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?
See our review paper on optimal experimental design!
Check out our real-time prediction of global magnetic perturbations! [preprint]
News
- (2025/05) Our paper “Bayesian model selection for network discrimination and risk-informed decision making in material flow analysis” is published in Journal of Industrial Ecology.
- (2025/05) Our paper “Comparative analysis of machine learning approaches for fetal movement detection with linear acceleration and angular rate signals” is published in Sensors.
- (2025/04) Our preprint “Intelligent data collection for network discrimination in material flow analysis using Bayesian optimal experimental design” is available on arXiv.
- (2025/01) Our paper “Decent Estimate of CME Arrival time from a Data-assimilated Ensemble in the Alfvén Wave Solar atmosphere Model (DECADE-AWSoM)” is published in Space Weather.
- (2025/01) Our preprint “A multi-fidelity estimator of the expected information gain for Bayesian optimal experimental design” 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!
- Upcoming/Interesting Events:
- (2025/06) IMA/ASA SRC
- (2025/06) MSEC
- (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.
