimization are essential goals. Manufacturing processes studied include composites additive manufacturing, high-rate stamp forming of thermoplastic systems and hybrid molding systems. The SURF student will work alongside engineering and physics graduate students and staff in carrying out this interaction
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Solid mechanics, fluid mechanics, software programming,
School/Dept.:
School of Materials Engineering
Professor:
R. Byron
Pipes
More information: www.purdue.edu/cmsc
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.
Research categories:
Biological Characterization and Imaging, Medical Science and Technology, Nanotechnology
Desired experience:
general chemistry, calculus, analytical or physical chemistry
School/Dept.:
Chemistry
Professor:
Julia
Laskin
More information: https://www.chem.purdue.edu/jlaskin/
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.
Research categories:
Biological Characterization and Imaging, Medical Science and Technology, Nanotechnology
Desired experience:
general chemistry, calculus, analytical or physical chemistry
School/Dept.:
Chemistry
Professor:
Julia
Laskin
More information: https://www.chem.purdue.edu/jlaskin/
Measuring Systemic Inflammatory Effects of Synovial Joint Injury
Description:
In this project, we are interested in the aftermath of synovial joint injury and whether it triggers inflammation or similar effects in other tissues of the body. We will conduct this work in an institution-approved animal model and collect tissues. The student role in this project will be to analyze some of the collected tissues and evaluate the data associated with this analysis.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Medical Science and Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
- Mechanical Engineering
- Biological Engineering - multiple concentrations
Desired experience:
Student should have an engineering background to understand the biomechanical nature of the joint injury model. Background in biology and biochemistry is also very helpful, as the tissue analysis and interpretation of data will require understanding of physiology and biology. Prior experience working in a wet laboratory, including with tissue dissection and biochemical / molecular biology assays, is strongly preferred.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Deva
Chan
More information: https://engineering.purdue.edu/ChanLab
Mechanically Driven Variations in Bone Composition via FT-NIR for Improved Fracture Risk Assessment
Description:
Project Role:
Students working on this project will be engaged in experimental biomechanics, imaging, and computational modeling to map mechanical stress and composition across human bone specimens. This work aims to enhance fracture risk prediction by identifying compositional variations that correlate with mechanical properties. Students will gain hands-on experience with cadaveric bone testing, FT-NIR spectroscopy, advanced imaging techniques, and data processing, contributing to clinically translatable insights for bone health assessment.
Project Description:
This project will utilize human cadaveric bone specimens to investigate the relationship between mechanical stress distribution and bone composition. We will conduct mechanical testing in the elastic region to map stiffness and other pre-yield properties, allowing us to model regions of high and low mechanical stress. These identified regions will then be analyzed using novel Fourier transform near-infrared (FT-NIR) spectroscopy, which enables high-resolution (6μm) compositional imaging across the bone surface.
To bridge the gap between microscale analysis and clinical application, ultrashort echo time (UTE) MRI of the whole specimen will be performed. This will help evaluate whether in vivo imaging can detect microscale compositional changes with sufficient sensitivity to provide clinically meaningful insights into bone quality.
Students will have the opportunity to:
Prepare and test human bone specimens in controlled mechanical loading conditions.
Analyze mechanical stress distributions using experimental and computational modeling techniques.
Perform high-resolution FT-NIR spectroscopy to assess spatial variations in bone composition.
Process and interpret UTE MRI data to assess its potential as a clinical tool for detecting early bone quality changes.
Develop data visualization and interpretation techniques to integrate mechanical and compositional findings for fracture risk assessment.
Students working on this project will be engaged in experimental biomechanics, imaging, and computational modeling to map mechanical stress and composition across human bone specimens. This work aims to enhance fracture risk prediction by identifying compositional variations that correlate with mechanical properties. Students will gain hands-on experience with cadaveric bone testing, FT-NIR spectroscopy, advanced imaging techniques, and data processing, contributing to clinically translatable insights for bone health assessment.
Project Description:
This project will utilize human cadaveric bone specimens to investigate the relationship between mechanical stress distribution and bone composition. We will conduct mechanical testing in the elastic region to map stiffness and other pre-yield properties, allowing us to model regions of high and low mechanical stress. These identified regions will then be analyzed using novel Fourier transform near-infrared (FT-NIR) spectroscopy, which enables high-resolution (6μm) compositional imaging across the bone surface.
To bridge the gap between microscale analysis and clinical application, ultrashort echo time (UTE) MRI of the whole specimen will be performed. This will help evaluate whether in vivo imaging can detect microscale compositional changes with sufficient sensitivity to provide clinically meaningful insights into bone quality.
Students will have the opportunity to:
Prepare and test human bone specimens in controlled mechanical loading conditions.
Analyze mechanical stress distributions using experimental and computational modeling techniques.
Perform high-resolution FT-NIR spectroscopy to assess spatial variations in bone composition.
Process and interpret UTE MRI data to assess its potential as a clinical tool for detecting early bone quality changes.
Develop data visualization and interpretation techniques to integrate mechanical and compositional findings for fracture risk assessment.
Campus:
Indianapolis
Research categories:
Biological Characterization and Imaging
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Rachel
Surowiec
More information: https://engineering.purdue.edu/QBIS
Mechano-transduction in progerin-expressing endothelial cells
Description:
A mutant truncated form of lamin called progerin is responsible for premature aging disease Hutchinson-Gilford progeria syndrome (HGPS). Interestingly, progerin is also found in normal adults, and increases with age. Defective nuclear lamina in aged cells share similarities with nuclei of HGPS cells such as distorted nuclear shape and increased mechano-sensitivity. HGPS patients share something else with older people: the progression of cardiovascular diseases such as atherosclerosis. Endothelial cells in the vasculature are constantly exposed to hemodynamic forces of blood flow on the lumen side. The presence of progerin yielded stiff nucleus, but softer chromatin in nuclear interior, which probably contributed to decreased responsiveness to mechanical stress. In this project, we will develop an endothelial cell line that stably express GFP-fused wildtype lamin and progerin proteins. These cells will be used in flow experiments to examine the effect of shear stress on how progerin affect signaling transduction such as the AMPK pathway, and the subsequent effect on cell homeostasis and aging. Students will be involved in a range of techniques, such as cell culture, transfection, fluorescence live cell imaging, immunohistochemistry, quantitative image analysis, Western blotting, PCR.
Campus:
Indianapolis
Research categories:
Biological Characterization and Imaging, Cardiovascular Disease Research, Cellular Biology
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Biomedical Engineering
- Chemical Engineering
- Biology
- Biological Engineering - multiple concentrations
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Julie
Ji
Methods for High-speed Imaging of Energetic Reactions (AAMP-UP-PERC)
Description:
Energetic reactions are characterized by high-temperature, rapidly evolving, multiphase fields. The goal of this project is to develop, demonstrate, and evaluate high-speed sources and detectors that are optimized for spatial resolution, temporal resolution, discrimination against background signals, low noise, and robust operation under complex environments. The student will become familiar with optical instrumentation and its use in analyzing rapidly evolving multiphase flows. The effort will take place within a research team with regular discussions of ongoing technical challenges, solution strategies, and research results. The work will culminate in a technical presentation and/or written report.
Campus:
West Lafayette
Research categories:
Thermal Technology, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
Desired experience:
Experience with thermodynamics, fluid mechanics, basic physics, basic chemistry, and engineering design is desired. Training on specific skills will be provided.
School/Dept.:
School of Mechanical Engineering
Professor:
Terrence
Meyer
More information: engineering.purdue.edu/trmeyer
Microfluidic Analysis of Nanoparticle Binding in the Subarachnoid Space
Description:
This research focuses on the use of nanoparticles to deliver genetic medicines intrathecally to the central nervous system. Here, the student will work with a graduate student mentor to develop technologies used for understanding the underlying physiology of intrathecal drug delivery. Completion of this work will aid in improving the delivery of injectable medicines with the goal of enabling better patient outcomes.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Luis
Solorio
More information: https://soloriolab.wixsite.com/tmet
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.
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:
U.S. Citizen
Preferred major(s):
- No Major Restriction
Desired experience:
Biomaterial synthesis and fluorescence microscopy techniques are a plus, but not a requirement.
School/Dept.:
School of Mechanical Engineering
Professor:
Arezoo
Ardekani
More information: https://engineering.purdue.edu/ComplexFlowLab/
Microstructural control of energetic materials
Description:
The goal of this project is to understand how various material properties affect the microstructure, and thus performance, of energetic materials (i.e. propellants, explosives, pyrotechnics). This project requires U.S. citizenship. The researcher will learn material science and manufacturing principles.
Campus:
West Lafayette
Research categories:
Material Processing and Characterization
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
- Materials Engineering
- Chemical Engineering
Desired experience:
Exposure to material science (i.e. powders characterization, polymer properties)
Exposure to manufacturing techniques (i.e. traditional manufacturing of particle compacts such as cast-cure or pressing, or advanced manufacturing techniques for polymer and particle processing)
School/Dept.:
School of Mechanical Engineering
Professor:
Monique
McClain
More information: https://mcclain.team/
Modeling Physiological Signals and its Application to Wearable Devices
Description:
Physiological signal dynamics provide insights into autonomic nervous system function, cardiovascular health, and overall well-being, but their interpretation is complicated and with most technologies more easily understood as a comparison to population-derived metrics as opposed to what is normal for the individual. These metrics may be singular or combinatorial and can include heart rate, heart rate variability, skin temperature, respiration, locomotor activity, and various derived metrics in the time and frequency domain. Measuring and accurately interpreting these metrics are crucial for understanding how factors such as exercise, sleep quality, mental stress, diet, and aging impact an individual’s physiological state. By tracking physiological signals over time and with respect to an individual’s typical daily activities, environments encountered, and other factors unique to their lifestyle or circumstances, we hypothesize that individuals will gain a clearer picture of their overall health. Such contextual information added to the physiological data will empower individuals with a deeper understanding of their physiology and overall health, how their decisions can impact their physiology/health, and how positive lifestyle changes could improve their specific health and wellbeing.
Ultimately, this project aims to provide a comprehensive and interactive physiological signal tracking tool that empowers users to take control of their health. By integrating smartwatch accessibility with MATLAB-based data analysis, users will be able to monitor their physiological state in real time, interact with their data, and recognize meaningful trends that affect their daily lives. This technology will not only enhance self-awareness but also provide a valuable resource for individuals looking to optimize their well-being through data-driven decision-making.
Ultimately, this project aims to provide a comprehensive and interactive physiological signal tracking tool that empowers users to take control of their health. By integrating smartwatch accessibility with MATLAB-based data analysis, users will be able to monitor their physiological state in real time, interact with their data, and recognize meaningful trends that affect their daily lives. This technology will not only enhance self-awareness but also provide a valuable resource for individuals looking to optimize their well-being through data-driven decision-making.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biotechnology Data Insights, Human Factors
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biomedical Engineering
- Computer Science
- Electrical Engineering
Desired experience:
A top candidate will have substantial experience with programming in any programming language (MATLAB familiarity required), will understand how to work with time-series data, and will thrive in a fast-paced, dynamic learning environment.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Matthew
Ward
Modeling and parameter estimation in mathematical biology
Description:
This EURO SURF project broadly focuses on research topics in mathematical biology. We will either develop mathematical models of biological self-organization and pattern formation (e.g., cells interacting during tissue development in ferns or fish) or apply methods from Bayesian inference, topological data analysis, or other areas to better understand the behavior of existing models of biological systems.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Simulation and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Programming experience, differential equations, linear algebra, excitement about interdisciplinary research
School/Dept.:
Mathematics
Professor:
Alexandria
Volkening
More information: https://alexandriavolkening.com
Modeling elastic cells in fluid flow
Description:
Overview: Microfluidic devices are increasingly used for lab-on-a-chip technology for medical diagnostics. Mammalian cells are elastic and deform under the flow of a microfluidic device. We want to model the deformation of a cell using elasticity theory combined with simple flow. The objective is to model the deformation of a sphere under a shear flow and visualize the resulting shape.
Objectives:
1. Background reading on elasticity theory.
2. Set up the equations of motion for an elastic cell in a fluid flow.
3. Solve the equations of motion either analytically with pen and paper, or numerically using MATLAB/python/ or other coding language.
4. Main Question: Given a sphere in a shear flow, what shape does it deform to?
Objectives:
1. Background reading on elasticity theory.
2. Set up the equations of motion for an elastic cell in a fluid flow.
3. Solve the equations of motion either analytically with pen and paper, or numerically using MATLAB/python/ or other coding language.
4. Main Question: Given a sphere in a shear flow, what shape does it deform to?
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology, Fluid Modelling and Simulation, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Differential Equations, Linear Algebra, and coding experience required. It is a plus if you have taken courses in Fluid Mechanics, Elasticity Theory, or Partial Differential Equations.
School/Dept.:
Mathematics
Professor:
Kaitlyn
Hood
More information: https://www.math.purdue.edu/~kthood/research.html
Modeling of drug-resistance evolution in malaria considering antigenic diversity
Description:
The current drug resistance prediction model falls short of capturing the distinct resistance prevalence patterns observed between West and East Africa, despite similar drug usage across these regions. Our recent mathematical model introduces a novel approach to drug resistance modeling by incorporating malaria antigenic diversity, which has successfully predicted the biogeographic patterns of antimalarial resistance. Building on this foundation, we developed an agent-based model of drug resistance evolution, extending the simple compartmental framework to predict drug resistance trends in various endemic transmission settings more effectively. The student is responsible for exploring the model and identifying key variables influencing drug resistance dynamics and assessing their impact on the persistence and spread of resistant strains.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Simulation and Technology
Citizenship requirements:
No citizenship requirements, U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Background in infectious disease modeling; Experience in biological data analyses; Willing to learn agent-based stochastic simulations; willing to learn malaria biology;
School/Dept.:
Biological Sciences
Professor:
Qixin
He
Mucus diversity in fishes
Description:
The skin of fishes is thought to be responsible for performing multiple biomechanics functions. Fish dermis, scales, and mucus likely all function together to protect the fish from predators, combat infection and parasites, and to improve hydrodynamic properties of the skin. However, relatively little is understood about the contributions of various skin components to different functions, especially mucus. Mucus is thought to both protect from infection and to improve hydrodynamics by modifying friction drag through the boundary layer (a layer of shearing fluid near the surface of the fish), but these ideas have only been demonstrated in a few species of fishes. We do not know if diversity exists in the hydrodynamic effects of mucus across species, and we do not know if mucus material properties have diversity across species and if these properties might relate to hydrodynamic effects. In this project, we will aim to fill those gaps by obtaining mucus from lives fishes, studying its material properties (e.g. viscosity), and then testing how mucus changes boundary layer hydrodynamics using an experimental flow tank where we can track flow over the skin using particles seeded in the water. We will aim to study 3-5 species of fishes with very different lifestyles and skin features in an effort to better reveal potential diversity in the mucus of fishes. This work may reveal a new axis of diversity in fishes (mucus function and properties) and will help us understand how fishes are modifying their skin to improve hydrodynamic efficiency. Our results could have implications for designing bio-inspired surfaces that decrease friction drag. The student will be responsible for carrying out all data collection and analysis for this project and will be advised by Prof. Dylan Wainwright and other lab personnel.
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:
Dylan
Wainwright
More information: https://www.dylanwainwright.com/
Multi-modal Capture of Symptoms Data from Speech or Text with Automatic Annotation to Continuous Analysis of Heart Rhythm: Application in the Ehlers-Danlos Syndromes
Description:
The Ehlers-Danlos syndromes (EDS) are a group of hereditary disorders of connective tissue that exhibit joint hypermobility and skin involvement, with other organs involved in varying degrees depending on the type of EDS. The most common type, known as the hypermobile type, or hEDS, is still without a known genetic cause. The manifestations of hEDS extend far beyond the musculoskeletal system and skin; aside from chronic pain, the most debilitating symptoms are due to autonomic nervous system (ANS) impairment. The precise mechanisms leading to autonomic dysfunction in hEDS are not well understood. Individuals with hEDS have hyper-elastic veins that allow up to 1/3 of the circulating blood volume to collect in the lower extremities upon standing. This can lead to diminished blood flow and oxygen-carrying capacity to the heart, lungs, and brain. A second possible mechanism is compression of the medulla due to craniocervical instability, which occurs when the tendons and ligaments that hold the head on the neck allow excessive movement between the skull and the cervical spine. Thirdly, people with hEDS are often living in constant pain, sending a consistent message to the brain that the body is under threat. This heightens the expression of the sympathetic “fight-or-flight” nervous system. Since the parasympathetic nervous system mediates central coordination of “rest-and-digest” functions, heart rhythms, gastrointestinal motility, sleep, and quality-of-life are subsequently impaired. Due to the rarity of this disease, there have been few long-term symptom profiling studies to understand the nature of symptoms with respect to autonomic dysfunction. In this project, a student will work with clinicians and graduate students to integrate LLM functionality into a digital Continuous Autonomic Physiologist (dCAP) mobile app in a manner that can transcribe spoken language and automatically tag the event to the dCAP data stream.
