While the effect of lactation on the health of neonates is commonly studied, its consequence on maternal health is still ambiguous. Previous work has suggested that complications of pregnancy are often associated with increased risk of cardiovascular disease, further complicating the long-lasting effects of pregnancy. Conversely, other research suggests that longer lactation periods may decrease the risk of cardiovascular diseases. Thus, we aim here to understand how pregnancy and lactation affect cardiovascular remodeling and if these changes could be attributed to altered risk of cardiovascular diseases. This project aims to better understand the cardiac remodeling process throughout normal pregnancy and lactation during the post-partum period. Four-dimensional ultrasound scans of the heart are acquired at several timepoints throughout gestation and post-partum. These scans will be used to quantify left ventricular geometry and function in normal pregnancies and during lactation. Image analysis of the ultrasound images will be performed using a custom MATLAB GUI. We will compare the postpartum cardiovascular remodeling that occurs in lactating and non-lactating mice. Analysis of the cardiac scans will provide information relating to the left ventricle volume, ejection fraction, and LV wall thickness. These metrics will provide conclusions and recommendations for further research in this area.

Big Data/Machine Learning, Biological Characterization and Imaging

• Biomedical Engineering
• Computer Engineering

BME

Craig Goergen

Machine learning (ML) tools are playing an increasingly important role in science and engineering, revealing patterns and providing predictive capabilities not achievable otherwise. This research area explores the utility of machine learning algorithms in the design, development, and characterization of various energetic material systems. Particular emphasis is placed on bringing a data science formalism to the field, with an eye toward both future capability development and more intelligent (and appreciably faster) material formulation and system design. The REU student would work closely with a Research Scientist and graduate student to gather data, analyze it using ML tools, and share these results.

This project is from the AAMP-UP summer program, which is a different program than SURF. AAMP-UP is a 10-week summer program that 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.

Big Data/Machine Learning, Other

• No Major Restriction

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 2023. 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.

Mechanical Engineering

Steven Son

Undergraduate researchers enthusiastic about applying artificial intelligence and machine learning (AI/ML) algorithms for a wide variety of data science and engineering-based tasks within industry including data masking, synthetic data generation, cybersecurity, additive manufacturing, software security, and intrusion detection.

Big Data/Machine Learning, Deep Learning, Internet of Things (IoT)

• No Major Restriction

- Basic programming experience in Python/MATLAB - enthusiasm for practical applications of linear algebra, statistics, and computer science-based application is preferred. - strong background in physics and mathematics

Nuclear Engineering

Hany Abdel-Khalik

The student will work with a group of researchers at Purdue, Harvard and Tuskegee University on an NSF Future Manufacturing project focused on internet of things (IoT) edge devices and artificial intelligence (AI) for manufacturing applications. The IoT devices will be deployed at local manufacturing companies and their data will be used to improve operations.

Big Data/Machine Learning, Deep Learning, Internet of Things (IoT)

• Electrical Engineering
• Mechanical Engineering
• Industrial Engineering
• Computer Science
• Computer Engineering

Electrical and Computer Engineering

Ali Shakouri

This project will use deep learning models to analyze sequences of data (such as music). The analysis results will trigger a generative model to create visual art (image or video). Different styles of music (such as class, jazz, and rock) will be used as the input. The music will have different tempos. The computer models analyzes the style and tempo of the music and sets the parameters to generate the visual art. Faster music produces fast moving video. The SURF student will evaluate the existing (open source) computer models for music analysis and visual art generation, integrate them, and provide proof-of-concept demonstrations.

Big Data/Machine Learning, Deep Learning, Human Factors

• Computer Engineering
• Computer and Information Technology
• Computer Science
• Music
• Data Science

Required: At least one course on computer programming. Desired: Knowledge about machine learning and music.

Electrical and Computer Engineering

Yung-Hsiang Lu

*** Desired experience: Strong coding skills and motivation in research are required. Background in deep learning, security, and natural language processing is not required but a plus.

*** Possible industry involvement: Some of these projects are funded by Meta/Facebook research awards and J.P.Morgan AI research awards. 

*** We especially encourage applications from women, Aboriginal peoples, and other groups underrepresented in computing.

*** Project 1. Data-Free Model Extraction

Many deployed machine learning models such as ChatGPT and Codex are accessible via a pay-per-query system. It is profitable for an adversary to steal these models for either theft or reconnaissance. Recent model-extraction attacks on Machine Learning as a Service (MLaaS) systems have moved towards data-free approaches, showing the feasibility of stealing models trained with difficult-to-access data. However, these attacks are ineffective or limited due to the low accuracy of extracted models and the high number of queries to the models under attack. The high query cost makes such techniques infeasible for online MLaaS systems that charge per query.

