
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


Cecelia Clementi - Coarse-graining classical and quantum systems - IPAM at UCLA

Payel Das - Design and Evaluation of Foundation Models and Generative AI in Molecular Space

Mohammed AlQuraishi - OpenFold: Lessons and insights from rebuilding and retraining AlphaFold2

Patrick Riley - Symbolic Regression for Discovery of a DFT Functional - IPAM at UCLA

Frank Noe - Advancing molecular simulation with deep learning - IPAM at UCLA

Lisa Nickerson - Addressing Confounds in Neuroimaging Machine Learning Predictions - IPAM at UCLA

Nikolaus Kriegskorte - Controversial stimuli: experiments to adjudicate computational hypotheses

Pamela Douglas - Exploring XAI Techniques for Neuroimaging - IPAM at UCLA

Vince Calhoun - Maximizing Information in neuroimaging: approaches for analysis and visualization

Anders Søgaard - What's Opaque to Whom - and Why? - IPAM at UCLA

Oliver Eberle - Explainable structured machine learning in similarity, graph and transformer models

André Freitas - Building explanation machines for science: a neuro-symbolic perspective

Matteo Valleriani - XAI Historian - IPAM at UCLA

Gregoire Montavon - Towards Higher-Order and Disentangled Explainable AI - IPAM at UCLA

Petros Koumoutsakos - Alloys: Artificial Intelligence and Scientific Computing for Fluid Mechanics

Cecelia Clementi - Designing molecular models with machine learning and experimental data

Michele Ceriotti - Physical insights from atomic-scale machine learning - IPAM at UCLA

Evrim Acar - Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations

Frederick Klauschen - AI in cancer research and diagnostics - IPAM at UCLA
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