Rina Foygel was born in Odesa, Ukraine.[3]
She attended Brown University, receiving a Bachelor of Science in mathematics in 2005.[1] She taught mathematics at the Park School of Baltimore from 2005 to 2007.[1] She completed her Masters and received her Ph.D. from the University of Chicago in 2012. Her dissertation, Prediction and model selection for high-dimensional data with sparse or low-rank structure, was jointly supervised by Mathias Drton and Nathan Srebro.[4] After postdoctoral research at Stanford University with Emmanuel Candès, she returned to the University of Chicago as a faculty member.[5]
Recognition
Barber won a Sloan Research Fellowship in 2016.[6] In 2017 the Institute of Mathematical Statistics gave her their Tweedie New Researcher Award "for groundbreaking contributions in high-dimensional statistics, including the identifiability of graphical models, low-rank matrix estimation, and false discovery rate theory ... [and] development of the knockoff filter for controlled variable selection".[5]
She was elected as a Fellow of the Institute of Mathematical Statistics in 2023, for "groundbreaking contributions to selective inference including the development of the knockoff filter", "groundbreaking contributions to model-free predictive inference including the jackknife+ and adapting conformal inference to covariate shifts", and "being a role model in every possible way as a lecturer, communicator, and research adviser to students and younger researchers".[7]
Also in 2023, she was awarded a MacArthur Fellowship, for "Developing tools to reduce false positives and improve confidence in high-dimensional data models." The MacArthur Foundation particularly cited the development of knockoff filtering and jackknife+, writing that "Barber’s innovative work at the intersection of statistics, machine learning, and data science is critical to overcoming the challenges presented by use of high-dimensional datasets."[8]
