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?
We have multiple openings for Postdoctoral Fellow!
News
- (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!
- (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.
- (2022/01) Xun Huan gave a presentation “Bayesian Recurrent Neural Networks for Monitoring Rotorcraft Icing from Aeroacoustics Time-Series Data” at the AIAA SciTech Forum [paper].
- (2021/12) Xun Huan gave a presentation “Bayesian Sequential Optimal Experimental Design for Nonlinear Systems via Policy Gradient” at the Applied Reinforcement Learning Seminar.
- (2021/12) AGU Fall Meeting
- Gabor Toth gave a presentation “Developing the Michigan Sun-to-Earth Model with Data Assimilation and Quantified Uncertainty”.
- Aniket Jivani presented a poster “Global Sensitivity Analysis for Solar-Wind Simulations in the Space Weather Modelling Framework”.
- Upcoming:
- (2022/01) Xun Huan is giving a presentation “Optimal Bayesian Design of Finitely Sequential Experiments with Deep Deterministic Policy Gradient” at the Royal Statistical Society (RSS) Computational Statistics and Machine Learning session on Automatic Experimentation.
Research Sponsors
We are grateful to all our current and past sponsors.
