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

Kyle Cranmer: "Simulation-based inference, interpretability, and experimental design"

Kieron Burke: "Density functionals from machine learning"

Anatole von Lilienfeld: "Quantum Machine Learning"

Sergei Kalinin: "Deep Learning Dive into the Scanning Transmission Electron Microscopy: Material..."

Adji Bousso Dieng: "Structured Deep Generative Models"

Peter Battaglia: "Learning structured models of physics"

Judea Pearl: "Interpretability and explainability from a causal lens"

Peter Frazier: "Accelerating Scientific Discovery through Interpretable Machine Learning and Int..."

J. Nathan Kutz: "Coordinates, governing equations and limits of model discovery"

Atılım Güneş Baydin: "Universal Probabilistic Programming in Simulators"

Heather Kulik: "Molecular design blueprints: materials and catalysts from new simulation and mac..."

Andrew White: "Maximum Entropy Methods for Combining Physics-Based Simulation with Empirical Data"

Weinan E: "Machine learning based multi-scale modeling"

Julia Ling: "Model Interpretability for Building Confidence and Sparking Insight in Scientific A..."

Shirley Ho: "Simulating the Universe with Deep Learning"

Michael Mahoney: "Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks"

Cecilia Clementi: "Learning molecular models from simulation and experimental data"

Juan Carrasquilla: "Simulating quantum dynamics with neural machine translation"

Han Pu: "Solving Quantum Many-Body Problems with Deep Neural Networks"

Giuseppe Carleo: "Generative and variational modeling for quantum many-body physics"