
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


Norbert Schuch - Tensor networks and the negative sign problem - IPAM at UCLA

Grigoris Paouris - Small ball probabilities for random tensors and analysis of tensor decompositions

Alessandra Bernardi - Defective tensor network varieties - IPAM at UCLA

Fabian Faulstich - Tailoring coupled cluster theory with matrix product states - IPAM at UCLA

Carolin Wille - Topological Dualities from Matchgate Tensor Networks - IPAM at UCLA

Jeroen Zuiddam - Discreteness of asymptotic tensor ranks - IPAM at UCLA

Di Fang - Numerical Analysis for Hamiltonian Simulation and Hamiltonian Learning - IPAM at UCLA

Jingmei Qiu - Conservative Adaptive Rank Integrators for Nonlinear Kinetic Models - IPAM at UCLA

Feng Pan - Arbitrary Tensor Network Algorithm: Theory, Methods and Applications - IPAM at UCLA

Elisa Ercolessi - Hybrid Variational Algorithms for Classical and Quantum Complex Systems

Steve White - The strange energy spectrum of approximate matrix product ground states - IPAM at UCLA

Ion Nechita - Monogamy of highly symmetric states - IPAM at UCLA

Liza Rebrova - Memory-efficient modewise measurements for tensor compression and recovery

Chia-Yu Chang - Border subrank of tensors - IPAM at UCLA

Yuehaw Khoo - High-dimensional PDEs, tensor-network, and convex optimization - IPAM at UCLA

Mathias Oster - Solving High-Dimensional Optimal Control Problems w/ Empirical Tensor Train Approx.

Roman Orus - News on tensor Networks for machine learning and quantum computing simulation

Benjamin Villalonga - Benchmarking NISQ and QEC experiments with tensor networks - IPAM at UCLA

Joseph Landsberg - Geometry of tensor networks and tensor invariants - IPAM at UCLA
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