Optimal Transport (OT) is a powerful theory with far reaching applications and connections both within and outside of mathematics. Originating from Monge’s classical question, it saw an impressive body of works at the crossroads of analysis and geometry (Wasserstein gradient flows, synthetic Ricci curvature, geometric and functional inequalities) reaching out to such diverse fields as economics, physics (cosmology, relativity, fluid mechanics), and machine learning. Ever since Kantorovich’s rigorous relaxation of Monge’s problem, optimal transport and Wasserstein distances have played an important role in many problems in statistics and beyond. However, only in recent years the full potential of OT in stochastic analysis and applied probability has become apparent.
The workshop aims to capitalise on the recent momentum, serve as a catalyst for further growth of probabilistic mass transports (such as Schroedinger problem / Entropic Transport, Martingale Optimal Transport, and Adapted Optimal Transport), and to foster interactions between different communities.