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Institute for Pure & Applied Mathematics (IPAM) · 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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Steve Brunton - Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics

24 Jan 2023
58m
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Cecelia Clementi - Coarse-graining classical and quantum systems - IPAM at UCLA

24 Jan 2023
56m
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Payel Das - Design and Evaluation of Foundation Models and Generative AI in Molecular Space

24 Jan 2023
52m
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Mohammed AlQuraishi - OpenFold: Lessons and insights from rebuilding and retraining AlphaFold2

23 Jan 2023
48m
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Patrick Riley - Symbolic Regression for Discovery of a DFT Functional - IPAM at UCLA

23 Jan 2023
52m
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Frank Noe - Advancing molecular simulation with deep learning - IPAM at UCLA

23 Jan 2023
58m
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Lisa Nickerson - Addressing Confounds in Neuroimaging Machine Learning Predictions - IPAM at UCLA

14 Jan 2023
47m
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Nikolaus Kriegskorte - Controversial stimuli: experiments to adjudicate computational hypotheses

13 Jan 2023
46m
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Pamela Douglas - Exploring XAI Techniques for Neuroimaging - IPAM at UCLA

13 Jan 2023
34m
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Vince Calhoun - Maximizing Information in neuroimaging: approaches for analysis and visualization

13 Jan 2023
48m
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Anders Søgaard - What's Opaque to Whom - and Why? - IPAM at UCLA

12 Jan 2023
36m
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Oliver Eberle - Explainable structured machine learning in similarity, graph and transformer models

12 Jan 2023
52m
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André Freitas - Building explanation machines for science: a neuro-symbolic perspective

12 Jan 2023
52m
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Matteo Valleriani - XAI Historian - IPAM at UCLA

12 Jan 2023
45m
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Gregoire Montavon - Towards Higher-Order and Disentangled Explainable AI - IPAM at UCLA

12 Jan 2023
43m
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Petros Koumoutsakos - Alloys: Artificial Intelligence and Scientific Computing for Fluid Mechanics

12 Jan 2023
58m
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Cecelia Clementi - Designing molecular models with machine learning and experimental data

11 Jan 2023
46m
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Michele Ceriotti - Physical insights from atomic-scale machine learning - IPAM at UCLA

11 Jan 2023
59m
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Evrim Acar - Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations

11 Jan 2023
49m
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Frederick Klauschen - AI in cancer research and diagnostics - IPAM at UCLA

11 Jan 2023
32m
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