Sherri Rose, Ph.D. is an Associate Professor in the Department of Health Care Policy at Harvard Medical School and Co-Director of the Health Policy Data Science Lab. Broadly, her methodological research focuses on statistical machine learning. Within health policy, Dr. Rose currently works on fairness for risk adjustment formulas and the generalizability of computational health economics algorithms. She has published interdisciplinary projects across varied outlets, including Biometrics, Journal of the American Statistical Association, Journal of Health Economics, Health Affairs, and New England Journal of Medicine. Dr. Rose is the Co-Editor of Biostatistics and Chair-Elect of the American Statistical Association’s Biometrics Section.

Recent honors include an NIH Director's New Innovator Award to develop novel estimators for generalizability, the ISPOR Bernie J. O'Brien New Investigator Award for exceptional early career work in health economics and outcomes research, and a Harvard Medical School Excellence in Mentoring Award. Her research has been featured in The New York Times, USA Today, and The Boston Globe. In 2011, Dr. Rose coauthored the first book on machine learning for causal inference, with a sequel text released in 2018.

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. 

Dr. Rose comes from a low-income background and is committed to increasing diversity in the mathematical and health sciences. She has been a faculty mentor in the Math Alliance’s Facilitated Graduate Applications Program and two Harvard summer research programs for undergraduate students from underrepresented backgrounds, among other initiatives.