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
- (2023/06) Xun Huan is giving a presentation “Bayesian Sequential Optimal Experimental Design” at the University of Toronto Insitute for Aerospace Studies Seminar Series.
- (2023/06) Jiankan Liao is giving a presentation “Bayesian Model Selection for Network Discrimination in Material Flow Analysis” at ISSST.
- (2023/06) Beckett Zhou is giving a presentation “Multi-Fidelity Propeller Noise Prediction using a Data-Driven Approach” at the AIAA Aviation Forum. [paper]
- (2023/03) Congratulations to Thomas Coons for winning the NSF Graduate Research Fellowships Program (GRFP) award!
- (2023/03) Congratulations to Hongfan Chen for successfully passing the Ph.D. qualifying exam!
- (2023/03) Congratulations to Maria Han Veiga for accepting a tenure-track postion at the Ohio State University!
- (2023/05) Our paper “Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference” is published in Journal of Industrial Ecology.
- (2023/05) Our preprint “Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial” is available on arXiv.
- (2023/03) Our preprint “Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and Prognosis” is available on arXiv.
- (2023/03) Our preprint “Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation” is available on arXiv.
- (2023/03) Our paper “Understanding Uncertainty: How Lay Decision-makers Perceive and Interpret Uncertainty in Human-AI Decision Making” is published in Proceedings of the 28th International Conference on Intelligent User Interfaces (IUI ’23).
- (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.
- (2023/01) Celebrating Lunar New Year and Yossi’s graduation with tasty dishes at Hong Hua!
- (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!
- Upcoming:
- (2023/07) ISIE2023
- (2023/07) USNCCM
- (2023/08) USC UQ Summer School
- (2023/08) ICIAM
- (2023/09) PHMAP
We are grateful to all our current and past sponsors.
