About

I am a fifth year PhD candidate in the Department of Statistical Science at Duke University, working with Jason Xu. Broadly speaking, I create methods for conducting Bayesian inference where constraints, or side information, impose structure on unknown parameters, and develop efficient Markov Chain Monte Carlo algorithms samplers for such problems. Prior to Duke, I completed my MS in the Department of Statistics at The University of Chicago, where I was advised by Lek-Heng Lim.

Outside of my PhD, my professional experience includes quant and consulting roles. I spent a few years before graduate school consulting in the financial services sector at Quantitative Risk Management, and since starting my PhD, I’ve interned in quantitative analytics and machine learning roles at Wells Fargo and JPMorganChase.