Prof. Elizaveta Rebrova
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG
- J. Lok and E. Rebrova. “Subspace-constrained randomized coordinate descent for linear systems with good low-rank matrix approximations.” SIAM Journal on Matrix Analysis and Applications 47(3), 1495–1529 (2026). DOI.
- A. Chaudhry and E. Rebrova. “Learning nonnegative matrix factorizations from compressed data.” SIAM Journal on Matrix Analysis and Applications 47(3), 1277–1303 (2026). DOI.
- C. Haselby, M. A. Iwen, D. Needell, E. Rebrova and W. Swartworth. “Fast and low-memory compressive sensing algorithms for low Tucker-rank tensor approximation from streamed measurements.” Numerical Algorithms 101, 2567–2629 (2026). DOI.
- M. Dereziński, D. LeJeune, D. Needell and E. Rebrova. “Fine-grained analysis and faster algorithms for iteratively solving linear systems.” Journal of Machine Learning Research 26(144), 1–49 (2025). Article.
- M. Dereziński and E. Rebrova. “Sharp analysis of sketch-and-project methods via a connection to randomized singular value decomposition.” SIAM Journal on Mathematics of Data Science 6(1), 127–153 (2024). DOI.
- J. Haddock, D. Needell, E. Rebrova and W. Swartworth. “Quantile-based iterative methods for corrupted systems of linear equations.” SIAM Journal on Matrix Analysis and Applications 43(2), 605–637 (2022). DOI.
- E. Rebrova and R. Vershynin. “Norms of random matrices: local and global problems.” Advances in Mathematics 324, 40–83 (2018). DOI.
See full list at Google Scholar https://scholar.google.com/citations?user=nZ27XjIAAAAJ&hl=en
Short Biography
I grew up in Moscow, Russia, and moved to the United States in 2013 to start my PhD. I studied in the Department of Functional Analysis at Moscow State University, graduating with a Specialist degree in Pure Mathematics. In 2018, I completed my PhD in Mathematics at the University of Michigan, working on random matrices and high-dimensional probability. I subsequently held positions in the Department of Mathematics at the University of California, Los Angeles (UCLA), the Computational Research Division at Lawrence Berkeley National Laboratory, and the Department of Operations Research and Financial Engineering at Princeton University, before joining the Mathematical Institute at the University of Oxford and New College in autumn 2026.
Current and Former PhD Students:
Sofiia Shvaiko, Department of ORFE, Princeton University, USA. Current.
Shambhavi Suryanarayanan, Department of ORFE, Princeton University, USA, 2026. “Randomized Iterative Methods for Tensor Estimation: Recovery, Factorization and Regression.”
Jackie Lok, Department of ORFE, Princeton University, USA, 2026. “Randomized Iterative Algorithms in Numerical Linear Algebra.”
Abraar Chaudhry, Department of ORFE, Princeton University, USA, 2024. “Algebraic Methods in Convex Geometry, Nonlinear Optimization, and Learning Dynamical Systems.” Co-supervised with Amir Ali Ahmadi.
Current and Former Master’s Students:
Nicolo Grometto, Department of ORFE, Princeton University, USA, 2023. “Essays on Random Matrices.” Master of Science in Engineering.
My research focuses on numerical linear algebra and mathematical data science. I develop randomized methods for solving linear systems and recovering low-rank structure in matrices and tensors, motivated by applications in data processing, machine learning, and scientific computing. I am particularly interested in making these methods efficient and reliable when datasets are large or observations are compressed or corrupted. I also study the interplay between optimization and machine learning, including how algorithmic choices influence convergence, regularization, and generalization. Across these directions, I combine probabilistic analysis and numerical methods to prove convergence and error bounds, exploring questions such as when matrix structure and random sampling can reduce computational and memory costs.