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Institute for Pure & Applied Mathematics (IPAM) ยท 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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Geordie Williamson - What can the working mathematician expect from deep learning? - IPAM at UCLA

14 Feb 2023
52m
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John Harrison - Formalization and Automated Reasoning: A Personal and Historical Perspective

14 Feb 2023
52m
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Andrej Bauer - Formalizing invisible mathematics - IPAM at UCLA

13 Feb 2023
37m
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Benedikt Ahrens - Univalent Foundations and the UniMath library - IPAM at UCLA

13 Feb 2023
47m
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Adam Topaz - The Liquid Tensor Experiment - IPAM at UCLA

13 Feb 2023
55m
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Anatole von Lilienfeld - Go EAST, young scientist - First principles view on chemical compound space

27 Jan 2023
50m
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Maxim Ziatdinov - Bits and Atoms: Exploring the Intersection of Machine Learning and Microscopy

27 Jan 2023
47m
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Rose Yu - Incorporating Symmetry for Learning Spatiotemporal Dynamics - IPAM at UCLA

27 Jan 2023
1h 3m
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Kyle Cranmer - Connections and cross pollination from quarks to the cosmos - IPAM at UCLA

27 Jan 2023
59m
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Jutta Rogal - Machine learning collective variables for sampling of structural transformations

26 Jan 2023
50m
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Tess Smidt - Learning how to break symmetry with symmetry-preserving neural networks - IPAM at UCLA

26 Jan 2023
59m
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Bruno Correia - Exploring new molecular representations for the design of biological function

26 Jan 2023
50m
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Kyunghyun Cho - Lab-in-the-loop de novo antibody design - what are we missing from machine learning?

26 Jan 2023
1h 2m
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Stefan Chmiela - Accurate global machine learning force fields for molecules with hundreds of atoms

25 Jan 2023
44m
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Alexandre Tkatchenko - Fully Quantum (Bio)Molecular Simulations: Dream or Reality? - IPAM at UCLA

25 Jan 2023
1h 2m
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Xavier Bresson - Graph MLP-Mixer for Molecular Analysis - IPAM at UCLA

25 Jan 2023
57m
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Mark Tuckerman - From A to B via a synthesis of rare-event sampling and machine learning

25 Jan 2023
45m
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Tommi Jaakola - Diffusion based distributional modeling of conformers, blind docking and proteins

25 Jan 2023
54m
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Kristin Persson - Data-Driven Design for Energy Materials - IPAM at UCLA

24 Jan 2023
45m
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Rafael Gomez-Bombarelli - End-to-end learning and auto-differentiation: forces, uncertainties, etc.

24 Jan 2023
56m
Page 56

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