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?
- (2023/12) Hongfan Chen is presenting a poster “Improving Prediction of CME Arrival Time through Data Assimilation of White Light Images” at AGU Fall Meeting.
- (2023/10) Our paper “Stochastic deep Koopman model for quality propagation analysis in multistage manufacturing systems” is published in Journal of Manufacturing Systems.
- (2023/10) Our paper “Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial” is published in Mechanical Systems and Signal Processing.
- (2023/09) Celebrating Zhiyi’s defense with a delightful dinner at Jolly Pumkin!
- (2023/09) Congratulations to Yossi Cohen for being selected to the Rising Stars in Mechanical Engineering workshop at Berkeley!
- (2023/09) Congratulations to Aniket Jivani for winning the 2023 SHINE Outstanding Student Poster Contest!
- (2023/09) Yossi Cohen gave a presentation “To Trust or Not: Towards Efficient Uncertainty Quantification for Stochastic Shapley Explanations” at PHMAP. [paper]
We are grateful to all our current and past sponsors.