
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


Anna Keselman: "Spectral Signatures of Quasiparticle Interactions in Antiferromagnets"

Mi-Song Dupuy: "A new ordering scheme for QC-DMRG based on symmetry of higher-order singular values"

Steven White: "Phase diagram of the 2D t-t'-J model"

Caterina De Bacco: "Matrix product states for modeling dynamical processes on networks"

Johnnie Gray: "Hyper-optimized tensor network contraction - simplifications, applications & appr..."

Nick Mayhall: "Selected Configuration Interaction in a Basis of Cluster State Tensor Products"

Aram Harrow: "Phase transitions in the complexity of simulating random shallow quantum circuits"

Nithin Govindarajan: "Spline-based separable expansions for approximation, regression & classifi..."

Ulrich Schollwöck: "Matrix product states for real materials"

Miles Stoudenmire: "Tensor Networks for Machine Learning and Applications"

Peter Benner: "Parallel Algorithms for Tensor Train Arithmetic"

Virginie Ehrlacher: "Tensorized Adaptive Biasing Force method for molecular dynamics simulations"

Nadav Cohen: "Implicit Regularization in Deep Learning: Lessons Learned from Matrix & Tensor Fac..."

Lei Wang: "Tropical Tensor Networks"

Ming Yuan: "Low rank tensor completion"

Reinhold Schneider: "Solving Backward Stochastic Differential Equation & HJB equations with Tree..."

Anthony Nouy: "Approximation and learning with tree tensor networks"

Lars Grasedyck: "Multigrid in Hierarchical Low Rank Tensor Formats"

Ankur Moitra: "Tensor Decompositions and their Applications (Part 2/2)"
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