Research Interests
I study the mathematical and algorithmic foundations of machine learning and AI, drawing on optimal transport, information theory, statistics, and optimization. My current interests include:
Optimal transport theory and the Gromov-Wasserstein (GW) problem: geometry, statistics, algorithms, and alignment of heterogeneous data.
Information theory (including quantum information theory): learning, privacy, security, and information-theoretic foundations of modern AI.
AI safety, mechanistic interpretability, and alignment: theory-driven approaches to understanding, controlling, and steering learned systems (in particular, LLMs).
Robust statistics and decision-making: inference and learning under local (perturbations) and global (outliers) data contamination.
My research is supported by the NSF CAREER Award, an NSF DMS Grant, an NSF-BSF CIF Grant, and the IBM University Award.