About

I recently completed a Ph.D. in the Department of Statistical Science at Duke University under the supervision of Jason Xu. Broadly speaking, my doctoral research focused on the interplay between methods in both Bayesian and frequentist statistical inference and the underlying geometry of the parameter space. A large portion of my research focused on constructing methods and developing efficient Markov Chain Monte Carlo samplers for conducting Bayesian inference in settings where externally-imposed constraints induce structure on the unknown parameters. 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, machine learning, and quantitative finance roles at Wells Fargo, JPMorganChase, and Morgan Stanley, respectively.