
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


Dong An - Introduction to quantum linear algebra, part 3/3 - IPAM at UCLA

Nicolas Delfosse - Introduction to quantum error correction, part 3/3 - IPAM at UCLA

Nicolas Delfosse - Introduction to quantum error correction, part 2/3 - IPAM at UCLA

Di Fang - Quantum algorithms for dynamics simulation: differential equations - IPAM at UCLA

Yu Tong - The Heisenberg limit and early fault-tolerant quantum algorithms, part 2/2 - IPAM at UCLA

Dong An - Introduction to quantum linear algebra, part 2/3 - IPAM at UCLA

Nicolas Delfosse - Introduction to quantum error correction, part 1/3 - IPAM at UCLA

Di Fang - Quantum algorithms for dynamics simulation: Hamiltonian simulation & general differential

Dong An - Introduction to quantum linear algebra, part 1 of 3 - IPAM at UCLA

Yu Tong - The Heisenberg limit and early fault-tolerant quantum algorithms, part 1/2 - IPAM at UCLA

Ping Yang - Autonomous Discovery for Separation Sciences - IPAM at UCLA

Cyrus Harrison - Visualization and Analysis of HPC Simulation Data with VisIt - IPAM at UCLA

Jan Janssen - Packaging Scientific Software with Conda-Forge - IPAM at UCLA

Rahul Gayatri - Introduction to Containers on Perlmutter - IPAM at UCLA

Evan Weinberg - A SNAPshot of Profiler Guided Optimization - IPAM at UCLA

Stan Moore - Optimizing GPU Performance: Case Study Using Chain Benchmark in LAMMPS

Justin Smith - The state of neural network interatomic potentials - IPAM at UCLA

Richard Hennig & Jason Gibson - AI-driven workflows for the discovery of novel superconductors

James Corbett - Flux: a next generation resource manager for HPC and beyond - IPAM at UCLA
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