
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


Levon Nurbekyan: "Computational methods for mean-field games (Part 2/2)"

Levon Nurbekyan: "Computational methods for mean-field games (Part 1/2)"

Jeff Calder: "An intro to concentration of measure with applications to graph-based l... (Part 2/2)"

Jeff Calder: "An intro to concentration of measure with applications to graph-based l... (Part 1/2)"

Wilfrid Gangbo: "Analytical Aspect of Mean Field Games (Part 2/2)"

Wilfrid Gangbo: "Analytical Aspect of Mean Field Games (Part 1/2)"

Claire Tomlin: "Towards Real-Time Reachability (Part 2/2)"

Claire Tomlin: "Towards Real-Time Reachability (Part 1/2)"

Adam Oberman: "Gradient descent, Stochastic gradient descent, and acceleration" (Part 2/2)

Adam Oberman: "Generalization Theory in Machine Learning" (Part 1/2)

Angela Schoellig: "Machine Learning for Robotics: Achieving Safety, Performance and Reliability..."

Lieven Vandenberghe: "Bregman proximal methods for semidefinite optimization."

Stephen Wright: "Nonconvex optimization in matrix optimization and distributionally robust optim..."

Dorsa Sadigh: "Beyond Theory of Mind: Learning and Influencing Conventions in Multi-Agent Intera..."

Richard Murray: "Can We Really Use Machine Learning in Safety Critical Systems?"

Francesco Borrelli: "Sample-Based Learning Model Predictive Control"

Melanie Zeilinger: "Learning-based Model Predictive Control - Towards Safe Learning in Control"

Zico Kolter: "Integrating optimization, constraints, and control within deep learning models"

Daniel Kuhn: "Wasserstein Distributionally Robust Optimization: Theory and Applications in Machi..."
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