Email:
{firstname}.{surname}@inf.ethz.ch
Research Interests: Statistical learning theory, non-parametric and high-dimensional statistics.
My theoretical work is deeply inspired by and builds upon convex and high-dimensional geometry (also known as finite-dimensional Banach space theory).
In addition, I am interested in the practical implications of theory in applied machine learning and statistics, for example, test time tuning and uncertainty quantification.
Recorded Talks:
UT Austin - IFML Seminar - Spring 25'- Video
TTIC - computer science seminar - Fall ‘25 - Video
Hausdorff Research Institute for Mathematics - Spring 24’- Video
2021 Annual Meeting on the Mathematical and Scientific Foundations of Deep Learning - Video
BIRS - Geometric Nonlinear Functional Analysis workshop - Video
*Acknowledgment:
Throughout my academic journey, from my undergraduate studies to the present, I have been fortunate to interact with the GAFA (Geometric and Functional Analysis) community, which has shaped my understanding of high-dimensional geometry and its deep connections to statistics. In particular, I am deeply grateful to Emanuel Milman, Bo’az Klartag, Gideon Schechtman, Shiri Artstein-Avidan, Artem Zvavitch, Mark Rudelson, Alexandros Eskenazis, and Grigoris Paouris. Your guidance has been invaluable!.