I have recently joined the Mathematical Institute at Oxford as a Hooke Research fellow, following the completion of my Ph.D. in applied mathematics at Tel Aviv University. During my Ph.D., I also had the opportunity to be a summer research intern in the Mathematics of AI group at IBM Research. Prior to that, I pursued both my Master’s and Bachelor’s degrees in applied mathematics at Tel Aviv University. My Bachelor’s degree was part of a specialized program for high school students passionate about mathematics.


I have taught a diverse range of mathematics courses, spanning from core subjects, like linear algebra, to those with a more physically engineered focus, such as partial differential equations and numerical analysis. At Oxford, I teach opinion courses in applied mathematics with a strong emphasis on data science applications.


My research interests are broadly in the field of numerical linear algebra, focused on its applications to a wide spectrum of challenges in machine learning. Leveraging the powerful tools from numerical linear algebra, my work aims to introduce novel and interpretable insights to tackle learning tasks, ultimately resulting in the design of adaptable, scalable, and highly efficient algorithms. My research lies at the intersection of numerical linear algebra, machine learning, and statistical methodology, including kernel methods, Bayesian modeling, and uncertainty quantification.


Paz Fink Shustin | Mathematical Institute (