Austin J. Stromme

Austin J. Stromme is an Assistant Professor in the Statistics Department at ENSAE Paris, where he serves as a permanent CREST member. His research bridges optimization, geometry, and probability with a focus on statistical aspects of optimal transport, positioning him at the forefront of theoretical machine learning and high-dimensional statistics.

Stromme earned his PhD in Electrical Engineering and Computer Science from MIT under Guy Bresler and Philippe Rigollet. His academic journey reflects deep engagement with mathematical foundations of data science, emphasizing rigorous theoretical frameworks for complex statistical problems.

His primary research spans entropic optimal transport, high-dimensional inference, and geometric methods in machine learning. Stromme investigates fundamental questions about statistical efficiency, computational complexity, and probabilistic structures in large-scale data analysis. His work reveals critical connections between functional inequalities, sampling algorithms, and manifold geometry, with applications spanning trajectory reconstruction, covariance estimation, and non-convex optimization.

Recent publications (2023-2025) demonstrate accelerating impact in theoretical statistics, particularly in establishing sample complexity bounds and convergence rates for entropic optimal transport. This body of work shows increasing sophistication in handling high-dimensional settings while maintaining computational feasibility, reflecting broader trends toward unifying geometric intuition with statistical rigor in modern machine learning.

Stromme received the Best Paper Award at the 2024 International Conference on Soft Methods in Probability and Statistics for his breakthrough on intrinsic dimension scaling in entropic optimal transport. This recognition highlights his contributions to advancing theoretical understanding of dimensionality challenges in statistical learning.

As an emerging faculty leader, Stromme co-organizes an influential online seminar series bridging statistics and geometry, fostering interdisciplinary collaboration. While no current PhD students are listed, his active seminar leadership and recent faculty appointment indicate growing mentorship responsibilities. His research is supported through CREST's institutional framework, which facilitates collaborations across mathematics, economics, and data science.

Stromme operates within CREST's vibrant research ecosystem at ENSAE Paris, contributing to a community that integrates advanced statistical theory with real-world economic applications. His work exemplifies the center's commitment to methodological innovation in quantitative social sciences and data-driven decision making.