Campus:
West Lafayette
Research categories:
Cardiovascular Disease Research, Mobile Computing
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Has or will develop confidence in communication across disciplines; familiarity with large language models; resilience; curiosity
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Matthew
Ward
Multi-step enzymatic synthesis planning using Monte Carlo Tree Search Algorithm
Description:
Enzymes enable the synthesis of complex pharmaceutical agents because they catalyze selective chemical reactions under mild conditions. Sankar Research Lab develops and tests computational tools capable of proposing synthesis routes (or “recipes”) that primarily use enzyme catalysis to make complex pharmaceutical agents. These computational tools would identify ways to cost-efficiently manufacture complex target molecules and would improve our ability to synthesize a diverse library of complex chemical compounds for drug discovery programs.
The successful candidate will join multidisciplinary, highly collaborative research group to invent novel synthesis routes for producing medicines by applying innovative tree search algorithm(s) for decision making in enzymatic synthesis planning. In addition, you’ll have the opportunity to expand your knowledge and skills through collaborations with talented and dedicated colleagues while advancing your career.
Specific responsibilities will include the following:
• Act as a computational expert in the laboratory, executing on the development of Monte Carlo tree search algorithm for decision making in enzymatic synthesis planning
• Develop and utilize innovative search algorithms coupled with the lab’s existing computational enzymatic synthesis planning algorithms to expand our current capabilities for producing complex pharmaceutical agents.
• Co-author scientific publications and presentations.
The successful candidate will join multidisciplinary, highly collaborative research group to invent novel synthesis routes for producing medicines by applying innovative tree search algorithm(s) for decision making in enzymatic synthesis planning. In addition, you’ll have the opportunity to expand your knowledge and skills through collaborations with talented and dedicated colleagues while advancing your career.
Specific responsibilities will include the following:
• Act as a computational expert in the laboratory, executing on the development of Monte Carlo tree search algorithm for decision making in enzymatic synthesis planning
• Develop and utilize innovative search algorithms coupled with the lab’s existing computational enzymatic synthesis planning algorithms to expand our current capabilities for producing complex pharmaceutical agents.
• Co-author scientific publications and presentations.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biotechnology Data Insights
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Education Minimum Requirement:
• Current junior/senior undergraduate or master’s student at Purdue University in Computer Science, Computer Engineering, Electrical and Computer Engineering, and/or related fields.
School/Dept.:
Department of Agricultural and Biological Engineering
Professor:
Karthik
Sankaranarayanan
More information: https://www.ksankargroup.com/
Multiple Target Tracking Using Influence Diagrams
Description:
The project will develop an interactive tool using that extends previously a developed influence diagram Kalman filtering tool to perform Multiple Target Tracking.
The student will be developing Python code to extend previously developed code that implements influence diagram Kalman filtering and test cases for the Python code using the MATLAB-based Sensor Fusion and Tracking Toolbox.
The student will be developing Python code to extend previously developed code that implements influence diagram Kalman filtering and test cases for the Python code using the MATLAB-based Sensor Fusion and Tracking Toolbox.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
Desired experience:
Linear Algebra, Multivariate Statistics, MATLAB, Python, GUI development
School/Dept.:
School of Industrial Engineering
Professor:
C. Robert
Kenley
More information: https://web.ics.purdue.edu/~ckenley/
Nanoscale 3D printing
Description:
The ability to create 3D structures in the micro and nanoscale is important for many applications including electronics, microfluidics, and tissue engineering. This project deals with developing and testing of a laser-based setup for building 3D structures with sub-micrometer resolution. A method known as femtosecond laser two photon polymerization is used to fabricate such structures in which a resin is exposed to laser. Moving the laser in a predefined path results in the desired shape and structure. The 3D printing process incorporates the steps from designing a CAD model file to slicing the model in layers to generating the motion path of the laser needed for fabricating the structure. Machine learning is also being used to improve the printing accuracy. Possible involvements by the undergraduate researcher include developing control algorithms, better CAD models, and better manufacturing strategies.
Campus:
West Lafayette
Research categories:
Material Processing and Characterization, Nanotechnology
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Mechanical Engineering
Desired experience:
Mechanical Engineering Junior or Senior standing with GPA > 3.5, CAD models, knowing Python is a plus
School/Dept.:
School of Mechanical Engineering
Professor:
Xianfan
Xu
More information: https://engineering.purdue.edu/NanoLab/
Network simulation for large-scale distributed training
Description:
This project investigates the end-to-end effect of optimizing data center transport for large-scale distributed training. The student will learn to implement and use various tools to simulate the traffic patterns in a data center network generated by distributed training and collective communication, as well as how different designs of data center transport affect the end-to-end performance of different training workload.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Engineering
- Computer Science
Desired experience:
The student is expected to have familiarity in C++, Python, and knowledge in Pytorch Distributed. The student should have taken courses in networking and have background and research interest in data center networks.
School/Dept.:
ECE
Professor:
Xiaoqi
Chen
Next-Gen AI Tutoring System for STEM Education
Description:
Conventional college education, especially in STEM fields, suffers from several key shortcomings. Students often face an unpersonalized teaching environment, where instruction does not adapt to individual pace, interests, or levels of understanding. The system also offers limited flexibility, with rigid schedules and standardized content delivery that restrict self-paced or exploratory learning. At the root of these issues is the scarcity of teaching resources—instructors cannot provide tailored support or real-time feedback to every student, resulting in a one-size-fits-all approach that leaves many learners behind.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Learning and Evaluation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
Desired experience:
students should be able to work in building system architectures for GenAI based interaction in virtual class rooms with a phd mentor
School/Dept.:
School of Mechanical Engineering
Professor:
Karthik
Ramani
More information: https://engineering.purdue.edu/cdesign/wp/
Nondestructive evaluation of hybrid additively manufactured materials
Description:
Projects involve the design, manufacturing, and characterization of multi-material samples using a variety of additive manufacturing platforms including polymer, ceramic, and metal processing. Student researchers will integrate process in situ sensing and post process nondestructive evaluation methods with analytical and/or FEA modeling for process and part quality optimization.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Composite Materials and Alloys, Fabrication and Robotics, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements, U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Interest in Additive Manufacturing, acoustics, and/or signal processing as well as enthusiasm for learning and collaboration. Applicants with one or more of the following skills are preferred:
• Modeling with CAD software
• Hands-on building, fabrication and/or testing
• Operation of an Additive Manufacturing system
• Experimental measurements and data collection
• Programing with Matlab or Python
School/Dept.:
School of Mechanical Engineering
Professor:
Luz
Sotelo
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=277732
Operation and characterization of SPT-100 Hall-effect thruster
Description:
Hall thrusters are widely utilized for spacecraft propulsion including applications in LEO (e.g., Starlink) and deep space exploration (e.g., Deep Space Transport). In Hall thrusters, a neutral gas propellant is ionized and accelerated in ExB-field configuration, achieving high exhaust velocities in the range of 10 - 50 km/s.
In this project, the student will work with the Hall-effect thruster SPT-100. The scope of the project includes operating the thruster and neutralizer (a hollow cathode or an inductively coupled plasma) and measuring the thruster's electrical parameters, exhaust plasma jet properties, and thrust level. The student will utilize Langmuir probes (along with other diagnostics) for measurements of plasma parameters and hanging pendulum thrust stand for thrust measurements. In addition, the student is going to prepare and update related documentation for AAE 521 Plasma Lab.
In this project, the student will work with the Hall-effect thruster SPT-100. The scope of the project includes operating the thruster and neutralizer (a hollow cathode or an inductively coupled plasma) and measuring the thruster's electrical parameters, exhaust plasma jet properties, and thrust level. The student will utilize Langmuir probes (along with other diagnostics) for measurements of plasma parameters and hanging pendulum thrust stand for 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):
- Aeronautical and Astronautical Engineering
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Alexey
Shashurin
More information: https://engineering.purdue.edu/EPPL
Optimization for Robust Machine Learning
Description:
This project aims to develop advanced optimization techniques to enhance the robustness of machine learning models. Robust machine learning is essential for managing noisy, adversarial, and uncertain data, which are common in real-world applications. The research focuses on designing and analyzing novel algorithms to address these challenges by leveraging theoretical tools such as mathematical optimization, stochastic processes, and statistics. The project seeks to improve model robustness and reliability across diverse domains, contributing to a deeper understanding of the interplay between optimization and machine learning, with practical implementations for solving modern data-driven problems.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
A strong mathematical background and solid computational skills are highly desirable. Prior experience in optimization and machine learning would be an advantage.
School/Dept.:
School of Industrial Engineering
Professor:
Miaolan
Xie
More information: https://miaolan.github.io/
Optimizing components with multi-material solutions
Description:
Traditional components are made of a monolithic material or repeating building block that are identified and fixed at an early stage in the design process. There are potential advantages to simultaneously designing the component and materials solution. The objective of this work is to create specific tools to help support and define compositional gradients, diffusion, and geometry. These tools will be part of a larger project to develop an integrated set of techniques and processes to produce optimized parts that are based on composition and functional gradients designed within the parts. The anticipated outcome of this research is a framework to integrate the design, analysis, manufacture, inspection, and testing of components, that results in the ability to optimize a materials-centric solution for enhancements in part design and new manufacturing processes to produce these components. The results of this project are expected to make significant advancements to improve component performance, reliability, and sustainability.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Aeronautical and Astronautical Engineering
- Materials Engineering
- Mechanical Engineering
Desired experience:
Experience with computer programming either Matlab or Python. Basic materials knowledge of phase diagrams and diffusion. Interested in juniors.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Michael
Sangid
More information: https://engineering.purdue.edu/ACME2
Organic Bioelectronics
Description:
Organic bioelectronics is an interdisciplinary field that merges organic electronics with biological systems, aiming to develop devices that can seamlessly interface with living tissues. By utilizing conductive polymers, , these devices are designed to be biocompatible, flexible, and capable of mimicking the properties of biological tissues. Applications range from biosensors and neural interfaces to drug delivery systems and wearable health monitors. Organic bioelectronics holds immense potential for advancing medical diagnostics, prosthetics, and personalized medicine, as it enables precise communication between electronic devices and biological processes. Its ability to integrate with the human body while maintaining minimal invasiveness makes it a transformative technology in healthcare and biotechnology.
Campus:
West Lafayette
Research categories:
Biotechnology Data Insights, Material Processing and Characterization, Microelectronics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Jianguo
Mei
PARI Scholars Undergraduate Hypersonics Researcher: Novel Material Development, Characterization, and Ground Testing of Aerostructures
Description:
The Purdue Applied Research Institute (PARI) is seeking motivated undergraduate researchers to support groundbreaking research in hypersonic ground testing, focusing on gas-surface interactions, transpiration cooling, and material responses under extreme conditions. This role offers hands-on experience with advanced experimental techniques, additive manufacturing, and high-enthalpy wind tunnel testing within Purdue's Hypersonic and Applied Research Facility (HARF). It is important to note that prior laboratory or research experience is not required, and this position serves as an opportunity to gain multi-year experience working on a multi-disciplinary research project.
Key Responsibilities:
• Assist in the design, fabrication, and testing of model aerostructures and nose cones using advanced additive manufacturing (AM) techniques.
• Support ceramics materials development for use in digital light projection (DLP) AM.
• Prepare porous ceramic and metal samples for transpiration cooling experiments, metrology, and characterization.
• Support high-speed diagnostics, including schlieren imaging, and other optical measurement methods to capture aerodynamic behavior of AM produced aerostructures
• Collect, process, and analyze experimental data related to thermo-mechanical testing, boundary layer interactions, ablation, and oxidation kinetics.
• Collaborate with PARI staff, Purdue faculty, and Air Force Research Laboratory (AFRL) mentors to advance hypersonic testing capabilities.
Key Responsibilities:
• Assist in the design, fabrication, and testing of model aerostructures and nose cones using advanced additive manufacturing (AM) techniques.
• Support ceramics materials development for use in digital light projection (DLP) AM.
• Prepare porous ceramic and metal samples for transpiration cooling experiments, metrology, and characterization.
• Support high-speed diagnostics, including schlieren imaging, and other optical measurement methods to capture aerodynamic behavior of AM produced aerostructures
• Collect, process, and analyze experimental data related to thermo-mechanical testing, boundary layer interactions, ablation, and oxidation kinetics.
• Collaborate with PARI staff, Purdue faculty, and Air Force Research Laboratory (AFRL) mentors to advance hypersonic testing capabilities.
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
- Aeronautical and Astronautical Engineering
- Engineering (First Year)
- Materials Engineering
- Mechanical Engineering
- Chemical Engineering
- Multidisciplinary Engineering
Desired experience:
• Enrollment in an undergraduate program in Aerospace Engineering, Mechanical Engineering, Materials Science, or related field.
• Excellent problem-solving skills and attention to detail.
• Ability to work both independently and as part of a multidisciplinary team.
• Ability to obtain and maintain a security clearance through the United States (US) Department of Defense (DoD).
School/Dept.:
Purdue Applied Research Institute
Professor:
Julio
Hernandez
More information: https://hamtc.pari.purdue.edu/
Particle Shape Prediction via Surface Energy Database Creation for Energetic and Pharmaceutical Systems
Description:
This project focuses on generating a comprehensive surface energy database to predict particle shapes of energetic materials and pharmaceuticals. Leveraging an unpublished nanoHUB tool, surfaces of molecular crystal systems like HMX, RDX, and TNT as well as drug molecules will be generated, relaxed, and analyzed to compute their corresponding surface energies. The tool integrates LAMMPS simulations to target the most common crystal surfaces initially. This effort bridges computational tools and experimental insights, contributing to advancements in materials design and pharmaceutical applications.
Key deliverables include:
- Tool Enhancement: Refining the nanoHUB tool for public release, enabling automated surface generation and relaxation for energetic materials.
- Database Creation: Establishing a robust ResultsDB to store simulation dataParticle Shape Prediction: Utilizing database for particle shape prediction via Wulff constructions.
- Resources:
o https://joss.theoj.org/papers/10.21105/joss.01944
o https://en.wikipedia.org/wiki/Wulff_construction
o https://nanoHUB.org
o https://pubs.acs.org/doi/10.1021/acs.cgd.0c00766
o https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.2c00562
o https://onlinelibrary.wiley.com/doi/full/10.1002/prep.202300230
Key deliverables include:
- Tool Enhancement: Refining the nanoHUB tool for public release, enabling automated surface generation and relaxation for energetic materials.
- Database Creation: Establishing a robust ResultsDB to store simulation dataParticle Shape Prediction: Utilizing database for particle shape prediction via Wulff constructions.
- Resources:
o https://joss.theoj.org/papers/10.21105/joss.01944
o https://en.wikipedia.org/wiki/Wulff_construction
o https://nanoHUB.org
o https://pubs.acs.org/doi/10.1021/acs.cgd.0c00766
o https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.2c00562
o https://onlinelibrary.wiley.com/doi/full/10.1002/prep.202300230
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Energy and Environment, Material Modeling and Simulation, Nanotechnology
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Intro programming, physics, and chemistry and a willingness to learn more are required.
School/Dept.:
School of Materials Engineering
Professor:
Alejandro
Strachan
Particle-Reinforced Polymers for 3D Printing in Space
Description:
Additive manufacturing, such as 3D printing technology, has great potential to transform space exploration by facilitating the on-demand production of intricate parts and structures in orbit. Several challenges must be overcome to leverage the potential of effectively
3D printing for space-grade applications. These challenges include limitations in printing speed, resolution, and material qualities. Ensuring the reliability and functionality of 3D-printed devices is crucial in the hostile space environment, where dependability, longevity, and efficiency are essential. To tackle these obstacles, cutting-edge materials and printing methods must be developed tailored to the particular space requirements. Polymer composites could offer an attractive solution by offering adjustable material qualities, including strength, thermal stability, and radiation resistance. The primary objective of this project is to investigate the behavior of a novel soft material that consists of amphiphilic Janus particles in polymer matrices by combining experimental research and computer simulations synergistically. Students working on this project will be tasked with synthesizing Janus particles at and above the colloidal range and mixing them with UV-crosslinkable resins for 3D printing composites. Undergraduate students with a background in chemistry, materials engineering, mechanical engineering, and physics are well-suited to work on the project. Prior experience in 3D printing and CAD will be beneficial.
3D printing for space-grade applications. These challenges include limitations in printing speed, resolution, and material qualities. Ensuring the reliability and functionality of 3D-printed devices is crucial in the hostile space environment, where dependability, longevity, and efficiency are essential. To tackle these obstacles, cutting-edge materials and printing methods must be developed tailored to the particular space requirements. Polymer composites could offer an attractive solution by offering adjustable material qualities, including strength, thermal stability, and radiation resistance. The primary objective of this project is to investigate the behavior of a novel soft material that consists of amphiphilic Janus particles in polymer matrices by combining experimental research and computer simulations synergistically. Students working on this project will be tasked with synthesizing Janus particles at and above the colloidal range and mixing them with UV-crosslinkable resins for 3D printing composites. Undergraduate students with a background in chemistry, materials engineering, mechanical engineering, and physics are well-suited to work on the project. Prior experience in 3D printing and CAD will be beneficial.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis, Composite Materials and Alloys, Material Processing and Characterization, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Materials Engineering
- Chemistry
- Mechanical Engineering
- Physics
School/Dept.:
School of Materials Engineering
Professor:
Carlos
Martinez
Peptide-based Biomaterials
Description:
The student will be responsible for the synthesis and purification of peptides that self-assemble into nanotubes with associated cargoes.
