Research

My research lies broadly at the interface of mathematics and data science. I study the properties and behaviour of random mathematical structures and algorithms, and use these insights to develop tools and models that are supported by theoretical guarantees and allow us to better work with and understand large-scale, complex data.

My main interests are in high-dimensional and discrete probability, random matrices, randomised algorithms, and numerical linear algebra. I am also interested in the applications of these areas to statistics, computer science, and machine learning.

Papers

• A subspace constrained randomized Kaczmarz method for structure or external knowledge exploitation. Jackie Lok and Elizaveta Rebrova. Preprint, 2023. [arXiv]

Miscellaneous

• Markov chains, mixing times, and cutoff (Honours thesis). [pdf]