In this project, we will design novel approaches to get higher accuracy and
query efficiency than prior data-free model extraction techniques.

Early work and background can be found here: 
https://www.cs.purdue.edu/homes/lintan/publications/disguide-aaai23.pdf

*** Project 2. Language Models for Detecting and Fixing Software Bugs and Vulnerabilities

In this project, we will develop machine learning approaches including code language models to automatically learn bug and vulnerability patterns and fix patterns from historical data to detect and fix software bugs and security vulnerabilities. We will also study and compare general code language models and domain-specific language models.

Early work and background can be found here: 
Impact of Code Language Models on Automated Program Repair. ICSE 2023. Forthcoming.
KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair. ICSE 2023. Forthcoming.
https://www.cs.purdue.edu/homes/lintan/publications/cure-icse21.pdf
https://www.cs.purdue.edu/homes/lintan/publications/deeplearn-tse18.pdf

*** Project 3. Inferring Specifications from Software Text for Finding Bugs and Vulnerabilities

A fundamental challenge of detecting or preventing software bugs and vulnerabilities is to know programmers’ intentions, formally called specifications. If we know the specification of a program (e.g., where a lock is needed, what input a deep learning model expects, etc.), a bug detection tool can check if the code matches the specification. 

Building upon our expertise on being the first to extract specifications from code comments to automatically detect software bugs and bad comments, in this project, we will analyze various new sources of software textual information (such as API documents and StackOverflow Posts) to extract specifications for bug detection. For example, the API documents of deep learning libraries such as TensorFlow and PyTorch contain a lot of input constraint information about tensors. Language models may be explored.

Early work and background can be found here: 
https://www.cs.purdue.edu/homes/lintan/projects.html

*** Project 4. Testing Deep Learning Systems  



We will build cool and novel techniques to make deep learning code such as TensorFlow and PyTorch reliable and secure. We will build it on top of our award-winning paper (ACM SIGSOFT Distinguished Paper Award)! 



Machine learning systems including deep learning (DL) systems demand reliability and security. DL systems consist of two key components: (1) models and algorithms that perform complex mathematical calculations, and (2) software that implements the algorithms and models. Here software includes DL infrastructure code (e.g., code that performs core neural network computations) and the application code (e.g., code that loads model weights). Thus, for the entire DL system to be reliable and secure, both the software implementation and models/algorithms must be reliable and secure. If software fails to faithfully implement a model (e.g., due to a bug in the software), the output from the software can be wrong even if the model is correct, and vice versa.  



This project aims to use novel approaches including differential testing to detect and localize bugs in DL software (including code and data) to address the testing oracle challenge. 



Early work and background can be found here: 
https://www.cs.purdue.edu/homes/lintan/publications/eagle-icse22.pdf
https://www.cs.purdue.edu/homes/lintan/publications/fairness-neurips21.pdf
https://www.cs.purdue.edu/homes/lintan/publications/variance-ase20.pdf
https://www.cs.purdue.edu/homes/lintan/publications/cradle-icse19.pdf


Big Data/Machine Learning, Cybersecurity, Deep Learning, Other

• Computer Science
• Computer Engineering
• software engineering

https://www.cs.purdue.edu/homes/lintan/

Lin Tan

Currently, most cancer diagnosis procedures include a diagnostic imaging process, such as a CT scan followed by a tumor biopsy. Tissue biopsy is an invasive and painful procedure and may pose health risks for patients such as those with kidney diseases. Liquid biopsy, the ability to detect and monitor disease through biofluids, is highly promising and may replace tissue biopsy with an immense potential public health impact. The use of liquid biopsy offers numerous advantages in the clinical setting, including its non-invasive nature, a suitable sample source for longitudinal disease monitoring, a better screenshot of tumor heterogeneity, and lower costs compared to tissue biopsy. Increasing evidence indicates an important cellular function of exosomes and other extracellular vesicle (EV) particles in tumor biology and metastasis, presenting them as intriguing sources for biomarker discovery and disease diagnosis. However, the vast majority of current exosome/EV studies focus on their miRNAs, with few studies on functional proteins such as phosphorylated proteins. As phosphorylation is a major player in cancer and other disease progression, EV phosphoproteins are expected to become actively pursued targets for in vitro disease diagnosis. We were the first group to demonstrate that many phosphoproteins in exosomes and microvesicles could be extracted from a small volume of biofluids, identified by high-resolution mass spectrometry (MS), and verified as potential cancer markers (Chen et al PNAS 2017). In this project, we will focus on non-invasive cancer detection by coupling CT scans with liquid biopsy to eliminate the need for surgery by more than 50%. The IU Urology team led by kidney surgeon Dr. Boris and Dr. Tao’s lab at Purdue University collaborated with prior funding have established specific biosignatures found in low- and high-grade clear cell RCC. An undergraduate student may be involved in the protein sample preparation from biofluids and tissues, maintenance of equipment, and/or bioinformatics analysis.

