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
- (2024/03) Thomas Coons is giving a presentation “Adaptive Covariance Estimation for Multi-fidelity Monte Carlo” at Michigan Student Symposium for Interdisciplinary Statistical Sciences.
- (2024/03) Our preprint “Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo” is available on arXiv.
- (2024/03) Our paper “Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity” is published in Frontiers in Systems Biology.
- (2024/02) Our preprint “Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes” is available on arXiv.
- (2024/02) Celebrating Jeremiah’s defense with cool cuisine at Sava’s!
- (2024/02) Our paper “Substituent Impact on Quinoxaline Performance and Degradation in Redox Flow Batteries” is published in Journal of the American Chemical Society.
- (2024/01) Congratulations to Jeremiah Hauth for successfully defending his Ph.D. thesis!
- (2024/01) Our paper “Uncertainty-aware explainable AI as a foundational paradigm for digital twins” is published in Frontiers in Mechanical Engineering.
- (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.
- Upcoming/Interesting Events:
- (2024/04) MICDE Scientific Foundation Models Conference
- (2024/04) Space Weather Workshop
- (2024/05) DAE
- (2024/06) MIDAS Summer Academy
- (2024/06) SIAM MPE
- (2024/06) IMSI Workshop on Mathematical and Statistical Foundations of Digital Twins
- (2024/07) ISBA World Meeting
- (2024/07) ACC
- (2024/07) WCCM
- (2024/08) UQ Summer School
- (2024/08) UQ-MLIP
- (2024/08) MCQMC
- (2024/09) MORe
- (2024/10) SIAM MDS
We are grateful to all our current and past sponsors.