Bio
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.
Her honors include an NIH Director's Pioneer Award, NIH Director's New Innovator Award, the ISPOR Bernie J. O'Brien New Investigator Award, and Mid-Career Awards from the American Statistical Association’s Health Policy Statistics Section, Washington Statistical Society/RTI-International, and Penn-Rutgers Center for Causal Inference. Dr. Rose was named a Fellow of the American Statistical Association in 2020 and she 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.
