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 an opening for Postdoctoral Fellow!
- (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!
- (2021/03) Congratulations to Aniket Jivani for successfully passing the Ph.D. Qualifying Exam!
- (2021/03) Congratulations to Codie Kawaguchi for winning the NSF Graduate Research Fellowships Program (GRFP) award!
- (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.
- (2021/07) Our paper “Digital Twin Concepts with Uncertainty for Nuclear Power Applications” is published in Energies (special issue Expanding Nuclear Applications and Technologies for a Clean Energy Future).
- (2021/07) Our paper “Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units” is published in Sensors (special issue Wearable Sensors for Gait and Falls Monitoring).
- (2021/10) Xun Huan gave a presentation “Bayesian Optimal Experimental Design for Batch and Sequential Experiments” at the Dartmouth College Applied and Computational Mathematics Seminar.
- (2021/08) Xun Huan gave a presentation “Closed-Loop Bayesian Design of Sequential Experiments via Dynamic Programming and Reinforcement Learning” at the IFIP TC7 Conference on System Modelling and Optimization.
- (2021/07) USNCCM
- Jeremiah Hauth gave a presentation “Variational Bayesian Inference for Convolutional Neural Networks in Precision Health Balance Training”.
- Chengyang Huang gave a presentation “Bayesian Inference via Conditional Generative Adversarial Networks”.
- Aniket Jivani gave a presentation “Uncertainty Quantification for Random Field Quantities Using Multifidelity Karhunen-Loeve Expansions”.
- Wanggang Shen gave a presentation “Sequential Optimal Experimental Design Using Reinforcement Learning with Policy Gradient”.
- Xun Huan co-organized a minisymposium “Optimal Experimental Design in Computational Science and Engineering”.
We are grateful to all our current and past sponsors.