Maya Petersen MD, PhD
Chair, Division of Biostatistics
- Associate Professor Epidemiology and Biostatistics
Maya L. Petersen is an Associate Professor of Biostatistics and Epidemiology who focuses on the development and application of novel causal inference methods to problems in health.
Dr. Maya L. Petersen is an Associate Professor of Biostatistics and Epidemiology at the University of California, Berkeley. Dr. Petersen’s methodological research focuses on the development and application of novel causal inference methods to problems in health, with an emphasis on longitudinal data and adaptive treatment strategies (dynamic regimes), machine learning methods, and study design and analytic strategies for cluster randomized trials. She is a Founding Editor of the Journal of Causal Inference and serves on the editorial board of Epidemiology and Epidemiologic Methods.
Dr. Petersen’s applied work focuses on developing and evaluating improved HIV prevention and care strategies in resource-limited settings. She currently serves as co-PI (with Dr. Diane Havlir and Dr. Moses Kamya) for the Sustainable East Africa Research in Community Health (www.searchendaids.com) consortium, and as co-PI (with Dr. Elvin Geng) for the ADAPT-R study (a sequential multiple assignment randomized trial of behavioral interventions to optimize retention in HIV care).
- MD – University of California, San Francisco 2007-2009
- PhD – Biostatistics, University of California, Berkeley 2004-2007
- Pre-Doctoral Fellow – Howard Hughes Medical Institute 2001-2006
- MS – Health and Medical Sciences, University of California, Berkeley Joint Medical Program 1999-2002
- BA – Human Biology, Stanford University 1994-1998
- Causal inference
- Dynamic treatment regimes
- Antiretroviral resistance
- Impact evaluation
- Implementation science
- PB HLTH 252D: Introduction to causal inference (“Causal I”) (Spring)
- PB HLTH 252E: Advanced topics in causal inference (“Causal II”) (Fall
- PB HLTH 290: Causal Inference Seminar (every two years)
- Doctoral Seminar in Epidemiology (first-year students)