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Institute for Pure & Applied Mathematics (IPAM) · Science

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

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Nick Vannieuwenhoven: "Sensitivity of tensor decompositions"

28 May 2021
29m
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Mateusz Michalek: "Algebraic methods to construct tensors"

28 May 2021
28m
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Kaie Kubias: "Rank-one tensor completion"

28 May 2021
27m
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Rong Ge: "Beyond Lazy Training for Over-parameterized Tensor Decomposition"

28 May 2021
28m
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Alex Wein: "The Kikuchi Hierarchy and Tensor PCA"

28 May 2021
34m
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Lexing Ying: "Strictly-correlated Electron Functional and Multimarginal Optimal Transport"

25 May 2021
29m
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Tomaž Prosen: "Exactly solved models of chaotic many-body dynamics"

25 May 2021
34m
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Román Orús: "News on tensor network simulations for quantum matter and beyond"

25 May 2021
30m
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Sylvain Capponi: "Chiral spin liquid phases in SU(N) quantum magnets"

25 May 2021
27m
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Yuriel Núñez Fernández: "Half-filled quantum Hall problem on cylinder, DMRG & composite fermions"

25 May 2021
26m
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Örs Legeza: "Tensor network state methods in material science and ab initio quantum chemistry"

25 May 2021
31m
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Cécile Repellin: "Detecting fractional Chern insulators in few-boson systems"

25 May 2021
29m
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Wei Li: "Decoding Quantum Magnetism Genome by Thermal Tensor Networks"

25 May 2021
33m
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Glen Evenbly: "Using tensor networks to design improved wavelets for image compression"

25 May 2021
31m
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Ivan Oseledets: "Tensor-train decomposition and its applications in machine learning"

25 May 2021
29m
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Frank Verstraete: "Algorithms for continuous matrix product states"

25 May 2021
32m
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Ian McCulloch: "Finite-entanglement scaling functions at quantum critical points"

25 May 2021
34m
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Zheng-Cheng Gu: "Emergent gapless quantum spin liquid from deconfined quantum critical point"

25 May 2021
30m
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Xiao-Gang Wen: "Exactly soluble tensor network model in 2+1D with U(1) symmetry & quantize Hall ..."

25 May 2021
32m
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Shi-Ju Ran: "Deep learning quantum states for Hamiltonian predictions"

25 May 2021
19m
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