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.
- (2022/06) Beckett Zhou is giving a presentation “A Data-Driven Approach for Enhancement of Propeller Performance Prediction” at the AIAA/CEAS Aeroacoustics Conference. [paper]
- (2022/06) Aniket Jivani presented a poster “Global Sensitivity Analysis for Solar Wind Simulations in the Space Weather Modeling Framework” at the SHINE 2022 Workshop.
- (2022/06) Xun Huan presented a poster “Goal-Oriented Optimal Experimental Design for Nonlinear Systems” at the ISBA World Meeting.
- (2022/08) Xun Huan is giving a presentation “Multifidelity Karhunen-Loeve Expansion Surrogate Models for Uncertainty Propagation” at the Joint Statistical Meetings.
- Upcoming:
- (2022/08) UQ for ML Integrated Physics Modeling
- (2022/09) SIAM MDS
- (2022/10) SES Annual Technical Meeting
- (2023/02) SIAM CSE
We are grateful to all our current and past sponsors.
