
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


Prineha Narang - Shining New Light on Quantum Matter - IPAM at UCLA

Spring 2023 Info Session - New Mathematics for the Exascale: Applications to Materials Science

Angus Kirkland - Strategies for quantitative imaging & reconstruction from high speed/low dose data

John Miao - Capturing the 3D Atomic Structure of Non-Crystalline Materials - IPAM at UCLA

Paul Weiss - Leveraging Sparsity in Scanning Probe Microscopy - IPAM at UCLA

Jerome Darbon - Algorithms for Non-Local Filtering; application CryoElectron & biological microscopy

Hong Zhou - New cryoEM Programs for Studying Native Biological Complexes, in situ and in Action

Paul Voyles - Data-Information-Knowledge: Material Microscopy Problems w/ Computational Solutions?

Peter Binev - Learning Theory and Computational Microscopy - IPAM at UCLA

Stanley Osher - Variational Methods for Computational Microscopy - IPAM at UCLA

John Miao - Coherent Diffractive Imaging: A Unification of Microscopy, Diffraction and Computation

Mahdi Soltanolkotabi - Machine Learning and Computational Imaging - IPAM at UCLA

Deanna Needell - Using Algebraic Factorizations for Interpretable Learning - IPAM at UCLA

Peter Binev - Modeling in Electron Microscopy - IPAM at UCLA

Tomislav Stankovski - Neural Cross-frequency Coupling: delta-alpha, resting state, anesthesia, sleep

Serhiy Yanchuk - Adaptive dynamical networks: from multiclusters to recurrent synchronization

Sarah Muldoon - Characterizing differences in structural network changes from traumatic brain injury

Ginestra Bianconi - Dynamics of higher-order networks: effect of topology and triadic interactions

Jaroen Lamb - Towards a bifurcation theory of random dynamical systems - IPAM at UCLA
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