
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


Joan Bruna: "On Computational Hardness with Graph Neural Networks"

Federico Monti: "Deep Geometric Matrix Completion"

Xavier Bresson: "Convolutional Neural Networks on Graphs"

Michael Elad: "Sparse Modeling in Image Processing and Deep Learning"

Stéphane Mallat: "Deep Generative Networks as Inverse Problems"

Kyle Cranmer: "Deep Learning in the Physical Sciences"

Daniel Rueckert: "Deep learning in medical imaging"

Alán Aspuru-Guzik: "Do androids dream of electric molecules?"

Ellie Pavlick: "Should we care about linguistics?"

Emily Fox: "Interpretable Neural Network Models for Granger Causality Discovery"

Sam Bowman: "Toward natural language semantics in learned representations"

Yann LeCun: “AI Breakthroughs & Obstacles to Progress, Mathematical and Otherwise”

Yann LeCun: "Deep Learning and the Future of Artificial Intelligence”

Edward Witten: "What's (Relatively) New in Two-Dimensional Gravity”

The Calculus of Comedy: Math in The Simpsons, Futurama, and The Big Bang Theory

David Koslicki: "The CAMI Project: Assessment of computational techniques in metagenomics"

Melissa Gymrek: "Analyzing complex repeat variation from high throughput sequencing data"

James Zou: "Deep learning for genomics: Introduction and examples"

Michael Schatz: "Advances in genome sequencing and assembly"
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