Big Data/Machine Learning, Biological Characterization and Imaging, Deep Learning, Medical Science and Technology, Nanotechnology

• Computer Science
• Biochemistry
• Biomedical Engineering
• Chemistry
• Biology

Certain coding skills and biostatistics are highly desirable but not required.

Biochemistry AND Chemistry

W. Andy Tao

Single cell pathway analysis refers to the study of biological pathways and processes in individual cells, rather than in bulk tissue samples or cell populations. This approach allows for a deeper understanding of cellular heterogeneity and enables the identification of rare cell types and subpopulations.

Single cell pathway analysis typically involves the use of single cell omics technologies such as single cell transcriptomics (scRNA-seq), single cell proteomics, or single cell epigenetics. These techniques provide a high-throughput and comprehensive view of the molecular changes taking place within individual cells.

Applications of single cell pathway analysis include the study of development, disease, and cellular signaling. For example, it can be used to uncover the complex molecular changes that occur during cell differentiation and the progression of diseases such as cancer. It can also be used to study the effects of drugs and other treatments on individual cells.

there has been multiple methods developed to perform single cell analysis, however, how well these methods perform remains unclear. The aim of this project is developing a benchmark to evaluate various single cell pathway analysis methods.

Big Data/Machine Learning, Biological Simulation and Technology

• No Major Restriction

Computational genomics/bioinformatics. Knowledge of pathway analysis tools and single cell technologies.

BCHM

Majid Kazemian

Project 1: This summer research project will use high resolution imaging test the hypothesis that decoding of Ca2+-flux frequency by CaM and CaMKII plays a major role in dynamic actin polymerization and dendritic spine morphology. Student will learn basic laboratory skills, primary cell culture, immunohistochemistry, confocal imaging and image analysis.

Project 2: This summer research project will use computational modeling of Ca2+/Calmodulin and CaMKII interactions in dendritic spines to test the hypothesis that decoding of Ca2+-flux frequency by CaM and CaMKII plays a major role in dynamic actin polymerization and dendritic spine morphology. Computational tools that will be used include ordinary and partial differential equations and machine learning techniques to rapid explore model parameter space.

Research Question Overview:
Neuronal synapses are tightly regulated intercellular junctions that rapidly convey information from an upstream pre-synaptic neuron to a downstream post-synaptic neuron. Dynamic strengthening or weakening of synaptic connective strength, known as synaptic plasticity, is a critical feature of neuronal function. The direction of synaptic plasticity (increased connective strength (LTP) versus decreased connective strength (LTD)) depends on the timing of action potentials (AP), which is translated into frequency signals of Ca2+ ion flux through NMDA
receptors (NMDAR) located on dendritic spines (100-500nm mushroom-like protrusions that form the post-synapse).

The timing and direction of synaptic plasticity is also exquisitely regulated by dynamic organization and spatial localization of synaptic adhesion molecules, signaling receptors, ion channels, and the intracellular cytoskeleton within spines. However, it not clear to how these electrical, biochemical, and mechanical cues are integrated to produce robust, repeatable, and highly dynamic synaptic plasticity that lasts over the lifetime of a neuron (decades). Our recent work has shown that competition for CaM-binding can influence the Ca2+ frequency-dependence of protein activation and downstream signaling. In particular, the highly expressed Ca2+/calmodulin-dependent kinase II (CaMKII) plays a key role in synaptic plasticity via two
important aspects of its function: (1) CaMKII is highly involved in Ca2+-dependent signal transduction via phosphorylation of a number of downstream proteins including ion channels, guanine nucleotide exchange factors (GEFs), GTPase activating proteins (GAPs), and transcription factors, and (2) CaMKII acts as a multivalent scaffold that binds multiple proteins simultaneously and localizes them to post-synaptic spines, including both filamentous and monomeric actin and may regulate actin polymerization in the spine.

Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Biotechnology Data Insights, Cellular Biology

• No Major Restriction

Weldon School of Biomedical Engineering

Tamara Kinzer-Ursem

The student will work on existing computational models (agent-based models or partial differential equation models), making updates toward adapting existing models to new biological systems. Student will be co-mentored by Elsje Pienaar, BME Dept.

Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology

• No Major Restriction

Mechanical Engineering

Adrian Buganza Tepole

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. One particular target project is a novel localized deep learning approach that we have named a Minimal Learning Unit (MLU). 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.

Big Data/Machine Learning, Deep Learning

• No Major Restriction

ECE

David Inouye

The project goal is to investigate the trends in next generation mobility in the US as evidenced by bike-sharing ridership and electric vehicle (EV) ownership. Objectives include: i) exploring the effect of the urban built environment and demographical fabric on the usage of bike-sharing; ii) forecasting EV ownership rates in the future considering the influence of incentives, new technologies, and barriers. The student candidate will collect historical data available from public sources such as US Census, US Department of Energy, FHWA and other sources and compile with bike-sharing ridership data from an open- source website, EV registration data and other survey data collected by the mentor/faculty advisor. Using these data, a baseline model (which can be a time series model, or any machine learning model) will be developed that will incorporate the effects of influencing factors affecting bike-sharing ridership and/or EV ownership. The student will get an opportunity to work with scholars in the STSRG group as well as to collaborate with the ASPIRE Engineering Research Center.

Big Data/Machine Learning, Energy and Environment, Engineering the Built Environment, Other

• No Major Restriction

- Knowledge of MS Excel and programming (R, Python, C++, Java) - Basic knowledge or course work in statistics (regression, time series) - Data analysis skills including downloading, cleaning, and merging different datasets

Civil Engineering

Nadia Gkritza

The pandemic, such as COVID-19 crisis, has highlighted the requirement for smart manufacturing in pharmaceuticals. Continuous manufacturing is a highly promising solution given its lower capital cost, smaller footprint, and higher efficiency compared to batch manufacturing. Instead of relying on frequent off-line quality tests of samples from each batch, designing an effective and efficient process with knowledge and optimal control strategies becomes increasingly important. Ultimately, an automated smart system can be built to produce high-quality drug products with minimized errors from human intervention.

In a dry granulation tableting line, the powders are transformed into granules before being compressed into tablets. The granulation step can increase the powder flowability by enlarging particle size and improving the powder blend's content uniformity by minimizing segregation. The goals of this project include (1) investigating the impact of granulation on final tablet qualities and building high-fidelity models using first principles and machine learning, and (2) developing soft sensors to predict critical quality attributes such as tensile strength in real time. (3) Implementing model-based process control strategy to control end-to-end pharmaceutical manufacturing processes. All the research works will be conducted in Purdue's newly installed tablet manufacturing pilot plant at the FLEX Lab in Discovery Park.

Big Data/Machine Learning, Chemical Unit Operations, Other

• No Major Restriction

Basic skills in programming (Python or MATLAB) and powder characterization experience would be a plus, but they are not necessary. All students are welcome if they are interested in hands-on experiments and pharmaceutical processes.

Chemical Engineering

Gintaras Reklaitis

The continuous mode of manufacturing for pharmaceutical products represents the future of the pharmaceutical industry; it ultimately leads to cheaper and safer drugs, as well as a more reliable drug supply chain. To realize these advantages, however, effective fault detection and diagnostic systems need to be in place, so intervention strategies can be implemented in case the system goes malfunctions.

In this project, we will investigate the ribbon splitting phenomenon in a roller compactor, which is a phenomenon can adversely affect that quality of the product granules coming out of the roller compactor. Little is known about its impact on the product quality as well as the predictability of the phenomenon. The ability to predict this phenomenon can be a boon to effective implementation of condition-based maintenance strategies that have been accepted to be a critical requirement for the successful shift to continuous pharmaceutical manufacturing. This study requires particle technology expertise, which will be provided by Prof. Marcial Gonzalez in Mechanical Engineering, as well as process systems engineering expertise provided by Prof. Rex Reklaitis and Prof. Zoltan Nagy in Chemical Engineering.

Big Data/Machine Learning, Chemical Unit Operations, Material Processing and Characterization, Other

• No Major Restriction

Python programming/coding experience is a PLUS, but not required, although enthusiasm to learn is a must. Students interested in a career in powder processing and/or pharmaceutical manufacturing, are encouraged to apply.

Davidson School of Chemical Engineering

Gintaras Reklaitis

As illustrated with the COVID vaccines, storage and stability of medications can limit widespread availability. We are developing innovative chemical imaging tools with ultrafast pulsed lasers capable of mapping transformations within medical formulations to model and inform stability and bioavailability. Depending on the interests of the students, project scope can range from: i) bench-science in sample preparation and characterization, ii) instrument design and optical path alignment, iii) data acquisition and image analysis algorithm development, iv) partnership with collaborators in the pharmaceutical industry. We have a vibrant and diverse cohort of current researchers dedicated to fostering a supportive and collaborative research environment for all.

