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? (See our review paper on optimal experimental design!)
- How can machine learning be used together with physical modeling?
News
- (2025/01) Our preprint “Bayesian Model Selection for Network Discrimination and Risk-informed Decision Making in Material Flow Analysis” is available on arXiv.
- (2025/01) Our paper “Enhancing dynamical system modeling through interpretable machine-learning augmentations: A case study in cathodic electrophoretic deposition” is published in Data-Centric Engineering.
- (2024/12) Our preprint “Variational Sequential Optimal Experimental Design using Reinforcement Learning” is significantly updated on arXiv.
- (2024/12) Our preprint “GeoDGP: One-hour ahead global probabilistic geomagnetic perturbation forecasting using deep Gaussian process” is available on ESSOAR.
- (2024/11) Healthy hike at Pinckney Recreation Area!
- (2024/10) Our paper “Variational Bayesian optimal experimental design with normalizing flows” is published in Computer Methods in Applied Mechanics and Engineering.
- (2024/10) Our paper “Understanding process—Structure relationships during lamination of halide perovskite interfaces” is published in ACS Applied Materials & Interfaces.
- (2024/09) Our preprint “Decent Estimate of CME Arrival time from a Data-assimilated Ensemble in the Alfvén Wave Solar atmosphere Model (DECADE-AWSoM)” is available on ESSOAR.
- (2024/09) Our paper “Optimal experimental design: Formulations and computations” is published in Acta Numerica. See arXiv for a more up-to-date version!
- (2024/08) Our preprint “A likelihood-free approach to goal-oriented Bayesian optimal experimental design” is available on arXiv.
- Upcoming/Interesting Events:
- (2025/03) SIAM CSE
- Chen Cheng is giving a presentation “Optimal-Stopping Control for Bayesian Sequential Experimental Design”.
- Thomas Coons is giving a presentation “Adaptive Pilot Sampling for Multi-Fidelity Stochastic Optimization”.
- Chengyang Huang is giving a presentation “Bayesian Variational System Identification”.
- Aniket Jivani is giving a presentation “Bayesian Optimal Experimental Design for Adaptive Training of Neural Network Surrogate Models”.
- Codie Kawaguchi is giving a presentation “Optimal Experimental Design for High-Energy-Density Physics”.
- Xun Huan is co-organizing a minisymposium “Advances in Bayesian Optimal Experimental Design”.
- (2025/06) IMA/ASA SRC
- (2025/07) USNCCM
- (2025/07) MCM
- (2025/10) IMSI Workshop on Optimal Control and Decision Making Under Uncertainty for Digital Twins
- (2026/03) SIAM UQ
We are grateful to all our current and past sponsors.
