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?
- (2021/06) ISBA World Meeting
- Xun Huan is giving a presentation “Optimal Bayesian Sequential Design Using Reinforcement Learning with Policy Gradient Methods“
- Chengyang Huang is giving a presentation “Fast Approximate Bayesian Inference via Conditional Generative Adversarial Networks“
- (2021/05) EMI/PMR Conference
- Xun Huan gave a presentation “Optimal Sequential Bayesian Design of Experiments Using Reinforcement Learning with Policy Gradient“
- Wanggang Shen gave a presentation “Optimal Bayesian Experimental Design for Variational System Identification of Materials Physics Phenomena“
- (2021/05) Our paper “Conceptual Design of Extreme Sea-Level Early Warning Systems Based on Uncertainty Quantification and Engineering Optimization Methods” is published in Frontiers in Marine Science.
- (2021/04) Xun Huan gave a presentation “Model-based sequential experimental design” at the USACM TTA Uncertainty Quantification and Probabilistic Modeling Webinar.
- (2021/03) Congratulations to Aniket Jivani for successfully passing the Ph.D. Qualifying Exam!
- (2021/03) Congratulations to Codie Kawaguchi for winning the NSF Graduate Research Fellowships Program (GRFP) award!
- News Archive
We are grateful to all our current and past sponsors.