
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


John Harrison - Formalization and Automated Reasoning: A Personal and Historical Perspective

Andrej Bauer - Formalizing invisible mathematics - IPAM at UCLA

Benedikt Ahrens - Univalent Foundations and the UniMath library - IPAM at UCLA

Adam Topaz - The Liquid Tensor Experiment - IPAM at UCLA

Anatole von Lilienfeld - Go EAST, young scientist - First principles view on chemical compound space

Maxim Ziatdinov - Bits and Atoms: Exploring the Intersection of Machine Learning and Microscopy

Rose Yu - Incorporating Symmetry for Learning Spatiotemporal Dynamics - IPAM at UCLA

Kyle Cranmer - Connections and cross pollination from quarks to the cosmos - IPAM at UCLA

Jutta Rogal - Machine learning collective variables for sampling of structural transformations

Tess Smidt - Learning how to break symmetry with symmetry-preserving neural networks - IPAM at UCLA

Bruno Correia - Exploring new molecular representations for the design of biological function

Kyunghyun Cho - Lab-in-the-loop de novo antibody design - what are we missing from machine learning?

Stefan Chmiela - Accurate global machine learning force fields for molecules with hundreds of atoms

Alexandre Tkatchenko - Fully Quantum (Bio)Molecular Simulations: Dream or Reality? - IPAM at UCLA

Xavier Bresson - Graph MLP-Mixer for Molecular Analysis - IPAM at UCLA

Mark Tuckerman - From A to B via a synthesis of rare-event sampling and machine learning

Tommi Jaakola - Diffusion based distributional modeling of conformers, blind docking and proteins

Kristin Persson - Data-Driven Design for Energy Materials - IPAM at UCLA

Rafael Gomez-Bombarelli - End-to-end learning and auto-differentiation: forces, uncertainties, etc.
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