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Sherri Rose, Ph.D. is an associate professor at Stanford University in the Center for Health Policy and Center for Primary Care and Outcomes Research as well as Co-Director of the joint Harvard–Stanford Health Policy Data Science Lab. A renowned expert in machine learning methodology for causal inference and prediction, her applied work has focused on risk adjustment, algorithmic fairness, health program evaluation, and comparative effectiveness research. Dr. Rose’s leadership positions include current roles as Co-Editor of  Biostatistics  and Chair of the American Statistical Association’s Biometrics Section. She is also a Fellow of the American Statistical Association. Dr. Rose earned a BS in Statistics from The George Washington University and a PhD in Biostatistics from the University of California, Berkeley 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. Below, an interview of Dr. Rose, conducted by her colleague, Dr. Laura Hatfield, on the occasion of her 2020 Mid-Career Award from the Health Policy Statistics Section (HPSS) of the American Statistical Association. This award recognizes leaders in health care policy and health services research who have made outstanding contributions through methodological or applied work and who show a promise of continued excellence at the frontier of statistical practice that advances the aims of HPSS.

A conversation with Sherri Rose, winner of the 2020 health policy statistics section mid-career award