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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!

News

  • (2021/07) USNCCM
    • Jeremiah Hauth is giving a presentation “Variational Bayesian Inference for Convolutional Neural Networks in Precision Health Balance Training”.
    • Chengyang Huang is giving a presentation “Bayesian Inference via Conditional Generative Adversarial Networks”.
    • Aniket Jivani is giving a presentation “Uncertainty Quantification for Random Field Quantities Using Multifidelity Karhunen-Loeve Expansions”.
    • Wanggang Shen is giving a presentation “Sequential Optimal Experimental Design Using Reinforcement Learning with Policy Gradient”.
    • Xun Huan is co-organizing a minisymposium “Optimal Experimental Design in Computational Science and Engineering”.
  • (2021/07) SIAM Annual Meeting
  • (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!

Research Sponsors

We are grateful to all our current and past sponsors.

darpa
NSF
US Dept Energy
MISCE
MIDAS
precision health