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?


  • (2022/12) Yummy food at Namaste Flavours!
  • (2022/11) Congratulations to Codie Kawaguchi, Shuyu Long, and Thomas Coons for successfully passing the Ph.D. Qualifying Exam!
  • (2022/05) End-of-semester celebration at the Grove!
  • (2022/02) A fun group dinner at Cafe Zola!

We are grateful to all our current and past sponsors.

US Dept Energy
precision health