Postdoctoral Fellow
@ BHW and HMS
I am currently a postdoctoral fellow at Brigham and Women's Hospital and Harvard Medical School, focusing on developing predictive models using electronic health records for diabetes treatment outcomes.
My research lies at the intersection of machine learning, causal inference, and health data science. I develop and apply machine (deep) learning models to address challenges in complex, high-dimensional longitudinal health data.
I earned a Ph.D. in Political Science with a Designated Emphasis in Computational Science and Engineering from UC Berkeley. My broader research agenda includes extending causal inference methods for multi-valued treatments, developing Bayesian frameworks for adversarial machine learning, applying recurrent neural networks to panel data for causal impact estimation, and evaluating deep learning approaches for missing data imputation in survey data.
I am open to exploring research scientist roles in tech or health sciences, where I can apply my expertise in machine learning and causal inference to solve complex problems and contribute to knowledge advancement in these fields.
Revisiting Diabetes Risk of Olanzapine versus Aripiprazole for Serious Mental Illness Care.
(with Denis Agniel, Sharon-Lise Normand, John Newcomer, Katya Zelevinsky, Jeannette Tsuei, and Marcela Horvitz-Lennon).
BJPsych Open, 2024.
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction.
Statistics and Public Policy, 2024.
[arXiv] [Code]
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). Statistics in Medicine, 2024.
[arXiv] [Code]
Antipsychotics and the Risk of Diabetes and Death among Adults with Serious Mental Illnesses.
(with Sharon-Lise Normand, Katya Zelevinsky, John Newcomer, Denis Agniel, Haley Abing, and Marcela Horvitz-Lennon).
Psychological Medicine, 2023.
[Code]
Adversarial Machine Learning: Bayesian Perspectives.
(with David Rios Insua, Roi Naveiro, and Víctor Gallego).
Journal of the American Statistical Association, 2023.
[arXiv] [Code]
Gender Gaps in Frontier Entrepreneurship? Evidence from 1901 Oklahoma Land Lottery Winners.
Journal of Historical Political Economy, 2023.
[arXiv] [Code]
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.
[arXiv] [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 (C), 2021.
[arXiv] [Code]
Amnesty Policy and Elite Persistence in the Postbellum South: Evidence from a Regression Discontinuity Design.
Journal of Historical Political Economy, 2021.
[arXiv] [Code]
Character-Based Handwritten Text Transcription with Attention Networks.
(with Rafael Valle). Neural Computing & Applications, 2021.
[arXiv] [Code]
Estimating Population Average Treatment Effects from Experiments with Noncompliance.
(with Kellie Ottoboni). Journal of Causal Inference, 2020.
[arXiv] [Code]
Land Lotteries, Long-Term Wealth, and Political Selection. Public Choice, 2019.
[Code]
Missing Data Imputation for Supervised Learning.
(with Rafael Valle).
Applied Artificial Intelligence, 2018.
[arXiv] [Supplementary Material] [Code]