Campus:
West Lafayette
Research categories:
Material Processing and Characterization
Citizenship requirements:
U.S. Citizen
Preferred major(s):
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Organic chemistry, laboratory experience
School/Dept.:
Department of Chemistry
Professor:
Jean
Chmielewski
Pharmaceutical Lyophilization
Description:
Freeze-drying, also called lyophilization, is widely used in manufacturing of injectable pharmaceuticals, vaccines, biotech products, chemical reagents, food and probiotic cultures. The SURF undergraduate researchers will have an opportunity to be involved in one of the ongoing projects in LyoHUB technology demonstration facility in Birck Nanotechnology Center.
LyoHUB, a leading industry-academia consortium, based here at Purdue University, works with over 36 industry partners to advance the science and technology of lyophilization, a critical process in food and pharmaceutical manufacturing. LyoHUB operates a state-of-the-art demonstration facility in Birck Nanotechnology Center and is excited to sponsor SURF students for Summer 2025.
The student will learn the basics of the freeze drying process and will get the skills of experimental work in the lab while engaged on one of many ongoing research projects.
This project will include in person meetings and hands-on lab work.
More information: www.lyohub.org
LyoHUB, a leading industry-academia consortium, based here at Purdue University, works with over 36 industry partners to advance the science and technology of lyophilization, a critical process in food and pharmaceutical manufacturing. LyoHUB operates a state-of-the-art demonstration facility in Birck Nanotechnology Center and is excited to sponsor SURF students for Summer 2025.
The student will learn the basics of the freeze drying process and will get the skills of experimental work in the lab while engaged on one of many ongoing research projects.
This project will include in person meetings and hands-on lab work.
More information: www.lyohub.org
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Chemical Engineering
- Aeronautical and Astronautical Engineering
- Biological Engineering - multiple concentrations
- Computer Science
- Mechanical Engineering
Desired experience:
interest in pharmaceutical manufacturing
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Alina
Alexeenko
More information: www.lyohub.org
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
Desired experience:
machine learning
python
School/Dept.:
Earth, Atmospheric and Planetary Sciences
Professor:
Lei
Wang
More information: http://leiw.org
Piezo in zebrafish wound closure
Description:
Student will characteriza how the mechanical sensitive ion channel Piezo regulate epithelial migration and wound closure in a zebrafish model
Campus:
West Lafayette
Research categories:
Cellular Biology, Genetics, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Biological Sciences
Professor:
Qing
Deng
More information: https://www.denglab.us/
Precision measurements of a transition polarizability in cesium
Description:
We will measure spectroscopic parameters of cesium in support of our program to measure the weak nuclear force between the electrons and protons of the cesium atom. The student participating in this project will help fabricate and mount field plates to create a very uniform electric field in the interaction region, design and fabricate circuits to control the magnitude of the electric field between these field plates, program the laboratory computer to control the experiment and collect the data, and write additional code to analyze the data.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering
Desired experience:
Circuit design, computer programing
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Dan
Elliott
More information: https://engineering.purdue.edu/QOptics
Predicting Carbon Intensity Using Deep Learning to Improve Environmental Sustainability
Description:
The rapid growth of artificial intelligence and machine learning (ML) has led to a surge in energy demand for datacenter computing. This increased demand is a significant contributor to carbon emissions, with datacenters now accounting for approximately 2% of global carbon emissions. To mitigate these impacts, it is crucial to measure the carbon intensity of the electricity grid. By predicting future carbon intensity, datacenter operators can optimize workload scheduling to minimize their carbon footprint by executing tasks during periods and in regions with lower carbon intensity. However, accurate carbon intensity prediction remains challenging due to the influence of various factors, including energy source availability, weather conditions, and policy changes. This project aims to address this challenge by leveraging advanced machine learning techniques, such as deep learning and foundation models, to forecast future carbon intensity. We are looking for self-motivated students with strong ML backgrounds (e.g., ML courses and internship experience) and programming skills in Python.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Ecology and Sustainability, Energy and Environment
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
- Computer Engineering
- Data Science
Desired experience:
Courses in artificial intelligence, machine learning and/or deep learning. Strong programming skills in Python. Prefer junior and senior students.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Yi
Ding
More information: https://arxiv.org/abs/2407.02390
Predicting alkali-silica reaction (ASR) in concrete using AI/ML modeling
Description:
Alkali-silica reaction is a deleterious reaction in concrete that causes damage to concrete structures. This project involves developing, testing, and refining machine learning models to analyze data related to concrete durability. By identifying patterns and predicting the onset and progression of ASR, you will contribute to improving the long-term sustainability of concrete structures. This project offers hands-on experience in applying machine learning methods to solve real-world engineering challenges, while also bridging the gap between materials science and data analytics.
Type of work:
1. Conduct literature reviews on ASR and related machine learning methodologies; gather, clean, and analyze experimental data.
2. Develop and implement AI/ML models using programming languages such as Python with a focus on predictive analytics.
3. Run simulations and validate model predictions against experimental data to ensure accuracy and reliability.
Type of work:
1. Conduct literature reviews on ASR and related machine learning methodologies; gather, clean, and analyze experimental data.
2. Develop and implement AI/ML models using programming languages such as Python with a focus on predictive analytics.
3. Run simulations and validate model predictions against experimental data to ensure accuracy and reliability.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Civil Engineering
- Data Science
- Computer Science
Desired experience:
Proficiency in programming languages
Understanding data analytics, statistics, and model validation techniques.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Yining
Feng
Provably Correct Quantum Circuit Cutting
Description:
In this project, students will explore quantum circuit cutting strategies, including CutQC, and evaluate these techniques on a real quantum computing platform (from IBM). Additionally, students will have the opportunity to learn about quantum verification, including the application of ZX-calculus. The ultimate goal of this project is to formally prove the correctness of quantum circuit cutting.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Elmore Family School of Electrical and Computer Engineering, College of Engineering
Professor:
Jingbo
Wang
Quantifying the alteration of the antimicrobial resistome, virulome, and mobilome in cancer patients
Description:
Chronic or recurring bacterial infections are common complications in cancer patients, particularly those undergoing chemotherapy, due to immunosuppression caused by the disease and/or its treatment. These infections increase the risks of hospitalization, reduce treatment efficacy, and shorten cancer-related survival.
At the same time, antimicrobial resistance (AMR) in the gastrointestinal microbiome poses an additional challenge. Microbial communities can acquire or modify genes that enhance their survival under antibiotic pressure, increasing the risk of severe infections in cancer patients. Despite this, there has been limited effort to comprehensively study the burden and composition of AMR and virulence genes in cancer and chemotherapy patients. Furthermore, little is known about how these genes and associated virulence factors affect survival, treatment outcomes, and health complications across different cancer types and therapies.
This project aims to address these gaps by conducting a functional scoping review of metagenomic sequence data and metadata. It will require the use of bioinformatic pipelines to characterize AMR genes and their functional impact on cancer and chemotherapy patients. Students will be responsible for data management and generation, visualization, and scientific communication of the findings.
At the same time, antimicrobial resistance (AMR) in the gastrointestinal microbiome poses an additional challenge. Microbial communities can acquire or modify genes that enhance their survival under antibiotic pressure, increasing the risk of severe infections in cancer patients. Despite this, there has been limited effort to comprehensively study the burden and composition of AMR and virulence genes in cancer and chemotherapy patients. Furthermore, little is known about how these genes and associated virulence factors affect survival, treatment outcomes, and health complications across different cancer types and therapies.
This project aims to address these gaps by conducting a functional scoping review of metagenomic sequence data and metadata. It will require the use of bioinformatic pipelines to characterize AMR genes and their functional impact on cancer and chemotherapy patients. Students will be responsible for data management and generation, visualization, and scientific communication of the findings.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biotechnology Data Insights, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Experience or formal coursework in computer science, bioinformatics, computational biology, biostatistics, algorithmics, etc.
School/Dept.:
Department of Veterinary Clinical Sciences
Professor:
Ilya
Slizovskiy
More information: slizovskiy.com
Quantum Federated Learning for Biomedical Applications
Description:
This project presents a quantum federated learning (QFL) framework designed to improve the security, efficiency, and scalability of AI-driven biomedical image classification. AI models have the potential to transform medical imaging by helping early disease detection and diagnosis, but privacy regulations such as HIPAA and GDPR limit the ability of healthcare institutions to share patient data. Although federated learning (FL) allows for decentralized model training without direct data sharing, existing methods remain vulnerable to security risks, including adversarial attacks and model inversion threats.
The student is going to help in setting up the simulations and computational framework related to this project and run simualtions on Purdue's supercomputing infrastructure.
The student is going to help in setting up the simulations and computational framework related to this project and run simualtions on Purdue's supercomputing infrastructure.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Engineering
- Computer Science
- Mathematics - Computer Science
- Computer and Information Technology
- Physics
- Chemical Engineering
Desired experience:
Machine Learning, Quantum Mechanics, Quantum Computing
School/Dept.:
Davidson School of Chemical Engineering
Professor:
David
Bernal Neira
More information: https://secquoia.github.io/
Rare Earth MXenes for Magnetic Applications
Description:
This project will explore alteration of the MXene family of nanomaterials with the goal of ferromagnetism. The compositional space of MXenes positions it uniquely with potential for tuning to form a ferromagnetic nanomaterial with room temperature stability. The student on this project will participate in synthesis of the pre-cursor, MAX, and MXene form of rare-earth incorporated MXenes, specifically exploring a doping approach to introduce rare earth elements into the lattice of the material.
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: https://www.babakanasori.com
Real-Time Chamber Music Companion
Description:
Motivation: Chamber musicians sometimes need to gather for rehearsals. However, when the group is large, it is common that one or more musicians are absent during rehearsals due to schedule conflicts, illness, traffic congestion, etc. These absences can significantly reduce the effectiveness of rehearsals. This project aims to develop a computer program that serves as a real-time chamber music companion, capable of substituting for an absent musician during chamber music rehearsals.
Project goals: The tool has the following functions:
* Users can upload music in the form of MIDI or MusicXML of the absent musician. The tool can synthesize the audio of the musician.
* The tool can detect the place in the music score played by the human musicians.
* The tool can play the music that should be played by the absent musician and adjust the tempo based on the other musicians.
* The tool can accept voice commands, such as "louder" or "faster" and adjust the volume or the speed accordingly
This project will improve an existing project that already provides some of these functions.
Project goals: The tool has the following functions:
* Users can upload music in the form of MIDI or MusicXML of the absent musician. The tool can synthesize the audio of the musician.
* The tool can detect the place in the music score played by the human musicians.
* The tool can play the music that should be played by the absent musician and adjust the tempo based on the other musicians.
* The tool can accept voice commands, such as "louder" or "faster" and adjust the volume or the speed accordingly
This project will improve an existing project that already provides some of these functions.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Human Factors
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Engineering
- Computer Science
- Electrical Engineering
- Computer and Information Technology
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Yung Hsiang
Lu
More information: https://ai4musicians.org/
Research on Educational Technologies for Promoting Disciplinary Practices in STEM
Description:
This research project focuses on the integration of educational technology for teaching, learning, and training in STEM learning and working environments for the enactment of disciplinary practices. Disciplinary practices could include modeling, simulation, visualization, and other computational data science and engineering practices. Other practices could include experimentation, characterization, and design of systems, processes, and products. Potential educational technologies could include simulations, haptic devices, IoT devices, virtual and augmented reality, and Generative AI.
The project explores how these technologies can be harnessed to improve disciplinary engagement, foster adaptive expertise, improve human performance, or promote deeper learning outcomes for students. It also investigates how faculty development and interdisciplinary collaboration between researchers can help design and implement these advanced learning experiences effectively.
The project involves interdisciplinary collaboration, particularly between STEM practitioners and discipline-based education researchers. The research aims to foster synergies between these communities to push forward innovations in educational practices.
The role of the student will be determined based on their research interests and backgrounds. Potential student roles include (a) designing learning environments using technology, (b) collecting and analyzing data from human subjects, (c) implementing data science and education research methods to extract insights from educational data, and (d) writing a technical and research report, conference, or journal publication.
The project explores how these technologies can be harnessed to improve disciplinary engagement, foster adaptive expertise, improve human performance, or promote deeper learning outcomes for students. It also investigates how faculty development and interdisciplinary collaboration between researchers can help design and implement these advanced learning experiences effectively.
The project involves interdisciplinary collaboration, particularly between STEM practitioners and discipline-based education researchers. The research aims to foster synergies between these communities to push forward innovations in educational practices.
The role of the student will be determined based on their research interests and backgrounds. Potential student roles include (a) designing learning environments using technology, (b) collecting and analyzing data from human subjects, (c) implementing data science and education research methods to extract insights from educational data, and (d) writing a technical and research report, conference, or journal publication.
Campus:
West Lafayette
Research categories:
Human Factors, Learning and Evaluation
Citizenship requirements:
No citizenship requirements, U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Some experience with research, experience writing technical reports, and highly-motivated students.
School/Dept.:
Department of Computer and Information Technology
Professor:
Alejandra
Magana
Research on monitoring for trace level alpha radiation emitters in aqueous media
Description:
Alpha emitting radionuclides (e.g., U-238, Ra-226, Po-210) in aqueous media (e.g., water supplies from municipalities, well-water, runoff at nuclear processing facilities) pose a significant potential health and safety threat since their activity levels are often very low (at pCi/L) levels in natural background as well. Monitoring for such low levels is a laborious/time-consuming task which is routinely contracted out to off-site laboratories by over 50,000 municipalities and related organizations to demonstrate safe levels.
The proposed project would involve advancing Purdue’s novel TMFD (tensioned metastable fluid detector) sensor technology for trace-level alpha actiivity level monitoring - this involves concentration of the activity levels and extraction followed with entry into TMFDs and detection. The research work would entail largely experimental work alongside theoretical modeling.
The proposed project would involve advancing Purdue’s novel TMFD (tensioned metastable fluid detector) sensor technology for trace-level alpha actiivity level monitoring - this involves concentration of the activity levels and extraction followed with entry into TMFDs and detection. The research work would entail largely experimental work alongside theoretical modeling.
Campus:
West Lafayette
Research categories:
Energy and Environment, Environmental Characterization, Fluid Modelling and Simulation, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Familiarity with nuclear physics and engineering
School/Dept.:
School of Nuclear Engineering
Professor:
Rusi
Taleyarkhan
Rydberg Photonics: Combining the Best of Both Worlds for the Next Quantum Revolution
Description:
We are a group of physics, electrical, and computer engineers, and computer scientists who work on a novel interdisciplinary field of atom-nanophononics at the interface of atomic physics and quantum Nano-photonics. Our goal is to combine the best of both worlds to enable integrable, scalable, and robust quantum technologies on a chip like the electronic chips we are using daily.
In our lab, we are working with Rydberg excitons, i.e. highly-excited giant atom-like systems in solid states. The electron wavefunctions of such excitons can be as large as microns, making them some of the most unique quantum objects that have such macroscopic sizes. Due to their large size, Rydberg excitons interact with each other strongly via the long-range van der Waals force. In our Quantum Nano-Photonics (QNP) lab, we utilize high-resolution laser spectroscopy techniques at cryogenic temperature mainly developed in the atomic physics field to study these Rydberg excitons and measure their interactions. Later we make Nano-photonic circuits composed of the waveguide, resonators, and beam splitters and combine them with Rydberg excitons to control the properties of these highly excited atoms on the chip scale. We also use pulsed laser spectroscopy techniques to excite these Rydberg excitons momentarily and study their relaxation and time dynamics. Further, we combine this technique with high-resolution fast microscopy to map the exciton dynamics and propagation spatially.
Prior Experience – We are excited to host researchers with previous experience or knowledge of optics lab, such as working with lasers, aligning the beam, and working with mirrors and lenses to shape and control the beam path. Previous programming skills in Python, Julia, or QT are desired but not required. Since our lab is in the Nano-technology center the intern will have the opportunity of visiting one of the most well-equipped cleanroom facilities among all Universities in the US.
In our lab, we are working with Rydberg excitons, i.e. highly-excited giant atom-like systems in solid states. The electron wavefunctions of such excitons can be as large as microns, making them some of the most unique quantum objects that have such macroscopic sizes. Due to their large size, Rydberg excitons interact with each other strongly via the long-range van der Waals force. In our Quantum Nano-Photonics (QNP) lab, we utilize high-resolution laser spectroscopy techniques at cryogenic temperature mainly developed in the atomic physics field to study these Rydberg excitons and measure their interactions. Later we make Nano-photonic circuits composed of the waveguide, resonators, and beam splitters and combine them with Rydberg excitons to control the properties of these highly excited atoms on the chip scale. We also use pulsed laser spectroscopy techniques to excite these Rydberg excitons momentarily and study their relaxation and time dynamics. Further, we combine this technique with high-resolution fast microscopy to map the exciton dynamics and propagation spatially.
Prior Experience – We are excited to host researchers with previous experience or knowledge of optics lab, such as working with lasers, aligning the beam, and working with mirrors and lenses to shape and control the beam path. Previous programming skills in Python, Julia, or QT are desired but not required. Since our lab is in the Nano-technology center the intern will have the opportunity of visiting one of the most well-equipped cleanroom facilities among all Universities in the US.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Physics
Desired experience:
quantum mechanics, solid-state physics, optics
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Hadiseh
Alaeian
More information: https://engineering.purdue.edu/qnp
SBOMCtl: building a platform for efficient and secure software metadata exchange
Description:
Software supply chain attacks are a devastating attack vector affecting industry and government alike. To prevent these, secure software supply chain metadata exchange platforms have been proposed, but their architectures haven't been fully realized. As such, we require to design and build a secure platform to capture and distribute software supply chain metadata.
