My research focuses on identifying the optimal strategies for cancer prevention, detection, and treatment. Many important decisions about cancer control must be made in the absence of evidence from randomized trials, which may be impractical or too lengthy to provide a timely answer. In these cases, I apply advanced causal inference methods to large observational databases (e.g., electronic health records) to provide the best available evidence to inform clinical decision-making and future trial design. I am particularly interested in the intersection between causal inference and machine learning. I teach clinical data science at the Harvard Medical School and causal inference methodology at the Harvard T.H. Chan School of Public Health.