
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


Petar Veličković - Reasoning Algorithmically: from Toy Experiments to AGI Modules - IPAM at UCLA

Tias Guns - Prediction + Optimisation: without and with decision-focused learning - IPAM at UCLA

Stefanie Jegelka - Two aspects of learning algorithms: generalization under shifts & loss functions

Hamsa Bastani - Decision-Aware Learning for Global Health Supply Chains - IPAM at UCLA

Timo Berthold - Machine Learning inside MIP solvers - IPAM at UCLA

Louis-Martin Rousseau - Combining Machine Learning & Optimization for efficient healthcare delivery

Phebe Vayanos - Integer optimization for predictive & prescriptive analytics in high stakes domains

Bartolomeo Stellato - Learning for Decision-Making Under Uncertainty - IPAM at UCLA

Pascal Van Hentenryck - Fusing Machine Learning and Optimization - IPAM at UCLA

Priya Donti - Optimization-in-the-loop AI for energy and climate - IPAM at UCLA

Thomas Laurent - Feature Learning and Generalization on a Discrete Data Model - IPAM at UCLA

Ellen Vitercik - Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty

Yuandong Tian - AI-guided nonlinear optimization for real-world problems - IPAM at UCLA

Xavier Bresson - Learning to Untangle Genome Assembly Graphs - IPAM at UCLA

Matej Balog - AlphaTensor: Discover faster matrix multiplication algorithms with RL - IPAM at UCLA

Vinod Nair - Restricted Boltzmann Machines for Maximum Satisfiability - IPAM at UCLA

Bistra Dilkina - Machine Learning for MIP Solving - IPAM at UCLA

Andrea Lodi - Continuous cutting plane algorithms in integer programming - IPAM at UCLA

Bill Fefferman - On "Experimental" Complexity Theory - IPAM at UCLA
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