My research focuses on nonparametric causal methods motivated by real-world policy issues. These methods lean on developments in Machine Learning to create flexible and robust estimates of causal effects. My goal is to provide practitioners across a variety of fields with the most robust possible estimates of the impacts of proposed policy changes.

I am currently a postdoc at the UC Berkeley School of Information under Joshua Blumenstock. There, I study the welfare effects of mobile banking. We are developing multivariate nonparametric methods to study ill-defined concepts like welfare in the presence of missingness.

In July 2018, I defended my dissertation for my PhD in Statistics, joint with Public Policy, at Carnegie Mellon University. I studied under Edward Kennedy, developing nonparametric causal inference tools to learn about policies to reduce recidivism in Pennsylvania prisons.

Before grad school, I worked as a Research Assistant at RAND. There, my team provided recommendations to the Air Force about the effects and sources of stress for the ICBM force. I also worked on a team which developed a nationwide survey for victims of crime and with the LAFD to improve their hiring practices.

I graduated from Barnard College with a BA in Economics in 2010, and earned my MA from Columbia University in Quantitative Methods in Social Sciences in 2011.