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Elizaveta Rebrova is an Assistant Professor in Operations Research and Financial Engineering at Princeton University. Her research interests encompass high-dimensional probability, randomized algorithms, numerical linear algebra, and matrix tensor methods. She is also involved in robust interpretable learning and non-asymptotic random matrix theory. With a commitment to advancing the field, her work integrates sophisticated mathematical concepts with practical applications in data analysis and interpretation. Elizaveta's approach is characterized by a focus on developing reliable methods that enhance understanding and decision-making across various domains, contributing significantly to both theoretical foundations and practical implementations in operations research and financial engineering.
GRE scores are not accepted. Ph.D. is the primary degree; students are not required to hold an M.S.E. prior to admission.