
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


Aidan Thompson - LAMMPS simulation: physics models, machine-learning potentials, exascale computing

Heather Kulik - Exploring multi-million compound spaces w/ chemical accuracy using machine learning

Lin Lin - Interacting models for twisted bilayer graphene: quantum chemistry approach - IPAM at UCLA

Segev Wasserkug - Democratizing Optimization Modeling: Status, Challenges, and Future Directions

Jack Deslippe - Guiding Exascale Application Optimization - IPAM at UCLA

Thomas Hudson - Multiscale Modeling - IPAM at UCLA

Danny Perez - Molecular Dynamics 2 - IPAM at UCLA

Evan Weinberg - Acceleration techniques - IPAM at UCLA

Elaine Raybourn - Sociotechnical aspects of new code collaborations - IPAM at UCLA

Tzanio Kolev - Meso and Macroscale Modeling 1 - IPAM at UCLA

Vikram Gavini - DFT 2 - Density functional theory - IPAM at UCLA

Bjoern Enders - Complex Workflows 2 - IPAM at UCLA

Erik Draeger - Future Exascale Architectures - IPAM at UCLA

Johannes Blaschke - Complex Workflows 1 - IPAM at UCLA

Tim Germann - Molecular Dynamics 1 - IPAM at UCLA

Vikram Gavini - DFT 1 - Density functional theory - IPAM at UCLA

Tina Eliassi-Rad - The Pitfalls of Using ML-based Optimization - IPAM at UCLA

Paul Grigas - Offline and Online Learning for Contextual Stochastic Optimization - IPAM at UCLA

Rahul Mazumder - Discrete Optimization-aided Structured Learning at Scale - IPAM at UCLA
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