I am a Hooke Research Fellow at the Mathematical Institute at the University of Oxford and a Nicolas Kurti Junior Research Fellow at Brasenose College. In Fall 2021, I am a Research Fellow at the Simons Institute for the Theory of Computing, participating in the special program on Geometric Methods in Optimization and Sampling.
My research focuses on two key questions in Machine Learning and Data Science:
How can we harness the geometric structure in our data for the design of efficient Machine Learning methods? Read more about Optimization on Manifolds, Machine Learning on Manifolds and Discrete Geometry and Machine Learning on Graphs.
How can we learn accurate models with limited data and resources? Read more on Learning to control with little data.
I recieved my PhD from Princeton University in 2021, where I was very fortunate to be advised by Charles Fefferman. I have also spent time at MIT’s Laboratory for Information and Decision Systems, the Max Planck Institute for Mathematics in the Sciences, as well as at the research labs of Facebook (FAIR), Google and Microsoft. I am also interested in applications of Artificial Intelligence in the legal space and am the Chief Scientist of the start up Claudius Legal Intelligence.
View my current CV here.
PhD in Applied Mathematics, 2021
Princeton University
BSc/MSc: Mathematics + Physics, 2016
University of Leipzig
MSc in Applied Math, 2015
University of Washington
Nov 29, 2021, Workshop on Optimization Under Symmetry
Oct 25, 2021, INFORMS Annual Meeting
Oct 23, 2021, AMS Fall Sectional Meeting
Jun 15, 2021, Oxford Data Science Seminar
May 20, 2021, SIAM Conference on Applied Linear Algebra (Minisymposium: Linear Algebra and Differential Geometry)
Riemannian Frank-Wolfe methods for nonconvex and g-convex optimization.
Discrete Ricci curvature for a curvature-based analysis of complex networks.
Harnessing the geometric structure of data in Machine Learning.