Campus:
West Lafayette
Research categories:
Cybersecurity, Internet of Things (IoT)
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Engineering
- Computer Science
Desired experience:
Computer Security, or equivalent
Operating Systems, or equivalent
Knowledge of confidential computing,
Golang programming preferred.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Santiago
Torres-Arias
More information: https://github.com/tselab/sbomctl
SCALE HI-AP: Engineering Materials for Thermal Transport for Semiconductor Packaging
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.
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.
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):
- 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:
• 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.
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.:
School of Mechanical Engineering
Professor:
Amy
Marconnet
More information: https://engineering.purdue.edu/MTEC
SCALE HI-AP: High-Temperature Solders for Aerospace and Defense
Description:
Research Areas:
Microelectronics, Technical Vertical name, other keywords
Description
Low-melting point metals based on tin are used to connect semiconductor packages to circuit boards. The specific solder composition that is chosen for a product depends on the product's use conditions, for example, consider the differences in use conditions for a cell phone, an implanted pacemaker, strapped onto a car engine, and in a satellite.
Micrograph of a solder for materials characterization
This project explores the performance and manufacturing differences between solders for different use cases as a function of composition and application. We are collaborating with researchers from Auburn University, the University of Maryland, Raytheon, BAE Systems, and the Department of Defense to develop a guide for solder selection for aerospace and defense applications. These researchers have backgrounds in materials engineering, mechanical engineering, industrial engineering, and electrical engineering. Many different skill sets are needed and you will see different perspectives.
This project will require extensive review of the literature and performing materials characterization, processing, manufacturing, and reliability experiments. Student researchers will learn a wide range of materials and mechanical property, processing, and characterization techniques and will work closely with faculty and graduate students from Materials Engineering and Mechanical Engineering.
Type of work
Review of the literature, materials characterization, processing, manufacturing, and reliability experiments.
Microelectronics, Technical Vertical name, other keywords
Description
Low-melting point metals based on tin are used to connect semiconductor packages to circuit boards. The specific solder composition that is chosen for a product depends on the product's use conditions, for example, consider the differences in use conditions for a cell phone, an implanted pacemaker, strapped onto a car engine, and in a satellite.
Micrograph of a solder for materials characterization
This project explores the performance and manufacturing differences between solders for different use cases as a function of composition and application. We are collaborating with researchers from Auburn University, the University of Maryland, Raytheon, BAE Systems, and the Department of Defense to develop a guide for solder selection for aerospace and defense applications. These researchers have backgrounds in materials engineering, mechanical engineering, industrial engineering, and electrical engineering. Many different skill sets are needed and you will see different perspectives.
This project will require extensive review of the literature and performing materials characterization, processing, manufacturing, and reliability experiments. Student researchers will learn a wide range of materials and mechanical property, processing, and characterization techniques and will work closely with faculty and graduate students from Materials Engineering and Mechanical Engineering.
Type of work
Review of the literature, materials characterization, processing, manufacturing, and reliability experiments.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Processing and Characterization, Microelectronics
Citizenship requirements:
U.S. Citizen
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:
• Any Engineering Major
3. Required Experience and Skills:
• Juniors and Seniors
• Motivation to learn
4. Desired experience:
• Analytical skills
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.
Websites
https://nanohub.org/groups/scale/research/purdue/surf
School/Dept.:
School of Materials Engineering
Professor:
Carol
Handwerker
SCALE HI-AP: Hybrid metamaterial designs with tunable physical properties for advanced photonic devices
Description:
Description
Unlike conventional integrated circuits (ICs), photonic integrated circuits (PIC) use photons instead of electrons to transmit and process information in both the classical and quantum regimes. They can provide transmission with higher interconnect bandwidths and long decoherence time for quantum systems. To enable these PICs, hetero-integration of various materials and device components is required similar to the integrated circuit designs in Si-based technology. Hybrid metamaterials that integrate two or more materials in one hybrid material with an artificially designed structure and physical properties are of interest as functional materials in potential PICs.
In this project, various oxide-metal-based vertically aligned nanocomposite thin film hybrid metamaterials will be designed, processed, and tested for targeted optical components in PICs, such as wave guide structures and optical switches. Tunability will be achieved by materials selection, composition tuning, growth parameter tuning, and multilayer stacking. The work will start with thin film deposition, structural and property measurements, and then follow with multilayer integration, lift-off and device integration and testing as illustrated in Figure 1. (See the project description in nanoHUB for the figure). Through this effort, a device component will be demonstrated and integrated for photonic applications and a technical paper is expected.
Type of work
Thin film deposition by pulsed laser deposition and sputtering, thin film transfer, electrical testing using a four point probe station, optical testing, microscopy analysis and image analysis, thin film structure design and property prediction by COMSOL simulation (wave module).
Unlike conventional integrated circuits (ICs), photonic integrated circuits (PIC) use photons instead of electrons to transmit and process information in both the classical and quantum regimes. They can provide transmission with higher interconnect bandwidths and long decoherence time for quantum systems. To enable these PICs, hetero-integration of various materials and device components is required similar to the integrated circuit designs in Si-based technology. Hybrid metamaterials that integrate two or more materials in one hybrid material with an artificially designed structure and physical properties are of interest as functional materials in potential PICs.
In this project, various oxide-metal-based vertically aligned nanocomposite thin film hybrid metamaterials will be designed, processed, and tested for targeted optical components in PICs, such as wave guide structures and optical switches. Tunability will be achieved by materials selection, composition tuning, growth parameter tuning, and multilayer stacking. The work will start with thin film deposition, structural and property measurements, and then follow with multilayer integration, lift-off and device integration and testing as illustrated in Figure 1. (See the project description in nanoHUB for the figure). Through this effort, a device component will be demonstrated and integrated for photonic applications and a technical paper is expected.
Type of work
Thin film deposition by pulsed laser deposition and sputtering, thin film transfer, electrical testing using a four point probe station, optical testing, microscopy analysis and image analysis, thin film structure design and property prediction by COMSOL simulation (wave module).
Campus:
West Lafayette
Research categories:
Heterogeneous Integration, Material Processing and Characterization, Microelectronics
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Materials Engineering
- Electrical 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:
• Materials Engineering
• Electrical Engineering
3) Required Experience and Skills:
• Incoming Junior and Senior students
• Previous experience in the following is preferred:
o Thin films
o Materials characterization (XRD, SEM, AFM and others)
o Property measurement (electrical, optical and magnetic property testing)
4) Desired experience:
• Some prior experience in the following fields will be desired:
o Data analysis and plotting using Origin or Excel
o Image processing using Photoshop
o COMSOL simulation
o Lab view and Python coding experience
o Technical writing and presentation
o Literature review and summary
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.
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.
Read more about Purdue SURF here: https://engineering.purdue.edu/Engr/Research/EURO/students/about-SURF
Websites:
More detailed project description in nanoHUB:
https://nanohub.org/groups/scale/research/purdue/surf
School/Dept.:
School of Materials Engineering
Professor:
Haiyan
Wang
More information: https://nanohub.org/groups/scale/research/purdue/surf
SCALE HI-AP: Interconnect Schemes for 3D Heterogeneous Integration and Advanced Packaging
Description:
Research Areas:
Heterogeneous Integration and Advanced Packaging, Interconnect technologies, thin-film materials
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 project description on the nanoHUB site 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.
Heterogeneous Integration and Advanced Packaging, Interconnect technologies, thin-film materials
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 project description on the nanoHUB site 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):
- 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:
• 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.
Apply here: https://engineering.purdue.edu/Engr/Research/EURO/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. Read more about Purdue SURF here: https://engineering.purdue.edu/Engr/Research/EURO/students/about-SURF.
Websites
https://engineering.purdue.edu/MTEC
https://nanohub.org/groups/scale/research/purdue/surf
School/Dept.:
School of Mechanical Engineering
Professor:
Shubhra
Bansal
More information: https://engineering.purdue.edu/MTEC
SCALE HI-AP: Multijunction devices for electroluminescent on-chip cooling of 3D Stacked-Die Assembly
Description:
Research Areas:
Heterogeneous Integration and Advanced Packaging, optoelectronic materials, thin-film semiconductors, photovoltaics, light emitting diodes
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.
Heterogeneous Integration and Advanced Packaging, optoelectronic materials, thin-film semiconductors, photovoltaics, light emitting diodes
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.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Modeling and Simulation, Microelectronics, Thermal Technology
Citizenship requirements:
U.S. Citizen
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:
• 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.
Apply here: https://engineering.purdue.edu/Engr/Research/EURO/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.
Websites
https://engineering.purdue.edu/MTEC
https://nanohub.org/groups/scale/research/purdue/surf
School/Dept.:
School of Mechanical Engineering
Professor:
Shubhra
Bansal
More information: https://engineering.purdue.edu/MTEC
SCALE HI-AP: Passive Two-Phase Thermosyphons for Energy Efficient Semiconductor Thermal Management
Description:
Description
Thermosyphons are widely used for thermal management in applications ranging from power electronics cooling to building heating ventilation and air conditioning, and others. A closed loop thermosyphon (CLT) is a gravity-assisted heat exchanger that stands out for its ability to transfer heat without relying on a pump. Additionally, CLTs have no moving parts, which makes them affordable, easy to maintain, and more reliable than pumped cooling systems.
Despite these advantages, CLTs face challenges related to operational flow instabilities that cause undesired fluctuations in temperature, flow rate, and pressure which shorten the life of the CLT. Geyser boiling instabilities (GBIs) are known to occur in small-diameter CLTs with low-pressure fluids. While GBI has been studied extensively in reflux thermosyphons, there are few studies that investigate GBI in CLTs.
This project is an experimental exploration of GBI in a small-diameter CLT with the aim of developing a predictive model for the onset of geyser boiling. For this work, a fully transparent CLT has been constructed with the capabilities of collecting local temperature and pressure data, high-speed flow visualization, and flow rate as shown in the image below. (See project on the nanoHUB website for the image).
Thermosyphons are widely used for thermal management in applications ranging from power electronics cooling to building heating ventilation and air conditioning, and others. A closed loop thermosyphon (CLT) is a gravity-assisted heat exchanger that stands out for its ability to transfer heat without relying on a pump. Additionally, CLTs have no moving parts, which makes them affordable, easy to maintain, and more reliable than pumped cooling systems.
Despite these advantages, CLTs face challenges related to operational flow instabilities that cause undesired fluctuations in temperature, flow rate, and pressure which shorten the life of the CLT. Geyser boiling instabilities (GBIs) are known to occur in small-diameter CLTs with low-pressure fluids. While GBI has been studied extensively in reflux thermosyphons, there are few studies that investigate GBI in CLTs.
This project is an experimental exploration of GBI in a small-diameter CLT with the aim of developing a predictive model for the onset of geyser boiling. For this work, a fully transparent CLT has been constructed with the capabilities of collecting local temperature and pressure data, high-speed flow visualization, and flow rate as shown in the image below. (See project on the nanoHUB website for the image).
Campus:
West Lafayette
Research categories:
Advanced Packaging, Energy and Environment, Heterogeneous Integration, Microelectronics, Thermal Technology
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Mechanical 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:
• Mechanical Engineering
3. Required Experience and Skills: N/A
4. Desired experience: N/A
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.
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.:
School of Mechanical Engineering
Professor:
Justin
Weibel
More information: https://engineering.purdue.edu/CTRC/research/index.php
SCALE HI-AP: Proximity Correction for Microscale 3D Printing Using Topology Optimization
Description:
Research Areas:
Microelectronics, Heterogeneous Integration and Advanced Packaging,
Description
Nano and micro-scale three-dimensional (3D) printing can be a powerful tool for future manufacturing at small scale, as shown in the Figure. However, the proximity effect, i.e., structure placement, starts to strongly affect their neighboring structures and cause undesired distortions. A practical solution is to perform correction steps to the printing pattern to account for the distortion, similar to the optical proximity correction approach used in 2D lithography. Here we will implement a microscale 3D printing method with software and hardware infrastructure that can support the optimized placements of 3D shapes based on the Topology Optimization method. Participants will learn the previous development and build upon existing infrastructures.
Type of work
Participants will learn the previous development and build upon existing infrastructures to develop a topology algorithm and software module that optimizes the printing parameters for the targeted 3D structures.
Microelectronics, Heterogeneous Integration and Advanced Packaging,
Description
Nano and micro-scale three-dimensional (3D) printing can be a powerful tool for future manufacturing at small scale, as shown in the Figure. However, the proximity effect, i.e., structure placement, starts to strongly affect their neighboring structures and cause undesired distortions. A practical solution is to perform correction steps to the printing pattern to account for the distortion, similar to the optical proximity correction approach used in 2D lithography. Here we will implement a microscale 3D printing method with software and hardware infrastructure that can support the optimized placements of 3D shapes based on the Topology Optimization method. Participants will learn the previous development and build upon existing infrastructures.
Type of work
Participants will learn the previous development and build upon existing infrastructures to develop a topology algorithm and software module that optimizes the printing parameters for the targeted 3D structures.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Heterogeneous Integration, Microelectronics
Citizenship requirements:
U.S. Citizen
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:
• Any Engineering major
• Any STEM major
3. Required Experience and Skills:
• Programming using MATLAB or python.
• College mathematics including multi-variable derivatives and matrix operations.
4. Desired experience:
• Previous research experience will help but not necessary.
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.
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.
Websites
https://nanohub.org/groups/scale/research/purdue/surf
School/Dept.:
School of Mechanical Engineering
Professor:
Liang
Pan
SCALE HI-AP: System Demonstrators for Energy-Efficient Compute-In-Memory AI Chiplets
Description:
Research Areas:
Microelectronics, Heterogeneous Integration & Advanced Packaging, Artificial Intelligence
Description
The rapid growth of artificial intelligence (AI), especially Large Language Models (LLMs) like GPT, is revolutionizing the way people work, learn, communicate, and access healthcare. Due to the complexity of AI workloads and the limitations of today’s semiconductor hardware, computing with powerful LLMs incurs enormous energy costs and generates significant carbon footprints. This project will develop a holistic computing solution to provide reliable, private, and energy-efficient computing capabilities directly to end users’ devices, thereby democratizing access to advanced AI for the broader society. Our team is synergizing semiconductor material advancements with novel chip designs and algorithms to create a new computing platform that will run AI workloads more efficiently than existing silicon-based platforms.
Type of work
• The SURF researcher will be mentored by faculty and graduate students to study, investigate, identify, and explore new algorithm-hardware co-design opportunities for the new AI hardware platform under development by the team.
• Develop and iterate new application-specific, system-level demonstrators using the software artifacts and hardware prototypes that the team generated so far.
• Design the PCB board for multi-chiplet prototypes.
• Connect software and hardware demos to broader, interesting real-world applications.
• Software/hardware "integrated live demo", using FPGA-PCB test boards.
• Document research results with a target for IEEE/ACM conference publications (student's travel will be sponsored by the faculty).
Microelectronics, Heterogeneous Integration & Advanced Packaging, Artificial Intelligence
Description
The rapid growth of artificial intelligence (AI), especially Large Language Models (LLMs) like GPT, is revolutionizing the way people work, learn, communicate, and access healthcare. Due to the complexity of AI workloads and the limitations of today’s semiconductor hardware, computing with powerful LLMs incurs enormous energy costs and generates significant carbon footprints. This project will develop a holistic computing solution to provide reliable, private, and energy-efficient computing capabilities directly to end users’ devices, thereby democratizing access to advanced AI for the broader society. Our team is synergizing semiconductor material advancements with novel chip designs and algorithms to create a new computing platform that will run AI workloads more efficiently than existing silicon-based platforms.
Type of work
• The SURF researcher will be mentored by faculty and graduate students to study, investigate, identify, and explore new algorithm-hardware co-design opportunities for the new AI hardware platform under development by the team.
• Develop and iterate new application-specific, system-level demonstrators using the software artifacts and hardware prototypes that the team generated so far.
• Design the PCB board for multi-chiplet prototypes.
• Connect software and hardware demos to broader, interesting real-world applications.
• Software/hardware "integrated live demo", using FPGA-PCB test boards.
• Document research results with a target for IEEE/ACM conference publications (student's travel will be sponsored by the faculty).
Campus:
West Lafayette
Research categories:
Advanced Packaging, Heterogeneous Integration, Microelectronics
Citizenship requirements:
U.S. Citizen
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:
• All Engineering majors can apply.
3. Required Experience and Skills: N/A
4. Desired experience:
Students with various backgrounds from software, machine learning, computer architecture, to VLSI and semiconductors are all welcome to apply, and will have a unique opportunity through this project to get exposed to a truly cross-cutting research topic – "AI hardware co-designs". Some specific skills/experience may be able to provide a jumpstart and boost the productivity during the summer:
• Programming skills (Python, popular ML frameworks)
• Prototyping experience with FPGA boards
• PCB board design
• Software development
• Basic knowledge about VLSI circuits and semiconductor devices
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.
