Bio
Sherri Rose, Ph.D. is a Professor of Health Policy and, by courtesy, of Computer Science at Stanford University, where she is Director of the Health Policy Data Science Lab. Her research is centered on developing and integrating innovative statistical machine learning approaches to improve human health. Within health policy, Dr. Rose works on 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.
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 (ASA) 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 received both the ASHEcon Willard G. Manning Memorial Award for Best Research in Health Econometrics and the ASA Outstanding Statistical Application Award. She was recently awarded the Open Science Champion Prize by Stanford University. 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.
