
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


Tomas Berggren - Dimer models and random tilings (Part 2) - IPAM at UCLA

Greta Panova - Symmetric Functions (Part 2) - IPAM at UCLA

Terrence George - Introduction to cluster algebras (Part 2) - IPAM at UCLA

Eveliina Peltola - Gaussian free field and Schramm--Loewner Evolution (Part 2) - IPAM at UCLA

Pavel Galashin - Introduction to cluster algebras (Part 1) - IPAM at UCLA

Greta Panova - Symmetric Functions (Part 1) - IPAM at UCLA

Cédric Boutillier - Dimer models and random tilings (Part 1) - IPAM at UCLA

Nathanael Berestycki - Gaussian free field and Schramm--Loewner Evolution (Part 1) - IPAM at UCLA

Raghu Meka - Complexity of Sparse Linear Regression - IPAM at UCLA

Surbhi Goel - Beyond Worst-case Guarantees for Sequential Prediction: Robustness via Abstention

Pravesh Kothari - Algorithms Approaching the Threshold for Semirandom Planted Clique - IPAM at UCLA

Arya Mazumdar - Sample complexity of estimation in logistic regression - IPAM at UCLA

Vatsal Sharan - Memory as a lens to understand efficient learning and optimization - IPAM at UCLA

Sitan Chen - Provably learning a multi-head attention layer - IPAM at UCLA

Jelena Diakonikolas - Robust Learning of a Neuron: Bridging Computational Gaps Using Optimization

Giang Tran - Fast Multipole Attention: A Divide-and-Conquer Attention Mechanism for Long Sequences

Wasim Huleihel - Testing Dependency of Databases - IPAM at UCLA

Matus Telgarsky - A Perceptron Trio - IPAM at UCLA

Cynthia Rush - Is It Easier to Count Communities Than Find Them? - IPAM at UCLA
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