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Elizaveta Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering at Princeton University. She specializes in high-dimensional probability, randomized algorithms, numerical linear algebra, matrix tensor methods, and robust interpretable learning. Her expertise also extends to non-asymptotic random matrix theory, contributing to advanced research and methodologies in her field. With a strong focus on mathematical data applications, she aims to enhance the understanding and implementation of statistical models. Rebrova's work is recognized for its rigor and relevance in addressing complex problems in statistics and probability, making significant contributions to both theoretical and applied domains.
GRE scores are not accepted. Ph.D. is the primary degree; students are not required to hold an M.S.E. prior to admission.