I recently completed my PhD in Statistics from University of Washington, Seattle. I work on problems related to causal inference. My PhD advisor is Thomas S. Richardson. My doctoral thesis is on Likelihood analysis of causal models.
I am interested in addressing various statistical challenges posed by causal inference, such as inference under non-standard asymptotics, finite sample, irregularity, and partial identifiability. Recently, I have been working on the efficiency theory of causal graphical models. I am also interested in approaches that enhance efficiency and interpretability, such as Bayes and empirical Bayes.
Previously, I studied computer science at Duke and MIT.
|Sep 30, 2021||I will be the Richard M. Karp Research Fellow for the Causality program at the Simons Institute Jan–May, 2022.|
|Sep 10, 2021||Started as a Research Associate in Cambridge, UK.|
|Apr 30, 2021||Defended my PhD in statistics!|
|Feb 17, 2021||Package multChernoff computes finite-sample tail bounds of multinomial LRT.|
|Dec 3, 2020||Package InvariantCausal.jl upgraded to Julia 1.x.|