About me

I am an Assistant Professor in the Statistics Department at UC Berkeley. My research aims at understanding causal relationships using large administrative datasets from the medical and social sciences. I develop new ways to form compelling matched or weighted comparison groups in these datasets using tools from optimization. I also study methods for transparent and interpretable inference about causal effects when unobserved confounding variables may be present. I am broadly interested in substantive problems relating to people and institutions, and my methodological work is motivated by collaborations to address such problems in health services research, epidemiology, and education.

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Curriculum Vitae

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EDUCATION

PhD, Statistics, University of Pennsylvania, May 2017.

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

SELECTED AWARDS

NSF CAREER award, 2022.

Hellman Family Fellowship, 2021.

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.

SELECTED TEACHING EXPERIENCE

Graduate:

Statistics 230A, Linear Models, University of California, Berkeley, Spring 2023.

Statistics 232, Experimental Design, University of California, Berkeley, Spring 2018, Spring 2022.

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

Undergraduate:

Statistics 151A, Linear Modeling: Theory and Applications, University of California, Berkeley, Spring/Fall 2020, Fall 2021.

Statistics 158, The Design and Analysis of Experiments, University of California, Berkeley, Spring 2019, Spring 2021.