
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


Terence Tao - Machine assistance and the future of research mathematics - IPAM at UCLA

Barry Barish - LIGO: 10 Years of New Science - IPAM at UCLA

Noah Brenowitz - Does progress in Earth System Modeling require more data, more compute, or both?

David John Gagne - Physics and Uncertainty in the Community Research Earth Digital Intelligence Twin

Franca Hoffmann - Efficient calibration for black-box physics-based models - IPAM at UCLA

Haiwen Guan - LUCIE Lightweight Uncoupled Climate Emulator long-term stability, physical consistency

Gary Froyland - Operator-theoretic methods for earth systems: atmosphere, ocean, and ML

Claire Valva - Koopman operator methods for analysis of tropical climate variability - IPAM at UCLA

Mohamed Iskandarani - Uncertainty Quantification in Ocean-Atmosphere Simulation w/ Polynomial Chaos

Po-Lun Ma - Encoding microphysics in Earth system modeling with artificial intelligence

Eduardo Siman - Image-to-Image Tropical Cyclone Wind Field Diagnosis - IPAM at UCLA

Michael Fischer - Machine Learning Approach to Estimate 3-D Tropical Cyclone Kinematic Structure

Maria Molina - Learning Without Labels: New Insights into Climate and Extremes - IPAM at UCLA

Donifan Barahona - Synthesizing Observations, Simulations, & In Situ Data in Atmospheric Retrieval

Milan Curcic - Deterministic AI Agents for Downstream Tasks & Application to Hurricane Forecasting

Adam Rupe - A complex systems perspective on machine learning for Earth science - IPAM at UCLA

Dimitris Giannakis - Operator-theoretic approach, feature extraction & statistical model of climate

Damian Rouson - Cloud microphysics via progressive refinement mimicking maximal info entropy

Tapio Schneider - Hybrid Physics/AI Model of Turbulence, Convection, & Cloud Feedback in CliMA Model
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