Rina F. Barber

Biosketch

Rina Foygel Barber is the Louis Block Professor of Statistics at the University of Chicago, where she has been faculty since Jan. 2014. Prior to joining the faculty, she was a NSF postdoctoral fellow at Stanford University. She received a Sc.B. in Mathematics at Brown University in 2005, a M.S. in Mathematics from University of Chicago in 2009, and a Ph.D. in Statistics from University of Chicago in 2012. Rina’s research focuses on developing theory and methodology for statistical problems in challenging modern settings, including distribution-free inference, selective inference and multiple testing, algorithmic stability, and sparse and low-rank estimation. Her research has been recognized by awards including the COPSS Presidents’ Award in 2020, an IMS Medallion Award and Lecture in 2022, and a MacArthur Fellowship in 2023. She is a member of the U.S. National Academy of Sciences, and a fellow of the Institute of Mathematical Statistics (IMS).

Research Interests

Rina's research focuses on the theoretical foundations of statistical problems in estimation, prediction, and inference. Her interests lie in modern settings where classical methods may not be reliable due to high dimensionality, failure of model assumptions, or other challenges. Her recent work focuses on distribution-free inference methods such as conformal prediction, and on developing hardness results to establish what types of inference questions can or cannot be solved with distribution-free methods. Her research has also focused on studying and developing multiple testing methods, including the knockoff filter for variable selection with false discovery rate control, and on questions in selective inference. She also collaborates with researchers in medical imaging on developing algorithms for image reconstruction.