Big Data/Machine Learning, Biological Characterization and Imaging, Material Processing and Characterization

• No Major Restriction

Chemistry

Garth Simpson

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.

Big Data/Machine Learning, Deep Learning, Fluid Modelling and Simulation

• No Major Restriction

Earth, Atmospheric, and Planetary Sciences

Lei Wang

Our research group is in the midst of constructing new quantum optics characterization setups. These setups are used to characterize the photoluminescence properties of different quantum emitters down to the single photon level! This single emitter level property characterization is critical in the development of quantum optical computing, sensing, and communications! We are looking for an undergraduate student that can help with the development of software to control the various parts of the setup and to write the drivers to interface the hardware to the upper-level analysis software required to control the setup.

Big Data/Machine Learning, Deep Learning, Fabrication and Robotics, Material Processing and Characterization, Nanotechnology, Other

• No Major Restriction

Experience with embedded systems programming, analog to digital conversion experience, driver experience, python software experience.

Electrical and Computer Engineering (ECE)

Vladimir Shalaev

This research project focuses on the development of software algorithms for automated analysis of single photon emitters in silicon nitride nanopillars. The students role will be to develop these algorithms to help produce large datasets to be used in machine learning studies and in basic process development studies of emitters generated though the annealling of SiN/SiO nanopillars.

Big Data/Machine Learning, Material Processing and Characterization, Nanotechnology, System-on-a-Chip

• No Major Restriction

Strong python programming skills and algorithm development skills. Additionally, image processing skills are a plus!

Electrical and Computer Engineering (ECE)

Alexander Kildishev

Internship opportunities:
1. Data analytics: Instrument and perform analysis of scientific application workloads on the Anvil system
2. High Performance Computing (HPC): Extend the Anvil system to burst scientific workflows into the Microsoft Azure cloud
3. Kubernetes: To support science gateways applications, extend Anvil’s Kubernetes-based composable subsystem to use cloud-based Kubernetes clusters
4. Containers to Support Education: Enable the use of large-scale notebook deployments to provide interactive access to Anvil in support of education

Applicants must be U.S. citizens. Open to undergrad students from all backgrounds.

Big Data/Machine Learning, Computer Architecture, Internet of Things (IoT), Other

• No Major Restriction

Linux command line experience preferred. However, programming experience is not a requirement for our REU. We seek students with a range of computational backgrounds and will provide research opportunities appropriate for beginner to advanced levels in computing. Our REU is designed to help you develop these computational skills.

RCAC

Amanda Hassenplug

Refractory alloys are used in extreme environments (high temperatures, corrosive environments, high stresses, high irradiation fluxes) and enable transportation, energy generation, and defense technologies to be realized. However, refractory alloys developed to date lack a balance of high temperature properties, namely oxidation resistance and strength, which, in some situations, prevents them from replacing other state of the art materials such as Ni-based superalloys. Over the past year, our group has developed machine learning and other predictive models that enable high-throughput discovery of novel refractory alloys exhibiting such balance of properties.

This SURF project aims to characterize the strength and oxidation resistance of tens to hundreds of refractory alloys using high-throughput characterization methods. Such methods for this project could include: Raman microscopy, surface profilometry, X-ray diffraction, automated scanning electron microscopy, and indentation. As part of this project, you will learn at least two of these methods and apply them to compositionally graded specimens comprising up to 85 unique alloys - potentially encompassing thousands of unique alloy compositions.

Significant data will be collected during this project, and the data must be collected and stored according to the FAIR principles (Findability, Accessibility, Interoperability, Reuse). Thus, some background in Python programming and Excel is desired for this project. It is expected that at the end of this project, you will publish a publicly accessible NanoHub.org tool that enables users from across the world to access and analyze the data.

Big Data/Machine Learning, Material Processing and Characterization

• Materials Engineering
• Chemical Engineering
• Mechanical Engineering
• Physics

- Materials Characterization - Computer Science / Programming (python preferred)

Materials Engineering

Michael Titus

The objective of this project is to utilize state-of-the-art proton transfer reaction mass spectrometry (PTR-MS) to evaluate emissions and exposures of volatile chemicals in buildings. My group is investigating volatile chemical emissions from consumer and personal care products, disinfectants and cleaning agents, and building and construction materials. You will assist graduate students with full-scale experiments with our PTR-MS in our new Purdue zEDGE Tiny House and process and analyze indoor air data in MATLAB.

