I am a PhD student at Harvard University studying Health Policy and Decision Sciences. I am a National Insitute of Mental Health (NIMH) funded Predoctoral Research Fellow in Comparative Effectiveness Research for Suicide Prevention at Brigham and Women's Hospital
My research develops and applies methods in causal inference, Bayesian statistics, and reinforcement learning to improve decision-making in health and public policy. I work at the intersection of statistics, computer science, and health policy, with applications to problems in suicide prevention, emergency department operations, and precision medicine. Methodologically, I focus on causal estimation in complex settings and sequential decision-making under uncertainty, but I am broadly interested in statistical computing, machine learning, and reproducible research.
Aside from research, I am passionate about teaching. I have extensive experience teaching students in a variety of settings, including as a teaching fellow at Harvard, instructor at UChicago Medicine, and as a Teach For America corps member in New Haven Public Schools.