I'm a Postdoctoral Fellow in Data Science in the Department of Health Care Policy at Harvard Medical School.
After receiving my PhD from UC Berkeley in 2019, I held a joint postdoctoral appointment in the Department of Statistical Science at Duke University and the Statistical and Applied Mathematical Sciences Institute (SAMSI), where I participated in the Causal Inference and Deep Learning programs.
My research focuses on leveraging machine (deep) learning for improving causal inference in observational studies in the social sciences and health data sciences.
I'm on the 2022-23 academic job market and interested in research scientist roles in tech or the health sciences.
Are Deep Learning Models Superior for Missing Data Imputation in Surveys? Evidence from an Empirical Comparison.
(with Zhenhua Wang, Olanrewaju Akande, and Fan Li). Survey Methodology, 2022.
[Code and Supplementary Material]
RNN-Based Counterfactual Prediction, with an Application to Homestead Policy and Public Schooling.
(with Shuxi Zeng). Journal of the Royal Statistical Society, 2021.
Amnesty Policy and Elite Persistence in the Postbellum South: Evidence from a Regression Discontinuity Design.
Journal of Historical Political Economy, 2021.
Character-Based Handwritten Text Transcription with Attention Networks.
(with Rafael Valle). Neural Computing & Applications, 2021.
Estimating Population Average Treatment Effects from Experiments with Noncompliance.
(with Kellie Ottoboni). Journal of Causal Inference, 2020.
Land Lotteries, Long-Term Wealth, and Political Selection. Public Choice, 2019.
Adversarial Machine Learning: Bayesian Perspectives.
(with David Rios Insua, Roi Naveiro, and Víctor Gallego).
Targeted Learning in Observational Studies with Multi-Level Treatments: An Evaluation of Antipsychotic Drug Treatment Safety.
(with Marcela Horvitz-Lennon, Katya Zelevinsky, Thomas Huijskens, Pooja Tyagi, Jiaju Yan, Jordi Diaz, Tudor Cristea-Platon, and Sharon-Lise Normand).