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. Read more about Purdue SURF here: https://engineering.purdue.edu/Engr/Research/EURO/students/about-SURF
Websites
https://engineering.purdue.edu/NanoX/research/
https://nanohub.org/groups/scale/research/purdue/surf
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Haitong
Li
More information: https://engineering.purdue.edu/NanoX/research/
SCALE HI-AP: Thermometers, Strain Gauges and Defect Detectors for Semiconductors using Light
Description:
Who we are…
Specere is a latin word (pronounced Spuh-Seer) 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 infrared physics to create spectroscopic, thermal, and sensing solutions.
Research Topic, Semiconductor Sensing:
Semiconductor chips are some of the most technologically advanced machines humanity has ever made. Like any complex machine, they break. Methods for predicting where they will break and why they have broken are therefore necessary. You will help us make the thermometers, strain gauges, and “defect detectors” that are up to the task using some of the world’s most advanced semiconductor characterization tools that we develop here at Purdue.
What You’ll Do:
Team members will be responsible for performing spectroscopic measurements of next generation semiconductor materials, devices, and packages. Specifically, you will use Raman (sounds like but is not the noodle) and photoluminescence (fancy for glow in the dark) to image the temperature, stress, and presence of defects in everything from commercial logic chips to materials being considered for next generation memory devices. 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.
Specere is a latin word (pronounced Spuh-Seer) 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 infrared physics to create spectroscopic, thermal, and sensing solutions.
Research Topic, Semiconductor Sensing:
Semiconductor chips are some of the most technologically advanced machines humanity has ever made. Like any complex machine, they break. Methods for predicting where they will break and why they have broken are therefore necessary. You will help us make the thermometers, strain gauges, and “defect detectors” that are up to the task using some of the world’s most advanced semiconductor characterization tools that we develop here at Purdue.
What You’ll Do:
Team members will be responsible for performing spectroscopic measurements of next generation semiconductor materials, devices, and packages. Specifically, you will use Raman (sounds like but is not the noodle) and photoluminescence (fancy for glow in the dark) to image the temperature, stress, and presence of defects in everything from commercial logic chips to materials being considered for next generation memory devices. 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.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Modeling and Simulation, Microelectronics
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
- Materials Engineering
- Physics
- Mechanical Engineering
- Electrical 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:
• Your major doesn’t matter as long as it’s in a STEM-related field.
3) Required Experience and Skills:
• All applicants should be capable of working independently while effectively communicating within a team setting.
• Strong motivation and problem-solving ability.
• Juniors and Seniors
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.
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.
Websites:
• www.specere.org
• https://nanohub.org/groups/scale/research/purdue/surf
School/Dept.:
School of Mechanical Engineering
Professor:
Thomas
Beechem
More information: www.specere.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):
- No Major Restriction
- 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: Development and application of nuclear techniques in human health
Description:
Research Areas:
Neutron activation analysis, x-ray fluorescence, synchrotron x-ray techniques, human body composition, neurodegeneration, medical physics, machine learning/ deep learning
Description
Dr. Nie’s group works on designing radiation instruments and methods to be used in human health. Students in her lab engage in projects aimed at developing neutron and xray technologies to quantify metals and trace elements in human bone and tissues in vivo. They also perform high resolution mapping of elemental concentration and speciation in human and animal brains using cutting-edge synchrotron facilities. These novel techniques (See Figure 1) are used to investigate metal exposure and health, nutrition and health, and metal exposure and neurodegeneration.
Figure 1: (See the project description in nanoHUB to view the figure) 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)
Students will conduct Monte Carlos simulations, experiments, and data analysis as part of their research. The research focus has recently been broadened to explore the application of machine learning and deep learning for identifying elemental distribution patterns, aiming to investigate how metals and trace elements in the brain are linked to various neuropathies, particularly in the context of Alzheimer’s and Parkinson’s diseases.
Type of work
Student can select: Conduct Monte Carlo simulations on radiation transportation, radiation instrumentation design and development, perform experiments with the neutron generator, xray devices, and radiation detectors available in her 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, application of machine learning and deep learning on metal mapping to study metals and neurodegeneration.
Neutron activation analysis, x-ray fluorescence, synchrotron x-ray techniques, human body composition, neurodegeneration, medical physics, machine learning/ deep learning
Description
Dr. Nie’s group works on designing radiation instruments and methods to be used in human health. Students in her lab engage in projects aimed at developing neutron and xray technologies to quantify metals and trace elements in human bone and tissues in vivo. They also perform high resolution mapping of elemental concentration and speciation in human and animal brains using cutting-edge synchrotron facilities. These novel techniques (See Figure 1) are used to investigate metal exposure and health, nutrition and health, and metal exposure and neurodegeneration.
Figure 1: (See the project description in nanoHUB to view the figure) 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)
Students will conduct Monte Carlos simulations, experiments, and data analysis as part of their research. The research focus has recently been broadened to explore the application of machine learning and deep learning for identifying elemental distribution patterns, aiming to investigate how metals and trace elements in the brain are linked to various neuropathies, particularly in the context of Alzheimer’s and Parkinson’s diseases.
Type of work
Student can select: Conduct Monte Carlo simulations on radiation transportation, radiation instrumentation design and development, perform experiments with the neutron generator, xray devices, and radiation detectors available in her 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, application of machine learning and deep learning on metal mapping to study metals and neurodegeneration.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Deep Learning, Medical Science and Technology, Radiation Hardening
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- Nuclear Engineering
- Physics
- Radiation Sciences
- Biomedical Engineering
- 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:
• Nuclear Engineering
• Physics
• Radiation Sciences
• Biomedical Engineering
• Computer Science
3. Required Experience and Skills:
• Juniors and Seniors
• Motivation to learn
4. Desired experience:
• Analytical skills
• Programming
• Basic knowledge on radiation sciences
• Interest in human health
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.
Websites
https://hhs.purdue.edu/directory/linda-nie/
https://nanohub.org/groups/scale/research_opportunities/research_purdue
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):
- No Major Restriction
- 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.
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.:
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):
- No Major Restriction
- 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.
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.:
School of Nuclear Engineering
Professor:
Allen
Garner
More information: https://sites.google.com/site/garnerresearchgroup/
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):
- No Major Restriction
- 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:
• 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.
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. 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:
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. The team is organized like a small chip design company with sub-teams for logic design, verification, chip-layout, analog design, printed circuit board (PCB) design, test, software, and special research projects in collaboration with research groups in ECE. Special projects include applications in hardware security and GPU design. Based on your interests and background, team leaders will work with you to assign you to an appropriate sub-team or special project. Because of the wide range of projects, the experience and skill requirements for SoCET are flexible. Almost any kind of background in circuit design, logic design, circuit simulation, computer architecture, and microcontroller programming will be useful in some part of the team. For more details on possible projects and sub-teams, see https://engineering.purdue.edu/SoC-Team.
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 to 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.
To apply:
In your application, be sure to talk about this specific SCALE project. By applying to a SCALE project, you can be considered for other SCALE projects as well; please indicate which of the SCALE projects or project areas you are most interested in. Be sure to state how you satisfy the project requirements and how this project relates to your academic and career goals.
You can view all SCALE SURF projects here: https://nanohub.org/groups/scale/research/purdue/surf
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 to 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.
To apply:
In your application, be sure to talk about this specific SCALE project. By applying to a SCALE project, you can be considered for other SCALE projects as well; please indicate which of the SCALE projects or project areas you are most interested in. Be sure to state how you satisfy the project requirements and how this project relates to your academic and career goals.
You can view all SCALE SURF projects here: https://nanohub.org/groups/scale/research/purdue/surf
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:
Qualifications:
1) 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:
Selected participants will usually be taken from electrical engineering, computer engineering, or computer science, but other majors will be considered if one has skills or experience directly relevant to chip design and testing.
3) Required Experience and Skills:
None except strong motivation and problem-solving ability.
4) Desired experience:
Desired skills and experience include digital design and simulation using Verilog, analog circuit design, printed circuit board design, computer processor design, IC testing, and microcontroller programming.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Mark
Johnson
More information: https://engineering.purdue.edu/SoC-Team
SGLT2 Inhibitors for the Treatment of Cardiomyopathy in Duchennes Muscular Dystrophy
Description:
Duchenne’s Muscular Dystrophy (DMD) patients each carry one of many different X-linked mutations of the dystrophin gene, resulting in no dystrophin synthesis, and it leads to premature loss of ambulatory mobility, respiratory distress, left ventricle (LV) dilation, low left ventricular ejection fraction (LVEF), increase of LV volume, arrhythmia, sudden cardiac death (SCD) and all-cause mortality. Survival is very low after 35 years of age. Ischemic Heart Failure with reduced Ejection Fraction (HFrEF) is common, where patients have increased the incidence of arrhythmia, SCD and all-cause mortality. Despite completely different etiology and rate of disease progression, Duchenne’s cardiomyopathy and HFrEF have similar cardiac dysfunction phenotypes and outcomes.
New HF drugs that block the sodium-glucose linked transporter 2 (SGLT2) in the kidney proximal tubules, called SGLT2 inhibitors (SGLT2i), dramatically improves cardiac function, structure, morbidity and mortality in HFrEF patients (without diabetes). SGLT2i treatment of various rodent models of ischemic HFrEF or non-ischemic cardiomyopathy with low LVEF, also produced cardiac function and structural benefits. Mechanistically, SGLT2i blocks increase natriuresis and diuresis, reducing blood volume, peripheral vascular resistance, cardiac preload and afterload leading to improved cardiac performance. SGLT2i treatment elevated production of ketone bodies (beta-hydroxybutyrate) providing an additional source of cardiac fuel. They reduce cardiomyocyte apoptosis, aberrant ion channel expression, increase cell survival and function.
The student will work to quantify (i) heart function and structure in the mouse D2.mdx cardiomyopathy model and on (ii) survival and function in IPSC-derived cardiomyocytes with various mutations of dystrophin. The intent is to provide evidence supporting a proof-of-concept study in DMD patients with cardiomyopathy.
New HF drugs that block the sodium-glucose linked transporter 2 (SGLT2) in the kidney proximal tubules, called SGLT2 inhibitors (SGLT2i), dramatically improves cardiac function, structure, morbidity and mortality in HFrEF patients (without diabetes). SGLT2i treatment of various rodent models of ischemic HFrEF or non-ischemic cardiomyopathy with low LVEF, also produced cardiac function and structural benefits. Mechanistically, SGLT2i blocks increase natriuresis and diuresis, reducing blood volume, peripheral vascular resistance, cardiac preload and afterload leading to improved cardiac performance. SGLT2i treatment elevated production of ketone bodies (beta-hydroxybutyrate) providing an additional source of cardiac fuel. They reduce cardiomyocyte apoptosis, aberrant ion channel expression, increase cell survival and function.
The student will work to quantify (i) heart function and structure in the mouse D2.mdx cardiomyopathy model and on (ii) survival and function in IPSC-derived cardiomyocytes with various mutations of dystrophin. The intent is to provide evidence supporting a proof-of-concept study in DMD patients with cardiomyopathy.
Campus:
West Lafayette
Research categories:
Cardiovascular Disease Research
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Craig
Goergen
More information: https://engineering.purdue.edu/cvirl
Schistosoma egg migration characterization via hydrogels
Description:
Undergraduate student will work on 1) manufacturing polyacrylamide gels with different concentration to mimic the cell migration of Schitosoma eggs through cell walls 2) image the microbead system to mimic the cell encapulation under the fluorescence microscope and 3) utilize the matlab code to develop a quantitative 2D spatial vector for describing the cell migration
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
Desired experience:
Prioer experience w/ gel making, cell culture techniques and matlab coding experience
School/Dept.:
School of Mechanical Engineering
Professor:
Euiwon
Bae
Seismic behavior of steel to concrete connections
Description:
This project investigates the performance of steel column-to-concrete foundation connections under various loading conditions, including seismic forces. Large-scale experiments and finite element analyses are used to study the behavior of these connections under monotonic and cyclic loading, replicating seismic forces. The findings aim to enhance the design of earthquake-resilient structures by improving the understanding of anchorage behavior during seismic events and developing simplified models to assist engineers in designing safer and more efficient connections. As part of this project, the undergraduate student will assist PhD students with experimental setups and computer simulations. Experimental work will take place at the Bowen Laboratory at Purdue University, providing hands-on experience with cutting-edge research.
Campus:
West Lafayette
Research categories:
Composite Materials and Alloys, Energy and Environment, Engineering the Built Environment, Learning and Evaluation, Material Modeling and Simulation, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Civil Engineering
- Mechanical Engineering
- Mechanical Engineering Technology
- Aeronautical Engineering Technology
- Aeronautical and Astronautical Engineering
- Mechatronics Engineering Technology
- Materials Engineering
- Construction Engineering
- Industrial Engineering
- Industrial Design
- Industrial Engineering Technology
- Nuclear Engineering
School/Dept.:
Lyles School of Civil Engineering
Professor:
Akanshu
Sharma
More information: https://www.akanshusharma.com/
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.:
School of Industrial Engineering
Professor:
Denny
Yu
More information: https://engineering.purdue.edu/YuGroup
Shock tube experimentation and high-speed shock wave imaging
Description:
The student(s) will work with Prof. Jewell and his graduate students to operate and improve the Purdue University 3-inch Shock Tube, with applications in shock wave measurements, high-speed videography of 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 like (Focused) Laser Differential Interferometry.
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:
Required: 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.
Desired: Experience with Labview, pressure transducers, high speed cameras, Overleaf/LaTeX, vacuum pumps and high-pressure systems.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Joseph
Jewell
More information: https://engineering.purdue.edu/AAE/people/ptProfile?resource_id=221718
Simulating how soft materials tear, fracture, and fail
Description:
Have you wondered how you might simulate slicing cheese or peeling of scotch tape? Are you interested in learning to do research on such cutting-edge computational mechanics? Join our summer undergraduate research project, in which you will learn to use PDMATLAB2D, an open-source MATLAB software based on the nonlocal theory of peridynamics, to perform advanced structural analysis and simulate how materials fail (fracture, tear, etc.). Unlike traditional methods, peridynamics excels at modeling the formation and propagation of discontinuities like cracks and fractures, making it a powerful tool for understanding material behavior under complex loading conditions. We're looking for motivated students with a strong background in MATLAB and computations. If you're curious about nonlocal mechanics, fracture and failure of materials, or passionate about structural analysis, we'd love to have you on our team!
You'll sharpen your mechanics, coding, and computation skills and maybe even become an author on a ground-breaking peer-reviewed manuscript in a top mechanics journal if you put in the effort!
You'll sharpen your mechanics, coding, and computation skills and maybe even become an author on a ground-breaking peer-reviewed manuscript in a top mechanics journal if you put in the effort!
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology, Composite Materials and Alloys, Fluid Modelling and Simulation, Material Modeling and Simulation, Nanotechnology, System-on-a-Chip
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Chemical Engineering
- Physics
- Mathematics
- Civil Engineering
- Computer Engineering
- Materials Engineering
- Computer Science
Desired experience:
Strong programming skills in MATLAB;
Strong performance in foundational engineering and mathematics courses;
Desire to learn about fundamental scientific research;
Self-starter able to follow through on tasks and goals;
Ability to work in a team;
School/Dept.:
School of Mechanical Engineering
Professor:
Ivan
Christov
More information: https://tmnt-lab.org/
Software Development for Electric Vehicle Thermal Management
Description:
We are in search of a passionate undergraduate researcher eager to join our cutting-edge Electric Vehicle (EV) thermal management project. You'll play a pivotal role in creating a sophisticated Graphical User Interface (GUI) that powers our advanced research. Our research involves leveraging graph-based models to analyze and optimize transient closed-loop performance in EV thermal management systems.
In this role, you'll select your preferred GUI framework to integrate Python-based models and tools into a seamless software package. You'll work both autonomously and collaboratively with our dynamic, cross-functional team, driving innovation at every step.
What You'll Gain:
UI/UX Mastery: Design intuitive, user-friendly interfaces.
Software Development: Enhance coding skills through hands-on experience.
GUI Frameworks: Work with the latest tools.
Graph Theory in Energy Management: Apply mathematical concepts to real-world challenges.
Version Control: Master tools like Github
In this role, you'll select your preferred GUI framework to integrate Python-based models and tools into a seamless software package. You'll work both autonomously and collaboratively with our dynamic, cross-functional team, driving innovation at every step.
What You'll Gain:
UI/UX Mastery: Design intuitive, user-friendly interfaces.
Software Development: Enhance coding skills through hands-on experience.
GUI Frameworks: Work with the latest tools.
Graph Theory in Energy Management: Apply mathematical concepts to real-world challenges.
Version Control: Master tools like Github
Campus:
West Lafayette
Research categories:
Energy and Environment, Thermal Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
- Computer Science
- Industrial Engineering
- Game Development and Design
- Computer and Information Technology
Desired experience:
Python Proficiency. Innovative Problem Solvers. Prior experience with GUIs and software engineering will set you apart.
