Assistant Professor of Statistical Science
My research targets the development of interpretable Bayesian learning models (with supporting theory and algorithms) that integrate broad types of scientific knowledge, and methods that leverage such models to guide cost-efficient scientific decision-making with expensive experiments. My research is motivated by ongoing interdisciplinary collaborations in high-energy and nuclear physics, aerospace engineering, bioengineering and public policy. Current research interests include Bayesian nonparametrics, Bayesian optimization, big data analysis, probabilistic machine learning, scientific computing, and uncertainty quantification.