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 geomagnetic perturbations! [paper]
News
- (2025/06) Our paper “GeoDGP: One-hour ahead global probabilistic geomagnetic perturbation forecasting using deep Gaussian process” is published in Space Weather.
- (2025/06) Our paper “Variational sequential optimal experimental design using reinforcement learning” is published in Computer Methods in Applied Mechanics and Engineering.
- (2025/06) Trail trek at Stinchfield Woods!
- (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.
- Upcoming/Interesting Events:
- (2025/06) Yossi Cohen is giving a presentation “Data-Driven Prediction and Uncertainty Quantification on Chemical Concentration in Electroless Plating Process” and another presentation “An Industrial Framework for Explainable Anomaly Detection: A Case Study for Pick-and-Place Machines” at MSEC.
- (2025/07) USNCCM
- Yossi Cohen is giving a presentation “Explainable AI for enhanced and trustworthy industrial anomaly detection”.
- Thomas Coons is giving a presentation “Efficient budget allocation strategies for multi-fidelity optimization under uncertainty”.
- Chengyang Huang is giving a presentation “Bayesian system identification via the weak form of the partial differential equations”.
- Siddhartha Srivastava is giving a presentation “Inverse characterization and coarse-grained modeling of agent-based epidemiological dynamics via delay differential equations”.
- (2025/07) Xun Huan is giving a presentation “Optimal Pilot Sampling for Multi-fidelity Monte Carlo Methods” and co-organizing a session “Next-generation optimal experimental design: theory, scalability, and real world impact” at MCM.
- (2025/09) Workshop on Scientific Machine Learning for Differential Equations
- (2025/10) Xun Huan is co-organizing the 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.