School/Dept.:
School of Mechanical Engineering
Professor:
Neera
Jain
Spectral decomposition of James Webb Space Telescope Observations of Supernova Remnant Cassiopeia A
Description:
The superior resolution and sensitivity to near- and mid-infrared wavelengths of the James Webb Space Telescope (JWST) opens new pathways to investigate critical questions about the nature of massive star explosions via observations of young supernova remnants (SNRs). Recent observations made by JWST of the youngest Galactic core-collapse SNR Cassiopeia A provide many opportunities to address outstanding questions about its progenitor system and explosion mechanism(s). Unfortunately, disentangling the many sources of emission (thermal vs nonthermal, ejecta vs CSM vs light echo) present in JWST images can complicate analysis. This student project involves creating a new method of analysis of JWST data that will distinguish overlapping emission from the circumstellar medium and the supernova ejecta, excited by the forward and reverse shocks, respectively. The student will utilize a powerful technique called "spectral decomposition" and aim to provide a tool that can be broadly used in the astrophysical community.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Physics and Astronomy
Professor:
Danny
Milisavljevic
More information: https://www.physics.purdue.edu/milisavljevic/
Standard test structure and protocols for in space manufacturing
Description:
Researchers will work in collaborative projects involving:
• Design, manufacturing, and characterization of a test or set of test structures for in-space manufacturing.
• Space-viable test protocols development and ground-demonstrations for relevant destructive and nondestructive testing methods.
• Multi-material manufacturing of regolith-metal and regolith-polymer samples using a variety of additive manufacturing.
• Integration of analytical, numerical and finite element modeling and analysis of test structure performance with expertimental data.
• Design, manufacturing, and characterization of a test or set of test structures for in-space manufacturing.
• Space-viable test protocols development and ground-demonstrations for relevant destructive and nondestructive testing methods.
• Multi-material manufacturing of regolith-metal and regolith-polymer samples using a variety of additive manufacturing.
• Integration of analytical, numerical and finite element modeling and analysis of test structure performance with expertimental data.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Composite Materials and Alloys, Ecology and Sustainability, Energy and Environment, Material Modeling and Simulation, Material Processing and Characterization
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
• ME 263 and ME 323 or equivalent
• Experience in one or more of the following:
o manufacturing and additive manufacturing processes
o materials characterization testing
o CAD and programming experience (Matlab, Python)
o Sensors and measurement systems
o Analytical modeling and statistical analysis
School/Dept.:
School of Mechanical Engineering
Professor:
Luz
Sotelo
More information: https://engineering.purdue.edu/MMRL
Statistical Process Control of Semiconductor Processes & Digital Twins
Description:
The student will learn how to operate one or more semiconductor processing tools in the Birck Nanotechnology Center cleanroom. Once proficient on the tool the student will perform test runs and measure results from the system. These results will be charted on a control chart using Statistical Process Control techniques. Once a sufficient number of runs is accomplished the student will work with NanoHub to not only plot the results but also to begin providing input parameters so that a digital twin of the tool can be developed.
Campus:
West Lafayette
Research categories:
Microelectronics, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Semiconductors, computer modeling, statistics would be helpful.
School/Dept.:
Office of Research
Professor:
Ronald
Reger
More information: https://birck.research.purdue.edu/
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:
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
String Musician Evaluator
Description:
Motivation: Music students (and professional musicians) often practice long hours in order to perfect their performance skills. The repetitive motions and wrong posture could lead to discomfort or even injuries. This project aims to create a computer tool that can analyze the video captured during a musician's practice and identify inappropriate postures that could lead to injuries.
Project goals: The tool has the following functions:
* Analyze video during a music student's and detect the body parts (e.g., fingers, arms, elbow, shoulders).
* Compare the student's posture and compare it with professional musicians' postures.
* Provide suggestions to improve the student's posture.
This project will improve an existing project that already provides some of these functions. This project will focus on cello as the first case study. After the solution is mature, the project will expand to other types of musical instruments.
Project goals: The tool has the following functions:
* Analyze video during a music student's and detect the body parts (e.g., fingers, arms, elbow, shoulders).
* Compare the student's posture and compare it with professional musicians' postures.
* Provide suggestions to improve the student's posture.
This project will improve an existing project that already provides some of these functions. This project will focus on cello as the first case study. After the solution is mature, the project will expand to other types of musical instruments.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Human Factors
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Engineering
- Computer and Information Technology
- Computer Science
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Yung Hsiang
Lu
More information: https://ai4musicians.org/
Structural and Functional Characterization of Metalloproteins Involved in Human Iron Homeostasis
Description:
We are seeking highly motivated undergraduate students to join our research lab, which focuses on uncovering the mechanisms of human iron homeostasis. This project offers an exciting opportunity to investigate how cells regulate iron levels and how disruptions in iron metabolism contribute to human diseases. Students will play an active role in identifying and characterizing novel metalloproteins critical for maintaining iron balance within cells.
Students will engage in cutting-edge research using chemical biology techniques to investigate protein-protein and protein-RNA interactions involved in iron regulation. Throughout the project, you will gain hands-on experience with a variety of advanced techniques, including:
• Mammalian cell culture for studying cellular processes and protein interactions.
• Proteomics to analyze protein composition and function.
• Cell imaging for visualizing cellular structures and interactions.
• Next-generation sequencing (NGS) to uncover molecular mechanisms underlying metal ion regulation.
This project will provide valuable exposure to several key techniques in molecular biology and chemical biology, offering students an immersive experience in scientific discovery.
Students will engage in cutting-edge research using chemical biology techniques to investigate protein-protein and protein-RNA interactions involved in iron regulation. Throughout the project, you will gain hands-on experience with a variety of advanced techniques, including:
• Mammalian cell culture for studying cellular processes and protein interactions.
• Proteomics to analyze protein composition and function.
• Cell imaging for visualizing cellular structures and interactions.
• Next-generation sequencing (NGS) to uncover molecular mechanisms underlying metal ion regulation.
This project will provide valuable exposure to several key techniques in molecular biology and chemical biology, offering students an immersive experience in scientific discovery.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Qualifications:
• Undergraduate students majoring in Biology, Biochemistry, or related fields.
• Strong interest in molecular biology and biochemistry.
• Enthusiasm for learning new experimental techniques and contributing to impactful research.
School/Dept.:
Chemistry
Professor:
Shiliang
Tian
More information: https://www.chem.purdue.edu/tian/index.html
Structural and computational analyses of virus-antibody complexes
Description:
This project will entail the analysis of structural data of virus-antibody complexes. Once a complex is identified at a near-atomic resolution, computation tools will be applied to enhance the resolution and features of the protein-protein interactions will be identified. The SURF student will participate in virus preparation and complex formation. Data will be acquired using cryo-electron microscopy and further analyzed by in house and commercially available computational software.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Basic chemistry, biochemistry, biology background. Knowledge of micobes and/immunology would be good.
School/Dept.:
Biological Sciences
Professor:
Richard
Kuhn
Structure and Function of G Protein-Coupled Receptor Kinase 3
Description:
Of the seven human G protein-coupled receptor (GPCR) kinases (GRKs), only two have not yet been structurally characterized. GRK3 is expressed broadly, but plays a prominent role in olfactory sensation. Atomic models of GRK3 would be useful to understand how to build selective inhibitors against GRK2, its close homolog, and would provide another tool for us to use in trapping complexes between GRKs and their target GPCRs.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cardiovascular Disease Research, Cellular Biology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
At least 2 semesters experience working in a modern biochemistry research focussed lab is required.
School/Dept.:
Biological Sciences
Professor:
John
Tesmer
Structure, Function and Inhibition of Enzymes Involved in Cancer, Alzheimers Disease and Coronavirus Infections.
Description:
Or lab elucidates the structure and molecular mechanisms of therapeutic enzymes and proteins with the goal of using this information to design new therapeutics. We work on different enzyme “drug targets” that are involved in cancer, Alzheimer’s Disease and coronavirus infections. Students selected will learn how to purify these enzymes and perform biochemical assays to measure their activity and their inhibition by small molecule, drug-like compounds. They will also learn how to crystallize these proteins and work on determining their 3D X-ray structures at atomic level resolution. Ultimately, these data and results will be used to help structure-based design of new drugs.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Cellular Biology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
At least 2 semesters or a summer working in a modern biochemistry focused research lab with hands on bench-work in an area related to enzyme function is required.
School/Dept.:
Department of Biochemistry
Professor:
Andrew
Mesecar
More information: https://www.science.purdue.edu/mesecar/
Structure-Based Drug Design for Phospholipase C Gamma 2
Description:
Patients with chronic lymphocytic leukemia (CLL) have a high, 5-year survival rate of 85% that is attributed to inhibition of Bruton’s tyrosine kinase (BTK) by the drug ibrutinib. Resistance to ibrutinib can occur through two mechanisms: 1. mutations in BTK which prevent binding of ibrutinib to the kinase active site or 2. mutations in Phospholipase C Gamma 2 (PLCG2), the downstream signal transduction molecule to BTK, that promote continued cancer cell proliferation via PLCG2 activation. This project will address ibrutinib resistance by inhibiting PLCG2 mutants driving CLL. Students will screen for inhibitory molecules using biochemical assays and will use structure biology to guide drug design.
Campus:
West Lafayette
Research categories:
Cellular Biology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
General chemistry courses required and a biochemistry or biology course(s) covering enzymes is preferred. At least 1 semester experience working in a biochemistry focused research lab is desired.
School/Dept.:
Department of Biochemistry
Professor:
Andrew
Mesecar
More information: https://www.science.purdue.edu/mesecar/
Study quantum transport properties of two-dimensional materials
Description:
Undergraduate students will produce two-dimensional materials, assemble two-dimensional heterostructures, design mesoscopic quantum devices and perform low-temperature characterization of two-dimensional devices.
Campus:
West Lafayette
Research categories:
Material Processing and Characterization, Medical Science and Technology, Microelectronics, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Physics and Astronomy
Professor:
Yihang
Zeng
More information: https://sites.google.com/view/zenglab2024/home
Summer Research on AI in Surgery
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
The SURF project and specific tasks for the student will be discussed in detail before the start of SURF. Contribution to either of the lab's ongoing research projects is possible. 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 seven 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
The SURF project and specific tasks for the student will be discussed in detail before the start of SURF. Contribution to either of the lab's ongoing research projects is possible. 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 seven 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, Cybersecurity, Deep Learning, Learning and Evaluation, 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/
Super-Resolution Optical Imaging through Scatter and Optomechanics with Nanostructured Membranes
Description:
Two projects are available.
The first project involves the investigation of enhancing optical imaging and sensing capabilities using motion in structured illumination. Conventional optical imaging has a theoretical limit on its spatial resolution, to about one half of the wavelength, and many situations can benefit from higher resolution. In addition, it is challenging to image through scattering media. The project would involve investigating methods of imaging and sensing through scatter involving structured light. Some work will involve performing experiments with lasers and optical imaging elements, with either cameras or single photon counting detectors, and then using the data to extract information about hidden objects. The project could also involve simulations performed using electromagnetic finite element analysis (FEM) methods to concurrently verify experimental results. This work has a diverse set of potential applications including biological imaging, sensing defects in semiconductors, and imaging through fog.
The second 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 propulsion, and has impacts in the fundamental sciences regarding the physics of optical forces on small length scales. The undergraduate project relating to this work would likely involve software modeling of electromagnetic and mechanical phenomena, and possibly computational analysis of experimental data.
The first project involves the investigation of enhancing optical imaging and sensing capabilities using motion in structured illumination. Conventional optical imaging has a theoretical limit on its spatial resolution, to about one half of the wavelength, and many situations can benefit from higher resolution. In addition, it is challenging to image through scattering media. The project would involve investigating methods of imaging and sensing through scatter involving structured light. Some work will involve performing experiments with lasers and optical imaging elements, with either cameras or single photon counting detectors, and then using the data to extract information about hidden objects. The project could also involve simulations performed using electromagnetic finite element analysis (FEM) methods to concurrently verify experimental results. This work has a diverse set of potential applications including biological imaging, sensing defects in semiconductors, and imaging through fog.
The second 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 propulsion, and has impacts in the fundamental sciences regarding the physics of optical forces on small length scales. The undergraduate project relating to this work would likely involve software modeling of electromagnetic and mechanical phenomena, and possibly computational analysis of experimental data.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Composite Materials and Alloys, Deep Learning, Nanotechnology
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- No Major Restriction
Desired experience:
Students with an interest in experimental or modeling work and some background in electromagnetics would be a good fit for this project. The undergraduate student will work with graduate students to perform experiments in an optics laboratory, model optical and mechanical interactions, analyze data using MATLAB or python, and review relevant literature.
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Kevin
Webb
Sustainable Aviation Propulsion Technologies
Description:
Research on batteries and sustainable aviation fuels (SAFs) is critical for advancing sustainable aviation. Battery technology must be developed to improve energy density, reduce weight, and extend operational range for electric aircraft. Innovations in materials and thermal management systems are necessary to enhance safety, reliability, and lifespan. Concurrently, SAFs must be optimized to minimize emissions and meet the performance requirements of different aircraft. This includes biofuels, synthetic fuels, and hydrogen-based options that can replace conventional jet fuel without compromising efficiency. We are recruiting undergraduate researchers to work on various aspects of sustainable propulsion technologies for future aircraft. Advances in these areas are vital to enabling a low-carbon future for aviation, reducing the industry’s environmental footprint, and ensuring long-term sustainability.
Campus:
West Lafayette
Research categories:
Energy and Environment, Thermal Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
A passion for hands-on research is essential, and a love for plumbing work is desired, as it is crucial for any propulsion system.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Li
Qiao
More information: qiaoresearchgroup.com
Synthesis and Characterization of Novel DNA Tetrahedra for Drug Delivery
Description:
One of the challenges posed by miRNA delivery is transfusing the oligos into the cells. Nanomaterials have been proven to decorate cells efficiently via DNA and protein aptamers, increasing the local drug concentration at the cellular membrane. A novel method of delivering miRNA via DNA-cholesterol anchoring improved the delivery efficiency in vitro in HeLa and HAT pathogenic cells.
Recently, we have designed a novel DNA tetrahedron that has high potential for miRNA drug delivery due to high loading capacity and rigidity. We are looking for a student to perform the assembly of the structure and test its stability in solution for future design use in drug delivery.
The student will be trained in gel electrophoresis and direct light scattering characterization. If the project goes very well, there is a potnetial to use atomic force microscopy and the the structures in vitro and use confocal microscopy.
Recently, we have designed a novel DNA tetrahedron that has high potential for miRNA drug delivery due to high loading capacity and rigidity. We are looking for a student to perform the assembly of the structure and test its stability in solution for future design use in drug delivery.
The student will be trained in gel electrophoresis and direct light scattering characterization. If the project goes very well, there is a potnetial to use atomic force microscopy and the the structures in vitro and use confocal microscopy.
Campus:
West Lafayette
Research categories:
Medical Science and Technology, Nanotechnology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Chremistry
- Biomedical Engineering
- Biological Engineering - multiple concentrations
- Biology
- Biochemistry
- Chemical Engineering
Desired experience:
Wet lab experience is a bonus, but no prior lab experience is required.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Tamara
Kinzer-Ursem
Testing point-of-care microfluidics platform for disease diagnostics
Description:
We will develop portable microfluidic devices for infectious diseases diagnostics based on the principles of electrokinetic assays previously developed in the lab. The student will utilize on-chip electrokinetic transport to selectively preconcentrate, control, and manipulate target reagents (e.g., DNA, RNA, viral particles, microbeads) within a microfluidic device. For this, the student will use off-the-shelf microfluidic chips and reagents, and use externally applied electric fields within the microfluidic chip to develop a rapid way to pre-concentrate and detect target biomolecules. Aspects of this project will involve testing, designing, and optimizing the microflduidic assays for infectious diseases applications.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Engineering the Built Environment, Fabrication and Robotics, Material Modeling and Simulation, Medical Science and Technology, Nanotechnology, System-on-a-Chip
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Mechanical Engineering
- Biomedical Engineering
- Biological Engineering - multiple concentrations
- Chemical Engineering
- Materials Engineering
Desired experience:
Intro biology and chemistry classes
School/Dept.:
School of Mechanical Engineering
Professor:
Ashwin
Ramachandran
More information: https://www.ashwinramachandranlab.org/research-areas
The Design and Synthesis of Peptide Nanotubes
Description:
The student will design peptide sequences for assembly into nanotube materials. The student will synthesize and purify the peptide. They will perform assembly reactions and analyze the materials formed using various microscopic techniques. Biopolymer cargo will be included in the nanotubes.
Campus:
West Lafayette
Research categories:
Material Processing and Characterization
Citizenship requirements:
U.S. Citizen
Preferred major(s):
- No Major Restriction
School/Dept.:
Department of Chemistry
Professor:
Jean
Chmielewski
The INSPIRE Research Institute for Pre-College Engineering
Description:
The position would entail working with INSPIRE faculty, staff, and graduate students in the School of Engineering Education to create engaging activities that integrate microelectronics and semiconductor technologies and contexts into engaging, broadly themed engineering activities for elementary, middle, and high school students. Additionally, the position would include delivering the activities in various summer day camp settings and helping the team collect and analyze research data associated with the camps.
Candidates for the position should have a passion for working with younger students, be open
to learning about various fabrication and microelectronic technologies, and be willing to take
on the responsibility of leading activities with students.
Candidates for the position should have a passion for working with younger students, be open
to learning about various fabrication and microelectronic technologies, and be willing to take
on the responsibility of leading activities with students.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics, Human Factors, Learning and Evaluation, Microelectronics, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Candidates are not required to have
extensive experience with microelectronics or fabrication technologies, but such skills are a
plus. All undergraduate students are welcome to apply, and those in education or
engineering/technology programs are strongly encouraged to apply.
