I am currently an assistant professor of epidemiology at Boston University School of Public Health. My research is on causal inference methodology for improving evidence-based decision-making by patients, clinicians, and policy makers. I use novel statistical methods to answer comparative effectiveness questions for complex and time-varying treatments using observational data and randomized trials when available, and individual-level simulation modeling when insufficient data exist in the time frame required for decision-making. I am currently applying these methods to a variety of medical conditions including HIV progression, cancer, psychiatric conditions, and cardiovascular disease. I was previously a postdoctoral research fellow in Epidemiology at the Harvard T.H. Chan School of Public Health, working on causal inference for comparative effectiveness and real-world evidence in the HSPH Program on Causal Inference. I have an ScD in Epidemiology and MSc in Biostatistics from Harvard, an MPH in Epidemiology from Columbia Mailman School of Public Health, and a BSc in Biology from McGill University.
- Assessing Knowledge, Attitudes, and Practices towards Causal Directed Acyclic Graphs among Epidemiologists and Medical Researchers: a qualitative research project
- Guidelines for estimating causal effects in pragmatic randomized trials
- A Note on Estimating Optimal Dynamic Treatment Strategies Under Resource Constraints Using Dynamic Marginal Structural Models
- Use of directed acyclic graphs (DAGs) in applied health research: review and recommendations