
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


Kazuaki Tanaka - Constructing Certified Enclosures with Neural Sub- and Super-Solutions

Samuel Pollard - Formal Verification of High Consequence Systems - IPAM at UCLA

Allison Baker - Is it still correct? Quality assurance for an Earth System Model - IPAM at UCLA

Terence Tao - The integrated explicit analytic number theory network - IPAM at UCLA

Terence Tao - Reflections on the Foundations of Interpretability workshop - IPAM at UCLA

Paul Riechers - The shape of beliefs and abstraction in neural networks - IPAM at UCLA

Eunice Jun - Imagining a new future for science - IPAM at UCLA

Jacob Tsimerman - Some new results in Open Source Game Theory - IPAM at UCLA

Dmitry Vaintrob - Statistical theory of learning sparse structure - IPAM at UCLA

Mahdi Soltanolkotabi - Interpreting Generative Models for Better Steering: Generation to Verifiable

Gitta Kutyniok - From Mathematical Guarantees to Computational Limits of AI Interpretability

Gyorgy Turan - Error-correcting codes: a sandbox for learning, interpretability and optimization

Gal Mishne - From Explanations to Mechanisms: Interpreting Computation in Graph Neural Networks

Elias Bareinboim - Towards Causal AI: From Mechanism to Understanding - IPAM at UCLA

Michael Winer - Mechanistic Understanding of Multilayer Perceptrons - IPAM at UCLA

Yusu Wang - Neural Network Generalization through an algorithmic lens - IPAM at UCLA

Sam Eisenstat - Concepts, information, and objectivity - IPAM at UCLA

Christopher Potts - Creating and detecting subliminal learning effects on demand - IPAM at UCLA

Ryan Cotterell - Two views of a language model - IPAM at UCLA
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