Big Data/Machine Learning, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization

• No Major Restriction

Preferred skills: experience with MATLAB, Python, or R. Coursework: environmental science and chemistry, physics, thermodynamics, heat/mass transfer, and fluid mechanics.

Lyles School of Civil Engineering

Nusrat Jung

This project is one of several SCALE SURF research projects, and is restricted to US citizens. If you are interested in more than one SCALE SURF project, you can apply to all of them with one application. ** Be sure to address each project by name in your application. ** See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.

Most of the materials we encounter in our daily lives are ‘bulk’ materials – they contain an enormous number of atoms in all three dimensions. However, if we instead consider materials with one dimension of only a few atoms in thickness, like graphene, we can achieve many unique physical and chemical properties unique from their bulk counterparts. For example, 2D magnetic materials have drawn significant attention because of their application in spintronics and quantum computing. One class of 2D materials with the potential to serve as the first room-temperature 2D magnets are MXenes, near atomically thin transition metal carbides or nitrides. For a magnetic material, the configuration can be ferromagnetic (FM) or antiferromagnetic (AFM) depending on the direction of spins of electrons. Using electronic structure calculations based on density functional theory (DFT), we can identify the magnetic configuration with lower energy. Further, the critical temperature, e.g. Curie temperature, is the temperature above which the material loses the spontaneous magnetization. For real-world applications, magnetic materials with a critical temperature that is higher than room temperature are desired. This project will combine DFT calculations to discover magnetic MXenes with high Curie temperatures.

In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.

For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.

Big Data/Machine Learning, Deep Learning, Material Modeling and Simulation, Microelectronics, Nanotechnology

• No Major Restriction

Introductory materials science or physics/chemistry of materials. Introductory programming

MSE

Alejandro Strachan

This project is one of several SCALE SURF research projects, and is restricted to US citizens. If you are interested in more than one SCALE SURF project, you can apply to all of them with one application. ** Be sure to address each project by name in your application. ** See https://nanohub.org/groups/scale/research_su23 to view all of the SCALE SURF research projects for summer 2023.

2D materials are a class of crystalline solids with a single layer only a few atoms thick. Because of their ultrathin body, 2D materials possess unique physical and chemical properties that are usually not seen in their bulk counterparts. Nowadays, 2D materials have been widely applied in solar cells, memory devices, chemical sensors. One emerging subset of the 2D materials class is MXenes, a new type of 2D material that has been successfully synthesized and studied in the last decade. MXenes are defined by a transition metal carbide or nitride with only atomically thin layers. The properties of a specific MXene are not always suitable for a given application, and one way to tune their properties is to apply strain. The mechanical strain has effects on the electronic and magnetic properties of materials because the strain changes the crystal structure of the materials. For example, the band gap of a material is an important property for electronic applications, and studies have shown that for some 2D materials, biaxial tensile strain decreases the band gap. Different strains, including biaxial, uniaxial, tensile, and compressive, also each have a different effect on the properties. In this project, the strain-tuned electronic and magnetic properties of novel MXenes will be studied. The physical mechanism behind the strain-induced properties will be characterized based on the change of crystal structures.

In your application, please specify which of the SCALE technical areas you are most interested in. The technical areas are:
• Radiation Hardening
• System-on-Chip
• Heterogenous Integration/ Advanced Packaging
• Program Evaluation
Be sure to name any specific SCALE projects you are interested in, and include information about how you meet the required and desired experience and skills for each of these projects.

For US citizen students who are interested: you can become part of the Purdue microelectronics program called SCALE, sponsored by the Department of Defense. In SCALE, you will have opportunities for continuing research (paid or for credit) and industry and government internships throughout your time at Purdue. Please apply to SCALE here: https://research.purdue.edu/scale/.

Big Data/Machine Learning, Material Modeling and Simulation, Microelectronics, Nanotechnology

Introductory materials science or materials physics/chemistry Introductory programing

MSE

Alejandro Strachan

The research project is to hunt for new particles, i.e bound states of top quark, in the huge data set collected with the CMS detector at the Large Hadron Collider. Proton-Proton collision data is searched for any evidence or deviation from the standard model of particle physics. The candidate will use cutting-edge machine learning, artificial intelligence techniques to shed light on what holds the universe together. In particular with regard to the stability of the electroweak vacuum aka the fate of the universe. The research is able to shed light on what stabilizes the Higgs boson mass at 125 GeV and renders it not impacted by higher order loop effects involving top quarks. Without a solution the Higgs boson mass is driven to unphysically high mass values and hence, is in contradiction with the Higgs boson observation at low mass in 2012.

