Pragya Sur is an Assistant Professor in the Statistics Department at Harvard University, where she develops cutting-edge statistical theory and methodology for solving big data problems arising across varied domains. Specifically, she works on machine learning/artificial intelligence based methods, and brings in insights from modern machine learning and statistics to develop rigorous solutions to contemporary real-life problems. Examples include learning signal-to-noise ratios from enormous datasets arising in genomics, developing robust prediction models for deployment in imaging problems, teasing apart causal relations from associations in big data, and developing fair algorithms using grounded statistical thinking.
She is currently recipient of a National Science Foundation Division of Mathematical Sciences award and a William F. Milton Fund award. During 2019-2020, she was a Postdoctoral Fellow at the Center for Research on Computation and Society at the Harvard John A. Paulson School of Engineering and Applied Sciences. She completed a Ph.D. in Statistics in 2019 from Stanford University, where she received the Ric Weiland Graduate Fellowship (2017-2019) and the 2019 Theodore W. Anderson Theory of Statistics Dissertation Award for her “deep, original results in large sample maximum likelihood theory for logistic regression with a large numbers of covariates.”