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
- (2022/05) End-of-semester celebration at the Grove!
- (2022/02) A fun group dinner at Cafe Zola!
- (2021/11) Congratulations to Jiayuan Dong for successfully passing the Ph.D. Qualifying Exam!
- (2021/08) Congratulations to Chengyang Huang for successfully passing the Ph.D. Qualifying Exam!
- (2021/08) Congratulations to Saibal De for successfully defending his Ph.D. thesis!
- (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.
- (2022/08) Xun Huan is giving a presentation “Multifidelity Karhunen-Loeve Expansion Surrogate Models for Uncertainty Propagation” at the Joint Statistical Meetings.
- (2022/08) WCCM-APCOM Congress
- Siddhartha Srivastava is giving a presetation “Discovery of Cell Migration Models by Data Driven Variational System Identification and Inverse Reinforcement Learning”.
- Christian Jacobsen is giving a presetation “Variational Bayesian optimal experimental design for the discovery of electro-deposition process models”.
- Upcoming:
- (2022/08) UQ for ML Integrated Physics Modeling
- (2022/09) SIAM MDS
- (2022/10) SES Annual Technical Meeting
- (2022/12) AGU Fall Meeting
- (2023/02) SIAM CSE
We are grateful to all our current and past sponsors.
