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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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Alexis Battle: "Methods for analysis of rare genetic variants"

07 Mar 2018
43m
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Melissa Gymrek: "Interpreting the role of short tandem repeats in human phenotypes"

07 Mar 2018
48m
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Peter Kharchenko: "Single-cell analysis of heterogeneity in normal and disease tissues"

07 Mar 2018
42m
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David Knowles: "Probabilistic programming for genomics"

07 Mar 2018
41m
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Alex Zelikovsky: "Modeling and Simulating Evolution and Spread of RNA Viruses"

07 Mar 2018
37m
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Smita Krishnaswamy: "Manifold-Learning Yields Insights into Single Cell Data Analysis"

07 Mar 2018
37m
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Geoffrey Schiebinger: "Analyzing Developmental Processes with Optimal Transport"

07 Mar 2018
38m
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Kayhan Batmanghelich: "Imaging as High-Dimensional Endophenotype"

07 Mar 2018
1h 5m
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Roger Piqué-Regí: "Tools for identifying gene regulatory variants and GxE interactions for..."

07 Mar 2018
49m
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Po-Ru Loh: "Detecting mosaic chromosomal alterations using long-range phasing"

07 Mar 2018
47m
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Wei Zhu: "LDMnet: low dimensional manifold regularized neural networks"

16 Feb 2018
26m
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Zuowei Shen: "Deep Learning: Approximation of functions by composition"

16 Feb 2018
43m
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Sainbayar Sukhbaatar: "Deep Architecture for Sets and Its Application to Multi-agent Communication"

16 Feb 2018
33m
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Michael Bronstein: "Deep functional maps: intrinsic structured prediction..."

16 Feb 2018
40m
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Stanley Osher: "New Techniques in Optimization and Their Applications to Deep Learning..."

16 Feb 2018
34m
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Tom Goldstein: "What do neural loss surfaces look like?"

16 Feb 2018
50m
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Stefano Soatto: "Invariance and disentanglement in deep representations"

16 Feb 2018
36m
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Pratik Chaudhari: "Unraveling the mysteries of stochastic gradient descent on deep neural networks"

16 Feb 2018
39m
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Yann LeCun: "A Few (More) Approaches to Unsupervised Learning"

16 Feb 2018
44m
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Arthur Szlam: "Composable planning with attributes"

16 Feb 2018
48m
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