Candidates will be able to use a vast sample of top quark data, literally 100's of millions of top quark to search for any evidence of new particles. The Jung group even uses quantum computers to boost efficiency for reconstructing events and participants can have a choice in the direction and emphasis of the research project to the limits of what is possible. Students will contribute to the review process of analysis and publication and have a chance to be author for publication of technical/algorithm side or even for physics publications (provided contributions are above required threshold), provided sustained and multiple semester engagement.

Big Data/Machine Learning

python, c/c++ other programming languages are an advantage, course work on quantum mechanics and/or particle physics introductory level courses, modern physics are an advantage.

Physics and Astronomy

Andy Jung

Two projects are available. One involves the investigation of enhancing optical imaging resolution using single photon counting techniques. Conventional optical imaging has a hard 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. By way of example, being able to sense with light deeper in the brain would be of enormous benefit in neuroscience. The statistics of photons emitted by or transmitted through an object contain valuable information about the object which could be used to enhance image resolution and possibly see through substantial background scatter. Experiments will be conducted using laser light and with a set of single photon avalanche detectors (SPADs) to measure photon correlations in time, over wavevector (direction), and between detectors in various imaging configurations. Results from these experiments will be used to assess the effectiveness of various techniques for enhancing spatial resolution in imaging applications. This work has a diverse set of potential applications including biological imaging, sensing defects in semiconductors, and imaging through fog. The other project relates to experimental work and the modeling of optical forces on structured membranes induced by a laser. The mechanical motion of a thin membrane deflected by laser light will be used to determine the membrane properties from experimental and simulated data. This will allow extraction of the mechanical material properties and more generally the validation of a theory for optomechanics that can then be used in design. The nascent field of optomechanics offers enormous impact scope, including remote actuation and propulsion, of importance in fields as diverse and molecular biology, communication, and transport. This project relates to attaining the underpinnings to move along such paths in engineering, as well as the basic physics of optical forces in material at small length scales.

Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Composite Materials and Alloys, Deep Learning, Material Processing and Characterization, Medical Science and Technology, Nanotechnology

• Electrical Engineering
• Mechanical Engineering
• Physics
• Biomedical Engineering

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, modeling, data analysis using MATLAB or python, and review relevant literature.

Electrical Engineering

Kevin Webb

Automation is being applied to increasingly complex tasks in manufacturing, medical, military applications and more. There is a need for better human automation interaction to prevent the misuse, disuse and abuse of automation. Our main objective is to develop algorithms for cognitive control so that automated and autonomous systems can respond better to, and guide, human behavior such that task performance is maximized. During this SURF 2023 project, the researcher will help with developing experimental platforms involving human-automation interaction utilizing an online quadrotor simulator module. This may include (but is not limited to) control algorithm and heuristic design of automation assistance, development of human automation interaction contexts and tasks, and incorporating psychophysiological sensors for data collection. Previous and current experiments utilizing this and similar platforms have involved modeling trust, modeling self-confidence, modeling risk perception, and improving learning rates.

Big Data/Machine Learning, Human Factors, Learning and Evaluation

• No Major Restriction

Strong coding skills, including experience in HTML, Javascript, MATLAB, and/or Python.

School of Mechanical Engineering

Neera Jain

Synopsis: The goal of this project is to apply quantum mechanics-based density functional theory simulations and machine learning to design novel halide perovskites with targeted photovoltaic, surface, and adsorption behavior for improved photocatalytic performance.

Targeted Need: Challenges of environmental pollution, global energy shortage, and overreliance on fossil fuels can be addressed using photocatalysis, where solar energy is harnessed for chemical processes such as hydrogen production, degradation of pollutants, and CO2 reduction [1]. Many semiconductors have been used as photocatalysts based on suitable band edge positions relative to redox potentials, strong optical absorption, and desirable adsorption and desorption of chemical species; examples include TiO2, Ga2O3, C3N4, CdS, and ZnS [2]. However, many limitations exist owing to wider than desired band gaps, ineffectiveness of charge carriers, and formation of harmful defects, motivating the search for novel and improved materials. Cheap and high-performing photocatalysts can also help avoid the use of transition or precious metals such as Pt and Pd as catalysts [3]. The chemical space of potential semiconductor photocatalysts is massive and not conducive to brute-force experimentation or even computation, which necessitates the use of data-driven strategies combining large computational datasets and state-of-the-art machine learning [4], prior to experimental validation and discovery.

