
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


Keith Promislow - Correlation as a Source of Stochasticity in Continuum Models - IPAM at UCLA

Katsuyo Thornton - The Role of Materials and Microstructures in Electrochemical Energy Storage

Jörg Neugebauer - DFT for Electrochemistry - IPAM at UCLA

Marwa Banna - Strong Convergence for Multiplicative Brownian Motions on the General Linear Group

Wei Zhu - Structure-preserving machine learning and data-driven structure discovery - IPAM at UCLA

Levon Nurbekyan - System Identification via Invariant Measures - IPAM at UCLA

Fuqun Han - Regularized Wasserstein Proximal Algorithms for Nonsmooth Sampling Problems

Tingwei Meng - Bayesian sampler for inverse problems of a stochastic process by leveraging HJ PDEs

Markos Katsoulakis - Hamilton-Jacobi Equations, Mean-Field Games, and Optimal Control for Robust ML

Jiajia Yu - Learning and Inference in Mean-Field Games - IPAM at UCLA

Yuanqi Du - Bridging Non-equilibrium Simulation and Probabilistic Machine Learning - IPAM at UCLA

Guang Lin - Scalable algorithms for Bayesian deep learning via stochastic gradient Monte Carlo

Alex Cloninger - Linearized Optimal Transport to Predict Evolution of Stochastic Particle Systems

Nisha Chandramoorthy - Toward physical generative modeling - IPAM at UCLA

Zecheng Zhang - Deep Operator Learning Approximation and Distributed Applications - IPAM at UCLA

Luc Rey-Bellet - IPAM at UCLA

Li Wang - Learning-enhanced structure preserving particle methods for Landau equation - IPAM at UCLA

Siting Liu - Score-Based Generative Models through the Lens of Wasserstein Proximal Operators

Benjamin Zhang - Probabilistic operator learning: generative modeling and uncertainty quantification
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