
Institute for Pure & Applied Mathematics (IPAM)
The Institute for Pure and Applied Mathematics (IPAM) is a National Science Foundation Math Institute at the University of California, Los Angeles (UCLA) that fosters the interaction of mathematics with a broad range of science and technology, builds new interdisciplinary research communities, promotes mathematical innovation, and engages and transforms the world through mathematics. IPAM fulfills its mission through workshops and programs that connect mathematics and other disciplines or multiple areas of mathematics. These activities bring in thousands of visitors annually from academia, government, and industry. The NSF has encouraged IPAM to make the scientific presentations of our speakers available to a wider audience. While this YouTube channel contains many of our most popular videos, our full video archive is available at www.ipam.ucla.edu/videos.
Episodes


Haomin Zhou - A supervised learning scheme for Hamilton-Jacobi equation via density coupling

Lars Ruthotto - Mixed-Precision Algorithms for Training Neural ODEs - IPAM at UCLA

Scott McCalla - Tracking bacterial growth using a nonlocal interfacial model - IPAM at UCLA

Wenjing Liao - Exploiting Low-Dimensional Data Structure & Understanding Neural Scaling of Trans...

Stan Osher - Characteristic-Based Deep Learning Framework for Hamilton–Jacobi Equations & O.T.

Alessio Figalli - Optimal Transport: From A to B… and Beyond - Green Family Lecture at IPAM at UCLA

Alessio Figalli - Stability in Geometric & Functional Inequalities: Optimal Transport & Beyond

Therese Basa Landry - Quantum Wasserstein Distance on the Quantum Permutation Group - IPAM at UCLA

Shmuel Friedland - Barrier relaxations of the classical and quantum optimal transport problems

Promit Ghosal - Convergence, Geometry, and Selection Principles in Entropic Optimal Transport

Anna Korba - Some novel kernel-based divergences between probability distributions - IPAM at UCLA

Sivaraman Balakrishnan - Stability Bounds for Smooth Optimal Transport Map, Statistical Implications

Jason Altschuler - Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm

Didier Henrion - Solving moment and polynomial optimization problems on Sobolev spaces

Tyler Maunu - Subspace Langevin Monte Carlo - IPAM at UCLA

Joel Tropp - Scalable semidefinite programming - IPAM at UCLA

Alessio Figalli - On the regularity of optimal transport maps - IPAM at UCLA

Bodhisattva Sen - Constrained denoising, optimal transport, and empirical Bayes - IPAM at UCLA

Tudor Manole - Sharp Deconvolution of Optimal Transport Matchings - IPAM at UCLA
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