School/Dept.:
School of Engineering Education
Professor:
Morgan
Hynes
More information: https://web.ics.purdue.edu/~hynesm/Welcome.html
The shape of an atom close to a metal surface
Description:
Density Functional Theory (DFT) is widely used to calculate the energies of atoms and molecules adsorbed to metal surfaces. Having accurate energies in those cases can often shed light on the catalytic mechanisms underlying many important chemical reactions. However, the accuracy of DFT calculations depends on the quality of the approximations employed for the exchange-correlation (XC) energy functional. In spite of significant progress over the last few decades, state-of-the-art approximations to the XC energy functional still suffer from errors that limit their applicability in many cases of interest for heterogeneous catalysis.
Our group has been investigating an idea to overcome related errors in molecules, see J.Phys.Chem.Lett. 15, 826 (2024). We suspect that the same idea should be applicable to atoms and molecules interacting with metal surfaces. Our hypothesis is that the difference between the XC energy of the metal and the XC energy of the adsorbate can be modeled through a simple functional involving the density overlap between metal and adsorbate. The goal of this project will be to test that hypothesis. The first step will be to determine how the ground-state electron density of the adsorbate (an individual atom) is polarized by the metal surface, and how the polarization depends on the nature of both the atom and the surface. This is a fundamental question that a SURF student could explore with the guidance of a graduate student in the group.
Our group has been investigating an idea to overcome related errors in molecules, see J.Phys.Chem.Lett. 15, 826 (2024). We suspect that the same idea should be applicable to atoms and molecules interacting with metal surfaces. Our hypothesis is that the difference between the XC energy of the metal and the XC energy of the adsorbate can be modeled through a simple functional involving the density overlap between metal and adsorbate. The goal of this project will be to test that hypothesis. The first step will be to determine how the ground-state electron density of the adsorbate (an individual atom) is polarized by the metal surface, and how the polarization depends on the nature of both the atom and the surface. This is a fundamental question that a SURF student could explore with the guidance of a graduate student in the group.
Campus:
West Lafayette
Research categories:
Chemical Catalysis and Synthesis, Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Adam
Wasserman
More information: https://sites.google.com/view/wassermangroup
Thermometers, Strain Gauges and Defect Detectors for Semiconductors using Light
Description:
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 infrared physics to create spectroscopic, thermal, and sensing solutions.
Research Topic, Semiconductor Sensing: Semiconductor chips are some of the most technologically advanced machines humanity has ever made. Like any complex machine, they break. Methods for predicting where they will break and why they have broken are therefore necessary. You will help us make the thermometers, strain gauges, and “defect detectors” that are up to the task using some of the world’s most advanced semiconductor characterization tools that we develop here at Purdue.
What’ You’ll Do: Team members will be responsible for performing spectroscopic measurements of next generation semiconductor materials, devices, and packages. Specifically, you will use Raman (sounds like but is not the noodle) and photoluminescence (fancy for glow in the dark) to image the temperature, stress, and presence of defects in everything from commercial logic chips made by Intel to materials being considered for next generation memory devices. 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.
Research Topic, Semiconductor Sensing: Semiconductor chips are some of the most technologically advanced machines humanity has ever made. Like any complex machine, they break. Methods for predicting where they will break and why they have broken are therefore necessary. You will help us make the thermometers, strain gauges, and “defect detectors” that are up to the task using some of the world’s most advanced semiconductor characterization tools that we develop here at Purdue.
What’ You’ll Do: Team members will be responsible for performing spectroscopic measurements of next generation semiconductor materials, devices, and packages. Specifically, you will use Raman (sounds like but is not the noodle) and photoluminescence (fancy for glow in the dark) to image the temperature, stress, and presence of defects in everything from commercial logic chips made by Intel to materials being considered for next generation memory devices. 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.
Campus:
West Lafayette
Research categories:
Advanced Packaging, Heterogeneous Integration, Material Processing and Characterization, Microelectronics, Nanotechnology, Thermal Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Thomas
Beechem
More information: www.specere.org
Topology Hiding Secure Computation
Description:
Traditionally, secure distributed computing (SDC) systems assume that the communication network is a complete graph; i.e., there are point-to-point (P2P) channels between every pair of nodes. As we consider SDC systems for distributed CPS systems, this assumption does not hold: Due to restrictions such as line of sight (LOS) communication or simply to reduce energy consumption, distributed CPS systems may form connected graphs, but they can be far away from being a clique.
Moreover, the communication network could itself contain sensitive information that the system may wish to hide. For instance, consider the following example: Satellites of different government states may wish to communicate important data while keeping the locations and number of their satellites mostly private. Topology hiding computation focuses on the SDC protocol that ensures that the adversary does not know any additional information about the topology of the communication graph beyond what may be computed from the SDC protocol output.
The project will focuses on designing and implementing topology hiding secure computation protocols for use in practical CPS.
Moreover, the communication network could itself contain sensitive information that the system may wish to hide. For instance, consider the following example: Satellites of different government states may wish to communicate important data while keeping the locations and number of their satellites mostly private. Topology hiding computation focuses on the SDC protocol that ensures that the adversary does not know any additional information about the topology of the communication graph beyond what may be computed from the SDC protocol output.
The project will focuses on designing and implementing topology hiding secure computation protocols for use in practical CPS.
Campus:
West Lafayette
Research categories:
Cybersecurity
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Computer Science
- Computer Engineering
School/Dept.:
Computer Science
Professor:
Aniket
Kate
Training a robot using AI to complete surgery
Description:
This project consists of developing algorithms to endow a Da-Vinci robot capabilities to conduct autonomous surgery or part of a surgery using machine learning and AI methods. The key idea will rely on reinforcement learning and imitation learning.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Fabrication and Robotics, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Very good experience programming using Python, Pytorch and machine learning algorithms. Experience in robotics and simulation. Experience with DvRK is a plus.
School/Dept.:
School of Industrial Engineering
Professor:
Juan
Wachs
More information: http://web.ics.purdue.edu/~jpwachs/
Trustworthy Machine Learning
Description:
The recent strides in Machine Learning (ML) innovation has led to large and complex models that permeate nearly every aspect of society. Much like the size and complexity of these ML models, the ML supply chains that produce them are immense and interconnected networks of activities carried out by various actors, involving diverse software stacks, within
different jurisdictions, and more. A malicious party who can control any step of the process will have the ability to impact all the following steps and the downstream product with the potential to affect many users. Even worse, machine learning systems are shown to be susceptible to specialized attacks where, for example, a malicious actor can easily extract private information or covertly derail operations. The aim of this project is to create holistic approaches to quantify trust in machine learning systems, incorporating all pillars of trustworthy machine learning. This project is closely advised by Prof. Ghodsi, and will allow students to gain hands-on experience in developing machine learning systems and experimenting with attacks and mitigations for them.
different jurisdictions, and more. A malicious party who can control any step of the process will have the ability to impact all the following steps and the downstream product with the potential to affect many users. Even worse, machine learning systems are shown to be susceptible to specialized attacks where, for example, a malicious actor can easily extract private information or covertly derail operations. The aim of this project is to create holistic approaches to quantify trust in machine learning systems, incorporating all pillars of trustworthy machine learning. This project is closely advised by Prof. Ghodsi, and will allow students to gain hands-on experience in developing machine learning systems and experimenting with attacks and mitigations for them.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Cybersecurity, Deep Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Zahra
Ghodsi
Tumor suppressive microgels
Description:
Microgels are spherical micron-sized hydrogels increasingly used in a diverse range of applications. For example, microgels can serve as carriers for culturing both adherent cells and suspension cells. Microgels have a higher surface-to-volume ratio than their bulky gel counterparts, improving oxygen/nutrient transport to support cell viability and function. Furthermore, the dimension of microgels enables their minimally invasive and injectable delivery. We will engineer gelatin-based tumor-suppressive microgels (TSM) for delivering anti-cancer therapeutics and culturing tumor-suppressive cells.
Campus:
Indianapolis
Research categories:
Cellular Biology, Material Processing and Characterization
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Biology lab, soft materials processing and characterization
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Chien-Chi
Lin
Tunable SIW Cavity Coupling Matrix Synthesis and Optimization
Description:
Abstract: Resonant cavities are widely used in tunable filters, diplexers, couplers, and other RF/microwave components [1]. Some of these devices are frequency reconfigurable as they possess a mechanical or electrical tuning mechanism to change the resonant frequency of each cavity. Aligning all the cavities to the same resonant frequency is challenging as their response depends on the other coupled resonators. Hence, an algorithm to determine the optimum point from the given measurement is needed to reduce the time to tune the device. The undergraduate researcher will perform RF simulations and/or measurements of a cavity-based device with a Vector Network Analyzer (VNA). The multi-port coupling matrix of the device will be synthesized from the S-parameter data [2] and then compared to the ideal coupling matrix. An optimization algorithm will be developed to predict the changes needed in the tuning for each cavity. Finally, the undergraduate researcher will integrate the algorithm into the MATLAB open-source ARES tool [3]. Previous RF knowledge is encouraged but not required as the graduate mentor will teach the undergraduate researcher the needed background.
[1] M. F. Hagag, R. Zhang and D. Peroulis, "High-Performance Tunable Narrowband SIW Cavity-Based Quadrature Hybrid Coupler," in IEEE Microwave and Wireless Components Letters, vol. 29, no. 1, pp. 41-43, Jan. 2019, doi: 10.1109/LMWC.2018.2884238.
[2] P. Zhao, Z. -A. Xiong, J. Fan, Y. Yang and J. Zhou, "Analytical Multiport Coupling Matrix Synthesis From Partial Fraction Expansions of S-Parameters," in IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 11, pp. 6554-6562, Nov. 2024, doi: 10.1109/TMTT.2024.3393910.
[3] J.A. Bolaños-Vargas and Alex D. Santiago-Vargas, “Automated Radio Evaluation Suite” https://github.com/bolanosv/AutomatedRadioEvaluationSuite
PI: Dimitrios Peroulis
Mentor: Alex D. Santiago-Vargas
[1] M. F. Hagag, R. Zhang and D. Peroulis, "High-Performance Tunable Narrowband SIW Cavity-Based Quadrature Hybrid Coupler," in IEEE Microwave and Wireless Components Letters, vol. 29, no. 1, pp. 41-43, Jan. 2019, doi: 10.1109/LMWC.2018.2884238.
[2] P. Zhao, Z. -A. Xiong, J. Fan, Y. Yang and J. Zhou, "Analytical Multiport Coupling Matrix Synthesis From Partial Fraction Expansions of S-Parameters," in IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 11, pp. 6554-6562, Nov. 2024, doi: 10.1109/TMTT.2024.3393910.
[3] J.A. Bolaños-Vargas and Alex D. Santiago-Vargas, “Automated Radio Evaluation Suite” https://github.com/bolanosv/AutomatedRadioEvaluationSuite
PI: Dimitrios Peroulis
Mentor: Alex D. Santiago-Vargas
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Microelectronics, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Electrical Engineering
- Computer Engineering
- Computer Science
- Mathematics
- Physics
Desired experience:
Preferred Skills:
* Programming (MATLAB, Python, etc.)
* Research or project experience
* Knowledge of optimization algorithms or ML
* Lab equipment
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Dimitrios
Peroulis
More information: https://scholar.google.com/citations?user=agc3kMMAAAAJ&hl=en&oi=ao
Two Projects: 1) Understanding the Documentation, Diagnosis, and Treatment of Obesity 2) Examining Uncertainty and Doubt in Medicine
Description:
Project #1: Understanding the Documentation, Diagnosis, and Treatment of Obesity, using Electronic Health Record Data
Despite the high prevalence of obesity, it remains rarely diagnosed in clinical settings. Rates of diagnosis also vary widely by patients’ gender and race/ethnicity, as well as other factors. The goal of this research project is to better understand patterns and social disparities in the documentation, diagnosis, and treatment of obesity, particularly since the recent introduction of anti-obesity medications.
The primary role for the student researcher will be wrangling and statistically analyzing complex, large-scale electronic health record data using Python (or R).
Project #2: Examining Uncertainty and Doubt in Medicine, using Doctor’s Notes
Medicine is full of uncertainty. For example, providers often face ambiguity in diagnostic and treatment decisions. Providers may also doubt the validity of information reported by patients. This research project explores clinical uncertainty using data from electronic health records (primarily, doctor’s notes).
The primary roles for the student researcher will be 1) developing natural language processing techniques to measure uncertainty in clinical notes, and 2) using these measures to exploring factors associated with clinical uncertainty.
Please specify whether you are interested in project #1, #2, or either.
Despite the high prevalence of obesity, it remains rarely diagnosed in clinical settings. Rates of diagnosis also vary widely by patients’ gender and race/ethnicity, as well as other factors. The goal of this research project is to better understand patterns and social disparities in the documentation, diagnosis, and treatment of obesity, particularly since the recent introduction of anti-obesity medications.
The primary role for the student researcher will be wrangling and statistically analyzing complex, large-scale electronic health record data using Python (or R).
Project #2: Examining Uncertainty and Doubt in Medicine, using Doctor’s Notes
Medicine is full of uncertainty. For example, providers often face ambiguity in diagnostic and treatment decisions. Providers may also doubt the validity of information reported by patients. This research project explores clinical uncertainty using data from electronic health records (primarily, doctor’s notes).
The primary roles for the student researcher will be 1) developing natural language processing techniques to measure uncertainty in clinical notes, and 2) using these measures to exploring factors associated with clinical uncertainty.
Please specify whether you are interested in project #1, #2, or either.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Human Factors, Medical Science and Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
For project #1: Candidates should be familiar with manipulating and analyzing quantitative data (ideally, using Python or R), and be eager to further develop these skills through this research experience. Candidates should ideally also have substantive interest in health, medicine, and/or social inequality.
For project #2: Candidates should ideally be familiar with natural language processing (NLP) and/or Python. Candidates should be eager to further develop their NLP, data analysis, and Python skills through this research experience. Candidates should ideally also have substantive interest in health, medicine, uncertainty detection, and/or social inequality.
School/Dept.:
Department of Sociology
Professor:
Alina
Arseniev-Koehler
Two Projects: 1) Understanding the Documentation, Diagnosis, and Treatment of Obesity and 2) Examining Uncertainty and Doubt in Medicine
Description:
Project #1: Understanding the Documentation, Diagnosis, and Treatment of Obesity, using Electronic Health Record Data
Despite the high prevalence of obesity, it remains rarely diagnosed in clinical settings. Rates of diagnosis also vary widely by patients’ gender and race/ethnicity, as well as other factors. The goal of this research project is to better understand patterns and social disparities in the documentation, diagnosis, and treatment of obesity in clinical settings, particularly since the recent introduction of anti-obesity medications.
The primary role for the student researcher will be wrangling and statistically analyzing complex, large-scale electronic health record data using Python (or R).
Project #2: Examining Uncertainty and Doubt in Medicine, using Doctor’s Notes
Medicine is full of uncertainty. For example, providers often face ambiguity in diagnostic and treatment decisions. Providers may also doubt the validity of information reported by patients. This research project explores clinical uncertainty using data from electronic health records (primarily, doctor’s notes).
The primary roles for the student researcher will be 1) developing techniques to measure uncertainty in clinical notes using natural language processing, and 2) using these measures to exploring factors associated with clinical uncertainty.
Please specify whether you are interested in project #1, #2, or both.
Despite the high prevalence of obesity, it remains rarely diagnosed in clinical settings. Rates of diagnosis also vary widely by patients’ gender and race/ethnicity, as well as other factors. The goal of this research project is to better understand patterns and social disparities in the documentation, diagnosis, and treatment of obesity in clinical settings, particularly since the recent introduction of anti-obesity medications.
The primary role for the student researcher will be wrangling and statistically analyzing complex, large-scale electronic health record data using Python (or R).
Project #2: Examining Uncertainty and Doubt in Medicine, using Doctor’s Notes
Medicine is full of uncertainty. For example, providers often face ambiguity in diagnostic and treatment decisions. Providers may also doubt the validity of information reported by patients. This research project explores clinical uncertainty using data from electronic health records (primarily, doctor’s notes).
The primary roles for the student researcher will be 1) developing techniques to measure uncertainty in clinical notes using natural language processing, and 2) using these measures to exploring factors associated with clinical uncertainty.
Please specify whether you are interested in project #1, #2, or both.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Human Factors, Medical Science and Technology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
For project #1: Candidates should have some some familiarity with manipulating and analyzing large-scale data (ideally, using Python or R), and be eager to further develop these skills through this research experience. Candidates should ideally also have substantive interest in health, medicine, and/or social inequality.
For project #2: Candidates should ideally have some familiarity with Python and/or natural language processing (NLP). Candidates should be eager to further develop their NLP and Python skills through this research experience. Candidates should ideally also have substantive interest in health, medicine, uncertainty detection, and/or social inequality.
School/Dept.:
Department of Sociology
Professor:
Alina
Arseniev-Koehler
Ultrafast Squeezed Light Generation and Measurement
Description:
In this project, squeezed quantum light will be generated and measured using femtosecond laser pulses in a nonlinear optical scheme. The student will work with a graduate student to build the optical setup, perform measurements and data analysis to demonstrate the generation and control of squeezed light.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Prior Quantum Optics experience desired.
