Welcome to the Uncertainty Quantification & Scientific Machine Learning Group!
Our research focuses on developing computational methods of uncertainty quantification and machine learning for complex systems in science, engineering, and medicine. We seek to derive these methods under a rigorous framework of mathematics and statistics together with computational feasibility and scalability. Our projects entail answering questions such as:
- How much uncertainty accompanies the model prediction, and how can we reduce it from new data/evidence?
- What data should we acquire next, and how many?
- How can machine learning be used together with physical modeling?
- (2023/04) Tahera Hossain and Wanggang Shen will give a presentation “A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning” at CHI 2023.
- (2023/03) SWQU Spring Meeting
- Hongfan Chen presented a poster “Connecting White Light Images with In-situ Observations of Solar Wind Quantities for CME Simulations in the SWMF”.
- Aniket Jivani presented a poster “Uncertainty Quantification & Global Sensitivity Analysis for CME Simulations in the SWMF”.
- (2023/01) Congratulations to Yossi Cohen for receiving the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship!
- (2023/01) Congratulations to Yossi Cohen for successfully defending his Ph.D. thesis!
- (2023/02) Our paper “Lowering the computational barrier: Partially Bayesian neural networks for transparency in medical imaging AI” is published in Frontiers in Computer Science.
- (2023/01) Our paper “Global Sensitivity Analysis and Uncertainty Quantification for Background Solar Wind using the Alfvén Wave Solar Atmosphere Model” is published in Space Weather.
- (2022/12) Yummy food at Namaste Flavours!
- (2022/11) Congratulations to Codie Kawaguchi, Shuyu Long, and Thomas Coons for successfully passing the Ph.D. Qualifying Exam!
- (2022/05) End-of-semester celebration at the Grove!
- (2022/02) A fun group dinner at Cafe Zola!
We are grateful to all our current and past sponsors.