Sherri Rose, Ph.D. is an Associate Professor in the Department of Health Care Policy at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Broadly, Dr. Rose's methodological research focuses on nonparametric machine learning for causal inference and prediction. Within health policy, Dr. Rose works on risk adjustment, comparative effectiveness research, and health program impact evaluation. She co-leads the Health Policy Data Science Lab where she directs projects in computational health economics and health outcomes research.  

Dr. Rose's recent honors include an NIH Director's New Innovator Award to develop novel robust estimators for generalizability. Her research has been featured in The New York Times, USA Today, Slate, and The Boston Globe. In 2011, Dr. Rose coauthored the book Targeted Learning: Causal Inference for Observational and Experimental Data published by the Springer Series in Statistics. She also serves on several editorial boards, including as Associate Editor for the Journal of the American Statistical Association and Biostatistics, and is the current Secretary/Treasurer of the American Statistical Association Biometrics Section.

Dr. Rose received her Ph.D. in Biostatistics from the University of California, Berkeley and a B.S. in Statistics from The George Washington University before completing an NSF Mathematical Sciences Postdoctoral Research Fellowship at Johns Hopkins University.