Research in my group usually falls at the interface of causal inference, causal machine learning, applied semi-parametric theory, model-agnostic inference, and statistical machine learning. Our methodological research is most often inspired directly by problems identified in applied science collaborations with biomedical and public health scientists, usually in clinical trials or observational studies of infectious diseases, chronic diseases, and cancer, or in comparative effectiveness studies and implementation science in these same substantive areas. We are often open to working in new domain areas—wherever the application of rigorous statistical techniques and tools is welcome.

current group members

friends of the group

Current group members, the most up-to-date calendar of our lab meetings is posted here.

joining the lab

If you will soon be graduating with a PhD in statistics, biostatistics or computer science (machine learning) and are interested in working with us, consider applying to join the group as a postdoctoral research fellow. We occasionally have openings. To succeed in our group you need to have a strong background in causal inference and/or statistical machine learning as well as ample experience in statistical data analysis and statistical/numerical programming.

If you are applying to graduate school and are interested in joining our group eventually, you must apply and gain admission to the Biostatistics PhD program at the Harvard Chan School. Students in this program usually select an advisor after their first, or occasionally in their second, year. Please note that admissions decisions for this PhD program are made by committee—individual faculty members cannot directly admit prospective students and contacting individual faculty members in advance has no impact whatsoever on the outcomes of the admissions process.