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 focus is 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 clinical informatics.  

Dr. Rose's recent honors include an NIH Director's New Innovator Award to develop novel robust estimators for generalizability and the ISPOR Bernie J. O'Brien New Investigator Award for exceptional early career work in health economics and outcomes research. Her research has been featured in The New York Times, USA Today, Slate, and The Boston Globe. In 2011, she coauthored the first book on machine learning for causal inference, with a sequel text released in 2018. Dr. Rose has served on several editorial boards, including as Associate Editor for the Journal of the American Statistical Association, and is the incoming Co-Editor of Biostatistics. She is the current Secretary/Treasurer and 2019 Chair-Elect 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.