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?
News
- (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) SIAM CSE
- Atlanta Chakraborty is giving a presentation “An Approximate Bayesian Computation Approach to Goal-Oriented Bayesian Experimental Design”.
- Thomas Coons is giving a presentation “A Multifidelity Approach to Model-Based Bayesian Optimal Experimental Design”.
- Jiayuan Dong is giving a presentation “Variational Bayesian Optimal Experimental Design with Normalizing Flows”.
- Jeremiah Hauth is giving a presentation “Stein VI for Bayesian Neural Network Prediction of Helicopter Rotor Performance”.
- Xun Huan is giving a presentation “Reinforcement Learning Framework for Bayesian Sequential Optimal Experimental Design”.
- Chengyang Huang is giving a presentation “Inverse Reinforcement Learning Via Variantioanl System Identification of Fokker-Planck Equation”.
- Aniket Jivani is giving a presentation “Uncertainty Quantification for Random Field Quantities Using Multi-Fidelity Karhunen-Loève Expansions with Active Learning”.
- Codie Kawaguchi is giving a presentation “Quantifying and Reducing Uncertainty in Inertial Confinement Fusion Experiments Using Optimal Experimental Design”.
- Snehal Prabhudesai is giving a presentation “Lowering The Computational Barrier : Selective Bayesian Uncertainty Quantification for Transparency in Medical Imaging AI”.
- Xun Huan co-organized a two-part minisymposium “Model-Based Optimal Experimental Design” (parts I, II).
- (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.
- (2022/07) Our paper “Multi-fidelity uncertainty quantification of particle deposition in turbulent pipe flow” is published in Journal of Aerosol Science.
- (2022/07) Our paper “A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning” is published in ACM Transactions on Computer-Human Interaction.
- (2022/07) Our preprint “Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference” is available on arXiv.
- (2021/10) Our preprint “Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning” is available on arXiv.
- (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.
