REU Projects (2022-24)

Undergraduate students that participated in the 2022-2024 REU summer programs under the NSF grant titled “Advancing high-performance computing (HPC) opportunities in undergraduate research at UW-Eau Claire to meet challenges of multidisciplinary computational science” where able to be involved in a variety of different research projects.

Details of each of the research opportunities are given below.

Computer Science

Rahul Gomes

Project: Designing Optimized Deep Learning Algorithm for Image Classification

Research in the Gomes Lab focuses on optimizing deep learning algorithms to enable faster image classification and segmentation with fewer training parameters and training labels. Using deep learning, students are developing software tools that have a direct impact on health sciences. They are exploring optimization techniques that could find application in imaging devices like CT scanners capable of rapid disease diagnosis as well as detection of surgical implants.

Rakib Islam

Project: Entity-Based Aspect-Oriented Opinion Mining in Software Engineering

Research in the Islam Lab focuses on concepts of software engineering and machine learning. Using natural language processing, students are exploring sentiment analysis of software engineers through the opinions on the various aspects (e.g., bug, performance, and security) of software-specific entities (e.g., tools, libraries, and APIs).

Chemistry and Biochemistry

Sudeep Bhattacharyay

Project 1: Hybrid Quantum-Classical Simulations to Probe Redox-Dependent Inhibition in Enzymes

The Bhattacharyay Lab in the Department of Chemistry and Biochemistry is involved in studying medicinal aspects of small organic molecules targeting oxidation-reduction processes involving enzymes. The research group uses hybrid quantum/classical simulations on supercomputers to study the chemistry and functional inhibitors of these enzymes. 

Project 2: Developing Inhibitor Screening Techniques Using Deep Learning and High-Throughput Docking

Based on students' learning outcomes, the Bhattacharyay lab group is exploring to develop a drug molecule screening method that employs deep learning and artificial intelligence-based algorithms in addition to computational quantum chemistry and high throughput docking of small molecules.

Stephen Drucker

Project: Testing and Refinement of Computed Properties of Electronically Excited Molecules

The Drucker Lab in the Department of Chemistry and Biochemistry explores the science of molecules that interact with electromagnetic radiation. Using quantum chemical calculations as well as the experimental jet-cooled laser spectroscopy technique, the researchers try to understand how the structure and bonding of the molecules under study have changed as a consequence of the laser excitation.

Sanchita Hati

Project: Modeling Protein Dynamics and Catalysis in Intracellular-Like Crowded and Confined Environments

The Hati Lab in the Department of Chemistry and Biochemistry explores how a protein behaves in a crowded and confined environment, just like in a realistic biological environment. Research students in the lab create model enzyme systems to mimic the intracellular environment and simulate those systems using high-performance computers to study how a protein’s structure and dynamics change due to crowding and confinement. A molecular-level understanding of the effects of crowding and confinement on protein structure, dynamics, and function could aid in drug development.  

Thao Yang

Project: High-Throughput Docking of Small Molecules Inhibitors to the Molybdenum-Containing Active Site of Xanthine Oxidase

The Yang Lab in the Department of Chemistry and Biochemistry is studying drug molecules design using high-performance computing. Using graphics-processor unit accelerated dynamics and molecular docking algorithm, his students will try to create a faster screening process of drug molecules that inhibit an enzyme. 

Geography and Anthropology

Papia Rozario

Project: Vegetation Mapping with High-Resolution Low Altitude UAV-based Imagery Using Deep Learning.

The Rozario Lab in the Department of Geography and Anthropology focuses on the acquisition, processing, and interpretation of very high-resolution UAV imagery. Students explore machine learning techniques to identify land cover classes and study land-use changes. Hyperspectral imagery is also being used for calculating biomass and carbon mapping in forestry applications.

Materials Sciences and Biomedical Engineering

Michael Walsh

Project: Laser-based Infrared Spectroscopic Imaging for Diagnostic Purposes

Research in the Walsh Lab is focused on developing a methodology, instrumentation, and algorithm for novel label-free imaging technologies that could be applied towards disease diagnosis. This research plays a promising role in identifying novel biomarkers in tissue images that were earlier impossible to perform using manual segmentation. Students use infrared spectroscopic imaging devices equipped with a hyperspectral image sensor to capture rich amounts of biochemical information from the tissues.

Ying Ma

Project: Computer Simulation of Electrode Materials for Lithium-ion Batteries

Research activities in the Ma Lab focuses on the computational study of novel materials for energy conversion and storage. Using computer simulation techniques, the students explore new cathode materials for lithium-ion batteries with improved energy density. Topics of recent projects include the study of amorphous sulfur for lithium-sulfur batteries, organic catholytes, and supercapacitors.

Physics

William Wolf

Project: Computational Astrophysical Study of Stellar Structure and Evolution

Research in the Wolf Lab is focused on modeling nova outbursts on accreting white dwarf stars. Students explore how white dwarf stars evolve through successive accretion-driven thermonuclear runaways known as novae by constructing computational models of these stellar systems using the open-source scientific instrument MESA.