About me

Hello! I’m an assistant professor in Statistical Science at Duke. Previously, I was a postdoc in the math department at MIT and before that, I obtained my PhD in math at the Courant Institute at NYU.

My research is on asymptotic theory of high-dimensional Bayesian inference and on how the theory informs computational methods. I provide rigorous guarantees which characterize when and why classical approximation tools—such as Laplace’s method and Bernstein–von Mises theorems —remain accurate in modern high-dimensional regimes. These insights shed light on the design of scalable and reliable algorithms for modern data.

My most recent work exploits asymptotic techniques to design more efficient rare event sampling methods. I am also interested in high-dimensional large deviations.