
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


Ziad Obermeyer - Dissecting Algorithmic Bias Pt. 1/2 - IPAM at UCLA

Sendhil Mullainathan - Economic Fairness Pt. 2/2 - IPAM at UCLA

Sendhil Mullainathan - Economic Fairness Pt. 1/2 - IPAM at UCLA

Guy Rothblum - Affirmative action, Composition Pt. 2/2 - IPAM at UCLA

Cynthia Dwork - Affirmative action, Composition Pt. 1/2 - IPAM at UCLA

Guy Rothblum - Individual Fairness - IPAM at UCLA

Guy Rothblum - Group Fairness and Individual Fairness Pt. 2/2 - IPAM at UCLA

Cynthia Dwork - Group Fairness and Individual Fairness Pt. 1/2 - IPAM at UCLA

David Uminsky - LatMath 2022 - IPAM's Latinx in the Mathematical Sciences Conference

Stephan Garcia - LatMath 2022 - IPAM's Latinx in the Mathematical Sciences Conference

Luis Garcia Puente - LatMath 2022 - IPAM's Latinx in the Mathematical Sciences Conference

Richard Tapia - LatMath 2022 - IPAM's Latinx in the Mathematical Sciences Conference

Hortensia Soto - LatMath 2022 - IPAM's Latinx in the Mathematical Sciences Conference

Jose Perea - LatMath 2022 - IPAM's Latinx in the Mathematical Sciences Conference

Alicia Prieto Langarica - LatMath 2022 - IPAM's Latinx in the Mathematical Sciences Conference

Andrea Tirelli - Monte Carlo and Machine Learning Approaches in Quantum Mechanics - IPAM at UCLA

Brenda Rubenstein - Extending the Reach of Quantum Monte Carlo Methods via Machine Learning

Bryan Clark - Variational Wavefunctions, Machine Learning Architecture for Fermions & Gauge Theories

Marivi Fernandez Serra - Machine learning to improve the exchange and correlation functional in DFT
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