BIOGRAPHY / CV

Sherri Rose, Ph.D. is a Professor of Health Policy and 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 ethical algorithms in health care, risk adjustment, chronic kidney disease, 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 currently teaches the course Methods for Reproducible Population Health and Clinical Research.

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 is a Fellow of the American Statistical Association and received the Mortimer Spiegelman Award, which recognizes the statistician under age 40 who has made the most significant contributions to public health statistics. In 2024, she was recognized with both the ASHEcon Willard G. Manning Memorial Award for Best Research in Health Econometrics and the American Statistical Association Outstanding Statistical Application Award. Her research has been featured in The New York Times, USA Today, and The Boston Globe. She was Co-Editor-in-Chief of the journal Biostatistics from 2019-2023.

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.