Sherri Rose, Ph.D. is a Professor of Health Policy and Co-Director of the Health Policy Data Science Lab at Stanford University. Her research is centered on developing and integrating innovative statistical machine learning approaches to improve human health and health equity. Within health policy, Dr. Rose works on risk adjustment, ethical algorithms in health care, comparative effectiveness research, and health program evaluation. 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. In 2011, Dr. Rose coauthored the first book on machine learning for causal inference, with a sequel text released in 2018. She has been Co-Editor-in-Chief of the journal Biostatistics since 2019.
Dr. Rose has been honored with an NIH Director’s Pioneer Award, NIH Director's New Innovator Award, the ISPOR Bernie J. O'Brien New Investigator Award, and multiple mid-career awards, including the Gertrude M. Cox Award. She was named a Fellow of the American Statistical Association in 2020 and received the 2021 Mortimer Spiegelman Award, which recognizes the statistician under age 40 who has made the most significant contributions to public health statistics. Her research has been featured in The New York Times, USA Today, and The Boston Globe.
She 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. Prior to joining the faculty at Stanford University, Dr. Rose was on the faculty at Harvard Medical School in the Department of Health Care Policy.