CSE DSI Research Support
The CSE DSI is able to provide seed funding for small projects at the initial stage of research that bring college faculty in data science and science and engineering together.
This funding is in the form of graduate student fellowships where the student is participating in the research and is mentored by at least two faculty from different departments. The initial results from these small projects are expected to form the basis for future larger research proposals.
The call for proposals is usually announced as part of our community-building exercises around a specific topic area chosen in response to interests expressed by our affiliated faculty. The announcement will be circulated to all CSE DSI affiliates.
The table below shows the range of projects supported to date.
| Research Topic | Fellows | Faculty |
|---|---|---|
Towards Unconstrained Multi-sensor “State-of-the-Heart” Monitoring and Disease Prediction | Xiangzhen (Raven) Kong Yijun Lin | Alena Talkachova (BME) |
Large Scale Data Extraction from Population Sampling of Dispersed Waterborne Photovoltaic Microparticles | Tianqi Luo Yijun Lin | Joey Talghader (ECE) |
Can Physically Informed Deep Generative Models Improve Seasonal Predictability of Global Precipitation? | Reyhaneh Rahimi | Ardeshir Ebtehaj (CEGE) |
Sharp Analysis of Atomic-Resolution STEM Data via Deep Learning | Zhong Zhuang Hengkang Wang | Ju Sun (CS&E) |
Constrained Deep Learning for the Efficient Discovery of Stable Solid-State Materials | Jane Schlesinger Ryan Devera | Chris Bartel (CEMS) |
Accelerating Novel 2D Material Discovery Through Machine Learning | Wei Ren Kyle Noordhoek | Ke Wang (Physics) |
Learning Using Privileged Information (LUPI) for Materials Discovery | Eng Hock Lee | Vladimir Cherkassky (ECE) |
Integrated Molecular Simulations and Machine Learning Tools to Uncover the Treasure Trove of Hidden Structures during Crystallization | Steven Hall | Sapna Sarupria (CHEM) |
| Computationally Efficient Bayesian Inference for Population Properties of Astrophysical | Xiao-Xiao Kou | Galin Jones (STAT) Vuk Mandic (PHYS) |
| Accelerating Electromagnetic Design and Simulations with Physics-Informed Artificial Intelligence | Binyao Guo Samuel Dietterich | Qizhi He (CEGE) Shaul Hanany (PHYS) |
| Event Identification from the Sun throughout the Solar System | JangHyeon Lee William Setterberg | Yao-Yi CHiang (CSE) Lindsay Glesener (PHYS) |
| Post-processing Techniques in Neural Gradient Networks with Applications to Inverse Problems in Astrophysics | Andrew Toivonen Akshay Kumar | Michael Coughlin Jarvis Haupt Vuk Mandic |
| Building Multi-modal Foundation Models for Supernovae Data | Felipe Fontinele Nunes Wenya Xie | Ray Liu Michael Coughlin |