School/Dept.:
Physics and Astronomy
Professor:
Niranjan
Hirisave Shivaram
More information: https://ultrafast.physics.purdue.edu/index.html
Ultrasound-based assessment of cardiac remodeling in chronic hypertension in pregnancy
Description:
Blood pressure can temporarily decrease during portions of pregnancy in both normo- and hypertensive patients (1). Although blood pressure influences cardiac contractility and fibrosis, few have assessed the potential impacts this may have on cardiac remodeling during pregnancy. The aim of this work is to assess the interplay between pregnancy (volume overload) and chronic hypertension (pressure overload) in left ventricle remodeling and biomechanics. This project includes a mix of laboratory work and data analysis, including but not limited to small animal ultrasound imaging, blood pressure acquisition, 4D ultrasound and 2D, histology analysis, and data processing. Students should have experience with data wrangling in MATLAB or python, and are ideally comfortable with animal handling.
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Students must have experience with MATLAB or Python. Students will ideally be comfortable working with animals and/or have laboratory experience. It will be helpful if students are already familiar with cardiac mechanics, and/or ultrasound imaging.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Craig
Goergen
More information: https://engineering.purdue.edu/cvirl
Understanding Cueing Gestures within Video Learning Environments for Statistics Education
Description:
Purdue University and the University of Illinois propose a 3-year project for the EHR Core Research program, Level 2 tier of the Research on STEM Learning and Learning Environments track. Our long-term goal is to inform the design and development of online and self-regulated educational environments that are responsive to the interactive and embodied nature of human learning. The main objective of this project is to evaluate an approach to designing a digital video learning environment (DVLE) for teaching statistics concepts that use gestures during instruction and cue learners to perform gestures. We will develop DVLEs in statistics covering three foundational topics: variance, central tendency, and least-squares regression. For each topic, we will develop three versions of the DVLEs to examine the features that are associated with learning. Undergraduate students will use gesture capture and animation software to help develop and create the DVLE videos, conduct literature reviews, collect, organize, and analyze quantitative and qualitative data.
Campus:
West Lafayette
Research categories:
Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Engineering Education
Professor:
Jason
Morphew
More information: https://engineering.purdue.edu/i2Engineering
Understanding Portability of Genomic Insights Across Populations
Description:
Genomics insights over the last 10 years have suffered from a robust understanding of translation across populations, ancestral, and demographic groups. In this project, we will use methods and tools developed in computational biology and statistical genomics to understand how genomic insights for health outcomes in the mental and physical health categories translate across these different populations. We will conduct gene discovery analyses using biobank-scale data (e.g., All of Us or the UK Biobank) and smaller datasets with more robust phenotyping (e.g., The National Longitudinal Study of Adolescent to Adult Health) that our lab already has access to on Purdue's high performance computing cluster. Our approaches will use methods and tools like genome-wide association studies and polygenic scores together with machine learning methods. The project will result in a paper that can be submitted for publication and presented at a national genomics research conference.
Campus:
Indianapolis
Research categories:
Big Data/Machine Learning, Biotechnology Data Insights, Genetics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Biology
Desired experience:
Basic biology
School/Dept.:
Department of Sociology
Professor:
Robbee
Wedow
More information: https://www.robbeewedow.com
Understanding Tactile Sensation: Neural Mechanisms of Texture Exploration in Freely Moving Mice
Description:
We rely on our sense of touch to identify textures and make decisions relevant to our everyday functioning, from choosing comfortable clothing to determining if a surface is slippery. However, the neural basis for encoding texture is not well understood. The main purpose of this project is to determine how texture is encoded by neurons in the mouse primary somatosensory cortex by recording neural activity in freely-moving mice during texture exploration. First, mice will undergo behavioral training in which they must discriminate between pairs of textures and indicate which feels more rough by navigating to a specific reward port within the arena. Once mice demonstrate successful behavioral performance, they will be implanted with a 16-channel microelectrode array and allowed to roam freely in an arena containing 17 different textures (16 sandpapers of varying grit and smooth plastic acrylic). Neural patterns of texture discrimination thresholds will be identified while we record neural activity with electrode arrays while mice perform the behavioral task. The breadth of data gathered from the multi-channel electrode array will provide both high spatial resolution and high temporal resolution neural recordings. This data will be used to assemble a more complete picture of multi-feature texture encoding in the brain. Knowledge of this representation of texture information will contribute to the development of next-generation prosthetics which aim to replicate naturalistic sensation.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Experience with handling animals, knowledge of python or MATLAB languages, ability to work with arduinos
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Maria
Dadarlat Makin
Understanding Type IL Cement Concrete Performance and Its Compatibility with Different Chemicals
Description:
This project aims to evaluate the performance of Type IL (limestone-blended) cement concrete and its compatibility with various chemical admixtures and environmental exposures. With the increasing adoption of Type IL cement to reduce carbon emissions in concrete production, it is crucial to understand how it interacts with different chemical agents, including admixtures, deicing salts, and aggressive solutions. The study will involve laboratory testing to assess strength development, durability, and chemical interactions, providing insights into its long-term performance in infrastructure applications. The findings will help guide the development of best practices for using Type IL cement in diverse construction environments.
Type of Work:
• Prepare and cast concrete specimens with different chemical additives.
• Perform mechanical and durability testing, including strength, permeability, and chemical resistance assessments.
• Analyze microstructural and chemical interactions using spectroscopy and microscopy techniques.
• Process experimental data and develop recommendations for practical applications.
Type of Work:
• Prepare and cast concrete specimens with different chemical additives.
• Perform mechanical and durability testing, including strength, permeability, and chemical resistance assessments.
• Analyze microstructural and chemical interactions using spectroscopy and microscopy techniques.
• Process experimental data and develop recommendations for practical applications.
Campus:
West Lafayette
Research categories:
Nanotechnology, Other
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Civil Engineering
- Materials Engineering
Desired experience:
• Basic knowledge of cement chemistry and concrete materials.
• Experience in materials testing, chemical analysis, or data processing is preferred
School/Dept.:
Lyles School of Civil Engineering
Professor:
Yining
Feng
Understanding bacterial biomechanics at air-porous solid media interface
Description:
This project aims to understanding how water/nutrient transport are coupled with bacterial cell shape and division pattern in determining microcolony morphogenesis and biomechanics at an air-porous media interface. The student will build upon preliminary experiments which suggest that spherical bacteria (e.g., S. aureus) divide and grow in 3D and away from the surface at much smaller colony sizes (~4 cells) than rod-shaped bacteria such as E. coli and P. aeruginosa (>30 cells per colony). For this project, the student will systematically vary the hydration and stiffness of the porous media (e.g., agarose percentage) that the cells grow in, and using microscopy, study the evolution of colony morphology in S. aureus and S. pneumoniae as cases of spherical bacteria that grow in clusters and chains, respectively. The student will compare these with rod-shaped bacterium such as E. coli and P. aeruginosa, using both experiments and simulations. Lastly, the student will study how mechanics and water/nutrient transport affects colony evolution of mixed bacterial species, as a more realistic model for biofilm-related infections.
Campus:
West Lafayette
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Biotechnology Data Insights, Cellular Biology, Engineering the Built Environment, Fluid Modelling and Simulation, Genetics, Material Modeling and Simulation, Medical Science and Technology, System-on-a-Chip
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Mechanical Engineering
- Biomedical Engineering
- Microbiology
- Biological Engineering - multiple concentrations
- Biology
- Cell Molecular and Developmental Biology
- Chemical Engineering
- Materials Engineering
Desired experience:
Knowledge of basic biology (intro level) and interest in quantitative biological measurements.
School/Dept.:
School of Mechanical Engineering
Professor:
Ashwin
Ramachandran
More information: https://www.ashwinramachandranlab.org/research-areas
Understanding the role of stability in fine tuning LLMs
Description:
The student is expected to assist a current PhD student in theoretical and empirical understanding of designing variants of fine tuning algorithms for Large Language Models.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
Courses on Machine Learning, background courses on linear algebra and probability, comfortable coding/editing on large codebases, exposure to LLMs are a plus but not required
School/Dept.:
Computer Science
Professor:
Rajiv
Khanna
Underwater Noise Abatement for Offshore Windfarm Installation
Description:
Offshore wind farms are essential for building a future with clean energy. However, the installation of wind farms requires hammering a huge pile into the seabed, generating strong noises that harm ocean animals. The goal of this project is to design, build, and experimentally validate acoustic metamaterials to mitigate noise for pile installation.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Desired experience:
Linear Algebra, ODE and PDE, acoustics
School/Dept.:
School of Mechanical Engineering
Professor:
Junfei
Li
Underwater Noise Control for Offshore Windfarms
Description:
Offshore windfarms are essential for a future with clean energy. However, installation of offshore windfarms requires hammering a pile into the seabed, generating strong noise that can propagate more than 20 miles and harm sea animals. This project aims to develop, design, fabricate, and experimentally verify acoustic metamaterials to reduce noise from piling. The student role includes the design, simulation of the acoustic metamaterials structures, and building experimental devices for the lab-scale tests.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Mechanical Engineering
School/Dept.:
School of Mechanical Engineering
Professor:
Junfei
Li
Using AI to Predict Health Outcomes in Single Ventricle Patients
Description:
A single ventricle (SV) heart is a serious condition that some babies are born with. In an SV heart, one of the two main pumping chambers of the heart is too small and does not work well. Without treatment, many babies with this condition would not survive. Thankfully, surgeries like the "Fontan Procedure" have been developed, allowing many children with SV to grow up and live into adulthood. However, even after surgery, the hearts of adult SV patients are still at risk of problems.
SV patients are regularly monitored, and their health information is recorded in electronic health records (EHRs). These records include information like body weight, lab test results, heart imaging, and medications. The Pediatric Heart Network (PHN) has collected over 1,000 records from studies on SV patients.
In this project, we will use AI and machine learning (ML) techniques to study changes in the health of SV patients over time. The goal is to create models that predict when a patient’s health might decline. As part of this project, you will help build and test these AI/ML models using real patient data. You will also learn skills in Python programming, computer science, and data analysis using tools like Pytorch.
SV patients are regularly monitored, and their health information is recorded in electronic health records (EHRs). These records include information like body weight, lab test results, heart imaging, and medications. The Pediatric Heart Network (PHN) has collected over 1,000 records from studies on SV patients.
In this project, we will use AI and machine learning (ML) techniques to study changes in the health of SV patients over time. The goal is to create models that predict when a patient’s health might decline. As part of this project, you will help build and test these AI/ML models using real patient data. You will also learn skills in Python programming, computer science, and data analysis using tools like Pytorch.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Cardiovascular Disease Research
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
As a research fellow, you will help set up an AI/ML system to track the health of SV patients. You will meet weekly with your mentor to discuss progress, solve problems, and plan the next steps in the research.
School/Dept.:
Regenstrief center for Healthcare Engineering
Professor:
Brett
Meyers
Using Resistor Networks to Predict Critical Exponents in Mott Metal-Insulator Transition Materials
Description:
Mott materials such as vanadium dioxide exhibit a drop in resistivity of up to a factor of 100,000, just above room temperature. This is known as the Mott insulator-to-metal transition. However, rather than transitioning all at once, the material displays intricate pattern formation, and the conducting regions form a fractal structure. The student will calculate the macroscopic resistance associated with such fractal structures, by developing a resistor network model. This will then be used to predict the power law behavior of resistance avalanches during the transition, and compared directly with experiments.
Campus:
West Lafayette
Research categories:
Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- Physics
Desired experience:
Must have experience coding, and using high performance computing clusters.
School/Dept.:
Physics and Astronomy
Professor:
Erica
Carlson
More information: https://www.physics.purdue.edu/~erica
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 to develop and acquire data to support ongoing project development.
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
Video analytics for dairy feed management
Description:
Video analytics and computer vision have 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.
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):
- Computer Science
- Electrical Engineering
- Computer Engineering
- Computer Engineering Technology
Desired experience:
Python programming is required
School/Dept.:
Elmore Family School of Electrical and Computer Engineering
Professor:
Amy
Reibman
Visualizing mechanotransduction of subcellular metabolic signaling in progerin-expressing endothelial cells
Description:
The human aging process increases the risk of cancer and cardiovascular diseases that together account for the most common causes of death in United States. Nuclear lamina, a network of intermediate filaments located right underneath the nuclear envelope, is implicated in the aging process. It plays a critical role in the structural integrity of the nucleus as well as in connecting with cell cytoskeleton. A mutant truncated form of lamin called progerin is responsible for premature aging disease Hutchinson-Gilford progeria syndrome (HGPS). AMP-activated protein kinase (AMPK) is a crucial regulator of cellular energy homeostasis, significantly influencing aging by modulating metabolic pathways. In this project, we will use fluorescence resonance energy transfer (FRET) imaging and visualize the activity of AMPK signaling in the nucleus, mitochondria, and cytoplasm in progerin-expressing endothelial cells under flow-induced shear stress.
In this project, a student will be involved in a range of techniques essential for studying cell mechanics and live cell imaging. This includes cell culture, transfection (introducing plasmids into cells), advanced live cell imaging using the FRET technique, and image analysis and quantification of cell signaling activity.
In this project, a student will be involved in a range of techniques essential for studying cell mechanics and live cell imaging. This includes cell culture, transfection (introducing plasmids into cells), advanced live cell imaging using the FRET technique, and image analysis and quantification of cell signaling activity.
Campus:
Indianapolis
Research categories:
Biological Characterization and Imaging, Cellular Biology, Medical Science and Technology
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
- Biomedical Engineering
- Biology
- Cell Molecular and Developmental Biology
- Mechanical Engineering
- Biological Engineering - multiple concentrations
Desired experience:
At least 3.0 GPA. Knowledge of cell culture is recommended, but not required.
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Sungsoo
Na
Volumetric Imaging of Mineral Precipitation on Rough Fracture 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, (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 how they correlate with surface mineralogy; (3) participate in the design and perform the 3D printing of a fluid chamber for exposing fracture surfaces to different fluid chemistries; and (4) developing fluid handling protocols to maintain a steady saturation index.
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 is desired
School/Dept.:
Physics and Astronomy
Professor:
Laura
Pyrak-Nolte
X-ray Imaging of Impact Dynamics for Energetic Materials (AAMP-UP-PERC)
Description:
The dynamic response of materials to high strain rates is important for understanding their safety requirements. Typically, drop weight impact characterization relies on the identification of a reaction, or lack thereof, and does not further interrogate the sample. The goal of this project is to adapt dynamic X-ray imaging to enable advanced investigation of sub-surface phenomena in high strain rate events. The student will review the current state of the art in this topic and assist with preparations for experiments using dynamic X-ray imaging. The student will work directly with the mentor and graduate students and participate in regular project meetings and progress updates. The work will culminate in a technical presentation and/or written report.
Campus:
West Lafayette
Research categories:
Thermal Technology, Other
Citizenship requirements:
U.S. Citizen, U.S. Permanent Resident
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
Desired experience:
Experience with thermodynamics, fluid mechanics, basic physics, basic chemistry, and engineering design is desired. Training on specific skills will be provided.
School/Dept.:
School of Mechanical Engineering
Professor:
Terrence
Meyer
More information: engineering.purdue.edu/trmeyer
experimental investigation of ice crystal growth under high pressure
Description:
This project aims at investigating the kinetics of ice crystal growth under high pressure. The role of the student includes literature review, design and fabricate diamond-anvil-cell (DAC) setup to achieve pressure up to 1 GPa, and develop temperature control system to study ice crystal growth.
Campus:
West Lafayette
Research categories:
Engineering the Built Environment, Fabrication and Robotics, Material Processing and Characterization
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/
fluid-structure simulation of microfluidic device
Description:
This project aims at simulating and optimizing the fluid-structure interaction in a microfluidic device. The role of the student include measuring mechanical properties of elastic membrane and integrate into a COMSOL model, machine learning will also be used to explore larger parameter space for optimization of microfluidic device design.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Deep Learning, Fluid Modelling and Simulation, Material Modeling and Simulation
Citizenship requirements:
No citizenship requirements
Preferred major(s):
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School/Dept.:
School of Mechanical Engineering
Professor:
Li
Zhan
More information: https://www.zhanlab.org/home
high throughout cryopreservation of cellular products
Description:
This project aims at developing high throughput cryogenic preservation of living cellular products which can be used to cure cancer. Student role in this project includes design and fabrication of flash resistive heating device, 3D printing microfluidic device, perform cell culture, cell viability assessment.
Campus:
West Lafayette
Research categories:
Biological Simulation and Technology, Cellular Biology, Fabrication and Robotics, Medical Science and Technology, Thermal Technology
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
machine learning assisted microfluidic sorting of biologics
Description:
This project aims at developing machine learning assisted real time microfluidic sorting of living biologics such as embryos. The role of the student includes 3D print and fabrication of microfluidic device, design of label-free electrical sorting methods, and developing machine learning algorithm for classifying images of embryos for sorting purpose.
Campus:
West Lafayette
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Deep Learning, Fabrication and Robotics, Fluid Modelling and Simulation, Medical Science and Technology
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
underwater robot development
Description:
Develop underwater robots that can switch between different configurations. The robot will operate in three modes: drifter, glider, and propeller. Transitioning between these modes is achieved by actuation mechanism. The project aims to design a mechanism that allows seamless switching between those operating modes.
Campus:
West Lafayette
Research categories:
Fabrication and Robotics
Citizenship requirements:
No citizenship requirements
Preferred major(s):
- No Major Restriction
Desired experience:
one or more training/experience from the following areas: design, CAD, robotics, control
School/Dept.:
School of Industrial Engineering
Professor:
Yu
She
More information: www.purduemars.com