My research focuses on the interplay between information and political preference based on game theory and behavioral political economy. On the empirical front, I use machine (reinforcement) learning methods for implementing adaptive experiments and various causal inference methods to analyze survey experiments and observational data on the US Congress.
During graduate study, I worked on data science problems at Netflix. I received my masters in Statistics from the Department of Statistics at Stanford in 2023, where I focused on causal inference methods for observational panel data, experimental design, and stochastic processes.
Prior to joining GSB, I worked at Analysis Group. I graduated from Middlebury College in 2016 with a B.A in economics and a minor in political science.