
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


Mateusz Michalek: "Algebraic methods to construct tensors"

Kaie Kubias: "Rank-one tensor completion"

Rong Ge: "Beyond Lazy Training for Over-parameterized Tensor Decomposition"

Alex Wein: "The Kikuchi Hierarchy and Tensor PCA"

Lexing Ying: "Strictly-correlated Electron Functional and Multimarginal Optimal Transport"

Tomaž Prosen: "Exactly solved models of chaotic many-body dynamics"

Román Orús: "News on tensor network simulations for quantum matter and beyond"

Sylvain Capponi: "Chiral spin liquid phases in SU(N) quantum magnets"

Yuriel Núñez Fernández: "Half-filled quantum Hall problem on cylinder, DMRG & composite fermions"

Örs Legeza: "Tensor network state methods in material science and ab initio quantum chemistry"

Cécile Repellin: "Detecting fractional Chern insulators in few-boson systems"

Wei Li: "Decoding Quantum Magnetism Genome by Thermal Tensor Networks"

Glen Evenbly: "Using tensor networks to design improved wavelets for image compression"

Ivan Oseledets: "Tensor-train decomposition and its applications in machine learning"

Frank Verstraete: "Algorithms for continuous matrix product states"

Ian McCulloch: "Finite-entanglement scaling functions at quantum critical points"

Zheng-Cheng Gu: "Emergent gapless quantum spin liquid from deconfined quantum critical point"

Xiao-Gang Wen: "Exactly soluble tensor network model in 2+1D with U(1) symmetry & quantize Hall ..."

Shi-Ju Ran: "Deep learning quantum states for Hamiltonian predictions"
Follow this podcast in Podwise
Sign in to get AI summaries, transcripts and mind maps for any episode, including new ones.
