Research achievements have been applied to areas such as groundwater flow, nuclear physics, and carbon fiber composites in manufacturing.
Research Experience
The research experience focuses on developing innovative numerical methods for efficiently quantifying uncertainty; applying these techniques to tackle data-driven, large-scale problems typically modeled in the form of differential equations; also focusing on designing and analyzing efficient numerical techniques for high- or infinite-dimensional Bayesian inverse problems, especially those constrained by differential equations.
Background
Research interests include Uncertainty Quantification, Inverse Problems and Bayesian Inference, Multiscale Methods.