Opportunity: Metal halide perovskites (HaPs) have risen in prominence for solar and related optoelectronic applications, and are suggested as promising photocatalysts. Recent publications report the use of MAPbI3, MAPbBr3 (MA=methylammonium), CsPbI3, Cs2BiAgBr6, and other single/double inorganic/hybrid perovskites, either in bulk crystalline form, 2D variants, nanoclusters, or as part of heterostructures, for water splitting, CO2 reduction, and organic synthesis [1,2]. However, this field remains very much in its infancy—HaPs are desirable photovoltaic (PV) materials with extremely tunable properties, but an exhaustive study of band edges, surface energies, and adsorption behavior across a wide chemical space is missing. Using high-throughput density functional theory (HT-DFT) computations, our research group has developed an initial dataset of the stability, band gap, and optical absorption characteristics of ABX3 HaPs with mixing at A, B, or X sites using common elemental or molecular species [5]. This provides the starting point for exploring photocatalytic activity of HaPs as a function of composition, phase, and surface orientation, by combining HT-DFT with machine learning (ML). Since DFT computations are expensive and cannot be performed endlessly, ML models trained on DFT data can help predict optical, electronic, surface, and adsorption properties of millions of new perovskite compositions, to accelerate by several orders of magnitude the screening of novel HaPs with a suitable combination of properties for catalyzing reactions.

Objectives: In this project, a HT-DFT+ML prediction, screening, and design approach will be applied to discover novel HaP compositions that display desired stability, optical absorption, surface stability, and activity towards species, for next-generation photocatalysis of technologically-important chemical processes, including CO2 reduction, H2 and O2 evolution (water splitting), and synthesis of various hydrocarbons. Specific objectives include: (i) using the existing DFT dataset of HaP crystal structures to build surface slabs, calculate surface energies, and adsorption energies of various molecules on stable surfaces, (ii) unique encoding of each material (descriptors) in terms of structure, composition, surface atoms, adsorbing species, etc. [4], and (iii) training of ML models based on regression techniques such as random forests and neural networks, ensuring rigorous optimization of hyperparameters, training data size, input dimensions, and applicability towards any new data point.

Role of Student Researcher: Using our available codes, software, and computing resources, students can quickly start running and analyzing simulations of photocatalytic properties. A variety of existing schemes can be applied and tested for numerical representation/description of materials and property prediction, such as using graph convolutional neural networks (GCNNs) for automatic crystal structure representation, which our group has good experience with. Student will carry out DFT and ML tasks under the guidance of a graduate student and the professor, and will be given the opportunity to lead one or two potentially high-impact journal publications. Given the prior work that has gone into this project, chances of success are very high, and future prospects will be plenty.

References
1. J. Yuan et al., Nanoscale, 13, 10281 (2021).
2. K. Ren et al., Journal of Materials Chemistry A, 10, 407 (2022).
3. Z. Luo et al., Nature Communications, 11, 4091 (2020).
4. J. Schmidt et al., npj Computational Materials, 5, 83 (2019).
5. A. Mannodi-Kanakkithodi et al., Energy and Environmental Science, 15, 1930-1949 (2022).

Big Data/Machine Learning, Chemical Catalysis and Synthesis, Energy and Environment, Material Modeling and Simulation

• No Major Restriction

Any experience with coding and/or data science will be useful, but not necessary. If student has taken courses on fundamentals of materials science, that will be helpful.

Materials Engineering

Arun Kumar Mannodi Kanakkithodi

The goal of this project is to develop precision learning intervention technology that leverages semantic network science to support early language learning and early intervention for developmental language disorder (DLD). DLD affects approximately 7% of the population, and results in lifelong risks for poor biomedical, educational, and professional outcomes, leading to tremendous costs to individuals and society. Our group seeks to combine recent theoretical and technical advances to develop methods for early identification and intervention of this common, yet understudied condition. Student will participate in coding / development of automated tools that tune early language learning targets according to the knowledge of the learner and will help pilot and assess efficacy of different intervention approaches. Student will work with senior members of the lab (postdocs and lab manager) to develop and acquire data to support the submission of a larger grant application in the Fall.

Big Data/Machine Learning, Medical Science and Technology, Other

• No Major Restriction

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.

SLHS

Arielle Borovsky

The human Microbiome is a critical regulator of health and disease. Vaginal microbiome dysfunction has been implicated in several female reproductive tract conditions, but a precise understanding of the mechanisms by which the vaginal microbiome regulates human health are poorly understood. The objective of this project is to analyze human Microbiome data from 400 women to identify microbes, metabolites, and bacterial functions that regulate the expression of the progesterone receptor.

Big Data/Machine Learning, Biological Characterization and Imaging

• No Major Restriction

Statistics, Modeling topics, Cellular biology

Weldon School of BME

Douglas Brubaker