Elizaveta Rebrova

Elizaveta Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University. She is also associated with the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML).

• Education: Specialist degree from Moscow State University (2012), PhD in Mathematics from University of Michigan (2018 under Roman Vershynin)
• Prior Appointments: Postdoctoral Scholar at Lawrence Berkeley National Lab (2021), Assistant Adjunct Professor at UCLA Mathematics Department (2018-2021)

Her research focuses on randomized numerical linear algebra, mathematics of data science, and high-dimensional probability. Key interests include developing algorithms for large-scale data with non-trivial structure, robust and interpretable learning, and stochastic optimization. Her recent work analyzes algorithmic convergence in structured settings, tensor-based data compression, and nonnegative matrix/tensor factorization under constraints.

Recent publications span topics in randomized NLA, robust solvers, tensor methods, and nonnegative matrix factorization. Notable trends include improving convergence rates for iterative methods, handling adversarial noise in linear systems, and leveraging tensor structures for efficient data recovery.

She supervises Ph.D. students including Jackie Lok, Shambhavi Suryanarayanan, and Sofiia Shvaiko. Her teaching at Princeton covers graduate probability theory (ORF526), convex optimization (ORF523), and network science (ORF387), with prior teaching roles at UCLA and University of Michigan.