Currently a postdoctoral associate in the Statistical Science Department at Duke University, working with Eric Laber and Simon Mak.
Education
Ph.D. in Computational Math from the University of Chicago, where he worked on statistical inverse problems under the guidance of Daniel Sanz-Alonso and stochastic approximation/optimization with Panos Toulis.
Background
Research interests broadly lie in developing computational/statistical tools for Bayesian/probabilistic modeling, various types of experimental designs, and sequential decision-making. Keen to utilize existing methodological tools to tackle scientific problems arising from domain sciences.