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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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Jure Leskovec: "Large-scale Graph Representation Learning"

16 Feb 2018
49m
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Joan Bruna: "On Computational Hardness with Graph Neural Networks"

16 Feb 2018
55m
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Federico Monti: "Deep Geometric Matrix Completion"

16 Feb 2018
33m
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Xavier Bresson: "Convolutional Neural Networks on Graphs"

16 Feb 2018
40m
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Michael Elad: "Sparse Modeling in Image Processing and Deep Learning"

16 Feb 2018
42m
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Stéphane Mallat: "Deep Generative Networks as Inverse Problems"

16 Feb 2018
37m
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Kyle Cranmer: "Deep Learning in the Physical Sciences"

16 Feb 2018
41m
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Daniel Rueckert: "Deep learning in medical imaging"

16 Feb 2018
41m
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Alán Aspuru-Guzik: "Do androids dream of electric molecules?"

16 Feb 2018
40m
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Ellie Pavlick: "Should we care about linguistics?"

16 Feb 2018
33m
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Emily Fox: "Interpretable Neural Network Models for Granger Causality Discovery"

16 Feb 2018
39m
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Sam Bowman: "Toward natural language semantics in learned representations"

16 Feb 2018
29m
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Yann LeCun: “AI Breakthroughs & Obstacles to Progress, Mathematical and Otherwise”

15 Feb 2018
1h 22m
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Yann LeCun: "Deep Learning and the Future of Artificial Intelligence”

15 Feb 2018
1h 16m
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Edward Witten: "What's (Relatively) New in Two-Dimensional Gravity”

05 Dec 2017
1h 0m
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The Calculus of Comedy: Math in The Simpsons, Futurama, and The Big Bang Theory

09 Nov 2017
1h 2m
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David Koslicki: "The CAMI Project: Assessment of computational techniques in metagenomics"

04 Aug 2017
42m
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Melissa Gymrek: "Analyzing complex repeat variation from high throughput sequencing data"

04 Aug 2017
48m
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James Zou: "Deep learning for genomics: Introduction and examples"

04 Aug 2017
49m
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Michael Schatz: "Advances in genome sequencing and assembly"

04 Aug 2017
47m
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