Rina Foygel Barber (born 1982 or 1983) is an American statistician whose research includes works on the Bayesian statistics of graphical models, false discovery rates, and regularization.
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.
[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".
[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.