She received her Ph.D. 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. Broadly, Dr. Rose's methodological research focuses on nonparametric machine learning for prediction and causal inference. Within health policy, she works on risk adjustment, comparative effectiveness research, and health program impact evaluation. Dr. Rose coauthored the first book on machine learning for causal inference and co-leads the Health Policy Data Science Lab, where she directs projects in computational health economics and health outcomes research.