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Institute for Pure & Applied Mathematics (IPAM) · Science

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

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Mira Todorova - Molecular Level Modeling - IPAM at UCLA

05 Sep 2025
1h 20m
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Keith Promislow - Correlation as a Source of Stochasticity in Continuum Models - IPAM at UCLA

05 Sep 2025
1h 9m
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Katsuyo Thornton - The Role of Materials and Microstructures in Electrochemical Energy Storage

04 Sep 2025
1h 11m
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Jörg Neugebauer - DFT for Electrochemistry - IPAM at UCLA

04 Sep 2025
1h 21m
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Marwa Banna - Strong Convergence for Multiplicative Brownian Motions on the General Linear Group

22 Jul 2025
42m
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Wei Zhu - Structure-preserving machine learning and data-driven structure discovery - IPAM at UCLA

18 Jul 2025
39m
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Levon Nurbekyan - System Identification via Invariant Measures - IPAM at UCLA

18 Jul 2025
37m
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Fuqun Han - Regularized Wasserstein Proximal Algorithms for Nonsmooth Sampling Problems

18 Jul 2025
42m
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Tingwei Meng - Bayesian sampler for inverse problems of a stochastic process by leveraging HJ PDEs

18 Jul 2025
36m
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Markos Katsoulakis - Hamilton-Jacobi Equations, Mean-Field Games, and Optimal Control for Robust ML

18 Jul 2025
41m
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Jiajia Yu - Learning and Inference in Mean-Field Games - IPAM at UCLA

18 Jul 2025
38m
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Yuanqi Du - Bridging Non-equilibrium Simulation and Probabilistic Machine Learning - IPAM at UCLA

18 Jul 2025
31m
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Guang Lin - Scalable algorithms for Bayesian deep learning via stochastic gradient Monte Carlo

17 Jul 2025
31m
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Alex Cloninger - Linearized Optimal Transport to Predict Evolution of Stochastic Particle Systems

17 Jul 2025
40m
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Nisha Chandramoorthy - Toward physical generative modeling - IPAM at UCLA

17 Jul 2025
42m
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Zecheng Zhang - Deep Operator Learning Approximation and Distributed Applications - IPAM at UCLA

17 Jul 2025
37m
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Luc Rey-Bellet - IPAM at UCLA

17 Jul 2025
36m
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Li Wang - Learning-enhanced structure preserving particle methods for Landau equation - IPAM at UCLA

17 Jul 2025
31m
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Siting Liu - Score-Based Generative Models through the Lens of Wasserstein Proximal Operators

17 Jul 2025
26m
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Benjamin Zhang - Probabilistic operator learning: generative modeling and uncertainty quantification

15 Jul 2025
40m
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