
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


Melissa Gymrek: "Interpreting the role of short tandem repeats in human phenotypes"

Peter Kharchenko: "Single-cell analysis of heterogeneity in normal and disease tissues"

David Knowles: "Probabilistic programming for genomics"

Alex Zelikovsky: "Modeling and Simulating Evolution and Spread of RNA Viruses"

Smita Krishnaswamy: "Manifold-Learning Yields Insights into Single Cell Data Analysis"

Geoffrey Schiebinger: "Analyzing Developmental Processes with Optimal Transport"

Kayhan Batmanghelich: "Imaging as High-Dimensional Endophenotype"

Roger Piqué-Regí: "Tools for identifying gene regulatory variants and GxE interactions for..."

Po-Ru Loh: "Detecting mosaic chromosomal alterations using long-range phasing"

Wei Zhu: "LDMnet: low dimensional manifold regularized neural networks"

Zuowei Shen: "Deep Learning: Approximation of functions by composition"

Sainbayar Sukhbaatar: "Deep Architecture for Sets and Its Application to Multi-agent Communication"

Michael Bronstein: "Deep functional maps: intrinsic structured prediction..."

Stanley Osher: "New Techniques in Optimization and Their Applications to Deep Learning..."

Tom Goldstein: "What do neural loss surfaces look like?"

Stefano Soatto: "Invariance and disentanglement in deep representations"

Pratik Chaudhari: "Unraveling the mysteries of stochastic gradient descent on deep neural networks"

Yann LeCun: "A Few (More) Approaches to Unsupervised Learning"

Arthur Szlam: "Composable planning with attributes"
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