Dr. Rina Foygel Barber

Biography

Rina Foygel Barber is a Professor in the Department of Statistics at the University of Chicago. Her research revolves around the theoretical foundations of statistical problems including estimation, prediction, and inference under modern and classical settings. She focuses on reliable methodologies given the challenges posed by high dimensionality and model assumption failures. Her significant work includes distribution-free inference methods and conformal prediction, where she develops hardness results that determine the types of questions that cannot be solved with distribution-free methods. Additionally, she is interested in multiple testing methods, algorithmic stability, and shape-constrained inference, collaborating on modeling optimization problems related to image reconstruction and medical imaging. Rina began her tenure at the University of Chicago after serving as a NSF postdoctoral fellow from 2012 to 2013 under the supervision of Emmanuel Candès. She received her PhD in Statistics from the University of Chicago in 2012, advised by Mathias Drton and Nati Srebro, and prior to that, she earned her MS in Mathematics from the same institution in 2009. Before her graduate studies, Rina was a mathematics teacher at the Park School of Baltimore from 2005 to 2007 and holds a ScB in Mathematics from Brown University, completed in 2005.

Research Interests

Experience