About me

I am an Assistant Professor in the Statistics Department at UC Berkeley. My research centers on methodology for causal inference in observational studies. I develop new ways to form matched comparison groups in large observational datasets using approaches from discrete optimization. These tools allow transparent and interpretable inferences about the effects of interventions, and provide opportunities to study the impact of potential unobserved confounding variables. I am also interested in applying these methods in health services research, public policy, and the social sciences.


Curriculum Vitae

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PhD, Statistics, University of Pennsylvania, May 2017.

BS, Mathematical & Computational Science, Stanford University, 2012.


J. Parker Bursk Memorial Prize for Excellence in Research, Dept. of Statistics, The Wharton School, University of Pennsylvania, 2016

Thomas R. Ten Have Award, Atlantic Causal Inference Conference, 2016. Recognizes "exceptionally creative or skillful research in causal inference."

National Defense Science and Engineering Graduate (NDSEG) Fellowship, 2013-2016.


Statistics 151A, Linear Modeling: Theory and Applications, University of California, Berkeley, Spring 2020.

Statistics 158, The Design and Analysis of Experiments, University of California, Berkeley, Spring 2019.

Statistics 232, Experimental Design, University of California, Berkeley, Spring 2018.

Statistics 260, Observational Study Design and Causal Inference, University of California, Berkeley, Spring 2018.