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
- (2022/05) End-of-semester celebration at the Grove!
- (2022/02) A fun group dinner at Cafe Zola!
- (2021/11) Congratulations to Jiayuan Dong for successfully passing the Ph.D. Qualifying Exam!
- (2021/08) Congratulations to Chengyang Huang for successfully passing the Ph.D. Qualifying Exam!
- (2021/08) Congratulations to Saibal De for successfully defending his Ph.D. thesis!
- (2022/05) Our preprint “Multi-fidelity uncertainty quantification of particle deposition in turbulent pipe flow” is available on arXiv.
- (2022/01) Our new project has been funded by the W. M. Keck Foundation! (Briefs: 1, 2)
- (2021/10) Our preprint “Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning” is available on arXiv.
- (2021/10) Our paper “Stratification by Tumor Grade Groups in a Holistic Evaluation of Machine Learning for Brain Tumor Segmentation” is published in Frontiers in Neuroscience.
- (2021/08) Our paper “Bayesian Inference of Parameters in Power System Dynamic Model Using Trajectory Sensitivities” is published in IEEE Transactions on Power Systems.
- (2021/07) Our preprint “Reconstruction of the Density Power Spectrum from Quasar Spectra using Machine Learning” is available on arXiv.
- (2022/04) SIAM UQ
- Andrew Davis gave a presentation “Local Approximation of Expected Utility Surface for Nonlinear Bayesian Optimal Experimental Design”.
- Jiayuan Dong gave a presentation “Expert Elicitation and Opinion Aggregation for Bayesian Prior Construction with Application to the U.S. Steel Flow Analysis”.
- Jeremiah Hauth gave a presentation “Efficient Large-Scale Bayesian Inference for Predictive Modeling in Precision Health Balance Training”.
- Xun Huan gave a presentation “Goal-Oriented Optimal Experimental Design for Nonlinear Models using MCMC”.
- Chengyang Huang gave a presentation “Fast Approximate Bayesian Uncertainty Quantification Using Conditional Generative Adversarial Networks”.
- Aniket Jivani gave a presentation “Sobol’ Sensitivity for Uncertain Model Parameters in Simulations of Background Solar Wind”.
- Snehal Prabhudesai gave a presentation “Partially Bayesian Neural Networks: Low-Cost Bayesian Uncertainty Quantification for Deep Learning in Medical Image Segmentation”.
- Wanggang Shen gave a presentation “Optimal Bayesian Design of Sequential Experiments Using Deep Deterministic Policy Gradient”.
- Maria Han Veiga gave a presentation “Optimal Experimental Design Using Variational Inference Approximations”.
- Xun Huan co-organized a three-part minisymposium “Model-Based Optimal Experimental Design” (parts I, II, III).
- Upcoming:
- (2022/06) ISBA World Meeting
- (2022/07) Joint Statistical Meetings
- (2022/08) UQ for ML Integrated Physics Modeling
- (2022/09) SIAM MDS
- (2022/10) SES Annual Technical Meeting
We are grateful to all our current and past sponsors.
