Sherri Rose

Dr. Sherri Rose is Professor of Health Policy and Director of the Health Policy Data Science Lab at Stanford University. She develops statistical machine learning methodologies to improve health equity, focusing on ethical algorithms, risk adjustment systems, chronic kidney disease, and health program evaluation. Her research has been published in leading journals including NEJM, JAMA, and Journal of Health Economics.

Education includes a PhD in Biostatistics from UC Berkeley and NSF Postdoctoral Fellowship at Johns Hopkins University. She teaches courses on reproducible research methods and mentors graduate students across health policy, computer science, and biomedical data science.

Research spans algorithmic fairness in healthcare, causal inference methods, and health policy evaluation. Recent work examines bias mitigation in clinical algorithms, generalized methods for Medicaid analyses, and ethical frameworks for medical AI. Her publications demonstrate consistent innovation in statistical methodology applied to critical healthcare challenges.

Awards and Honors:

• NIH Director's Pioneer Award (2022-2027)
• ASA Outstanding Statistical Application Award (2024)
• ASHEcon Willard G. Manning Memorial Award (2024)
• Mortimer Spiegelman Award (2021)
• NIH Director's New Innovator Award (2017-2022)
• Fellow, American Statistical Association (2020)

Directs the Health Policy Data Science Lab with 10+ doctoral students and postdoctoral scholars. Current research includes NSF and NIH-funded projects on algorithmic fairness, causal inference generalization, and health policy simulation modeling.