Ryan Tibshirani

Ryan Tibshirani is a Professor of Statistics at the University of California, Berkeley, and Principal Investigator in the Delphi group. Previously, he was faculty at Carnegie Mellon University (2011–2022). He holds a Ph.D. in Statistics (2011) and a B.S. in Mathematics (2007) from Stanford University. His research focuses on high-dimensional statistics, nonparametric methods, distribution-free inference, and machine learning, with applied work in computational epidemiology, particularly tracking and forecasting epidemics like influenza and COVID-19. He has contributed to open-source tools like the conformalInference R package for predictive inference.

Education: Ph.D. in Statistics, Stanford University (2011); B.S. in Mathematics, Stanford University (2007). Professional service includes Editor-in-Chief roles for Foundations and Trends in Machine Learning and Statistics, and membership in the Institute of Mathematical Statistics Council.

Research interests span theoretical and applied statistics, including convex optimization, numerical methods, and collaborative efforts in public health forecasting. His work on the Delphi group has advanced real-time epidemic monitoring via sensor fusion and probabilistic forecasting. Recent projects address challenges in estimating time-varying epidemic severity and improving predictive model calibration under distribution shifts.

Key contributions include unifying conformal prediction theories, developing trend filtering methods for lattice data, and advancing understanding of cross-validation in overparameterized models. His software tools emphasize reproducibility and scalability for large-scale statistical tasks.