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 are grateful to all our current and past sponsors.