Rate My Professor Pragya Sur

Sur's research focuses on high-dimensional statistics, statistical machine learning, overparametrized problems, learning under heterogeneity and distribution shifts, and causal inference in high dimensions. Her foundational contributions bridge classical asymptotic theory with high-dimensional regimes, providing rigorous analyses of algorithms such as logistic regression, least squares, and their regularized versions, as well as boosting and interpolated classifiers. Notable publications include "A modern maximum-likelihood theory for high-dimensional logistic regression" (Proceedings of the National Academy of Sciences, 2019, with E. J. Candès), "A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-L1-Norm Interpolated Classifiers" (The Annals of Statistics, 2022, with T. Liang), "The Asymptotic Distribution of the MLE in High-dimensional Logistic Models: Arbitrary Covariance" (Bernoulli, 2022, with Q. Zhao and E. J. Candès), and "Spectrum-Aware Debiasing: A Modern Inference Framework with Applications to Principal Components Regression" (The Annals of Statistics, 2025, with Y. Li). Sur has garnered major awards, including the 2026 Alfred P. Sloan Research Fellowship in Mathematics, the 2026 IMS Thelma and Marvin Zelen Emerging Women Leaders in Data Science Award, the NSF CAREER Award (award ID: 2440824) for "High-dimensional Learning and Inference from Heterogeneous Data Sources," NSF DMS Award (award ID: 2113426), and the 2026 ASA Noether Early Career Scholar Award. She served as President of the Institute of Mathematical Statistics New Researchers Group from 2022 to 2024 and as an International Strategy Forum Asia Fellow in 2023. Sur holds editorial positions as Associate Editor for the Journal of the Royal Statistical Society Series B and Statistical Science, and as Invited Guest Co-Editor for Statistical Science’s special issue on statistics and AI. Her scholarship influences theoretical and applied communities in modern data science.