Austin J. Stromme is an Assistant Professor in the Statistics Department at ENSAE Paris and CREST. His research lies at the intersection of optimization, geometry, probability, and statistics, with a particular focus on optimal transport and its applications to high-dimensional data.

His work explores the mathematical foundations of AI, especially how optimal transport can help better understand machine learning systems, statistical inference, and complex high-dimensional problems. He studies both the theoretical properties of optimal transport and its practical relevance for modern AI, where data often involves many variables, such as images or large-scale structured datasets.

Austin Stromme is a Hi! PARIS Fellow for the 2023-2026 period. His research at Hi! PARIS focuses on the mathematical foundations of AI, with an emphasis on optimal transport, high-dimensional statistics, and the links between theory and practice in machine learning.

Through this work, he contributes to a deeper understanding of how and why machine learning methods work, while developing mathematical tools that can support more robust and efficient AI systems.

Learn more about his Hi! PARIS Chair here.