Research
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.
Working Papers
Machine Learning to Personalize Psychotherapy Selection for Patients at Elevated Suicide Risk in a Large National Cohort
Under Review
Effectiveness of Extreme Risk Protection Orders in Reducing Firearm Deaths: A Bayesian Analysis of State-Level Implementation, 1999–2023
Under Revision
Publications
Sex Differences in Life-Course Suicide Rates by State Firearm Policy Environment
American Journal of Preventive Medicine, 2025
A National Study of Exposure to Prescription Medications with Evidence-Based Pharmacogenomic Information
Clinical Pharmacology & Therapeutics, 2025
Variation in Batch Ordering of Imaging Tests in the Emergency Department and the Impact on Care Delivery
Health Services Research, 2025
Male Gender Expressivity and Diagnosis and Treatment of Cardiovascular Disease Risks in Men
JAMA Network Open, 2024
Adolescent School Social Networks, Gender Norms, and Changes in Male Gender Expression With Young Adult Substance Use
Journal of Adolescent Health, 2024
Does the Doctor-Patient Relationship Affect Enrollment in Clinical Research?
Academic Medicine, 2023