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.

Institute for Pure & Applied Mathematics (IPAM)

Episodes

Tess Smidt: "Euclidean Neural Networks for Emulating Ab Initio Calculations and Generating Atomi..."

Benjamin Nachman: "Likelihood free generative modeling for high energy physics"

Jascha Sohl-Dickstein: "Generalizing Hamiltonian Monte Carlo with Neural Networks"

Ankit Patel: "Breaking Bad: Recent Advances from Function Space Characterization of Neural Nets ..."

Richard Hennig: "Machine-learning for materials and physics discovery through symbolic regressio..."

Phiala Shanahan: "Machine learning for lattice field theory"

Lars Ruthotto: "A Numerical Analysis Perspective on Deep Neural Networks"

Joshua Agar: "Automatic Feature Extraction from Hyperspectral Imagery using Deep Recurrent Neura..."

Katya Scheinberg: "Recent advances in Derivative-Free Optimization and its connection to reinfor..."

Ross King: "Automating Science using Robot Scientists"

Rafael Gómez-Bombarelli: "Coarse graining autoencoders and evolutionary learning of atomistic..."

Patrick Riley: "Reinforcement learning for molecular generation"

Nicola De Cao: "Deep Generative Models for Molecular Graphs"

Philip Kurian: "New horizons in quantum biology: Learning complexity, emergence, and coherence..."

Frank Noé: "Deep Generative Learning for Physics Many-Body Systems"

Laurent Dinh: "A primer on normalizing flows"

Joshua Bloom: "Physics-Informed (and -informative) Generative Modelling in Astronomy"

Alexandre Tkatchenko: "Towards a Unified Machine Learning Model of Molecular Chemical Space"

Frank Noé: "Connection between Statistics and Machine Learning"

Lars Ruthotto: "Deep Neural Networks Motivated By Differential Equations (Part 2/2)"