Sherri Rose, PhD, is an Associate Professor at Stanford University in the Center for Health Policy and Center for Primary Care and Outcomes Research. She is also Co-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, Rose works on risk adjustment, 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. Rose is the Co-Editor of Biostatistics and Chair of the American Statistical Association’s Biometrics Section.
Her honors include an 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 and Penn-Rutgers Center for Causal Inference. Rose was also named a Fellow of the American Statistical Association in 2020. Her research has been featured in The New York Times, USA Today, and The Boston Globe. In 2011, Rose coauthored the first book on machine learning for causal inference, with a sequel text released in 2018.
Rose received her PhD 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, she was on the faculty at Harvard Medical School in the Department of Health Care Policy.
Rose 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 summer research programs for undergraduate students from underrepresented backgrounds, among other initiatives.