
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


Pratik Chaudhari: "Learning with Few Labeled Data"

Oscar Beijbom: "Data, detection, and metrics for autonomous vehicles"

Adam Oberman: "Contributions to deep learning using a mathematical approach: improved model unce..."

Adrien Gaidon: "The 3 R's & P's of Autonomous Driving: Robustness, Randomness, & Risk in Percept..."

Benedetto Piccoli: "Beyond Vehicles and Other Flow Systems"

Paola Goatin: "Macroscopic models for Autonomous Vehicles"

Katie Driggs-Campbell: "Human Factors" (Part 2/2)

Jana Kosecka: "Perception and Learning for Autonomous Driving" (Part 2/2)

Jana Kosecka: "Perception and Learning for Autonomous Driving" (Part 1/2)

M. Grazia Speranza: "Fundamentals of optimization" (Part 2/2)

Katie Driggs-Campbell: "Human Factors" (Part 1/2)

M. Grazia Speranza: "Fundamentals of optimization" (Part 1/2)

Dan Work: "Transportation Engineering for Connected and Automated Vehicles" (Part 2/2)

Jonathan Sprinkle: "Cyber-Physical Systems Challenges: Vehicle Automation & Data Acqu..." (Part 2/2)

Bryant Walker Smith: "Legal Aspects of Autonomous Driving"

Jonathan Sprinkle: "Cyber-Physical Systems Challenges: Vehicle Automation & Data Acqu..." (Part 1/2)

Frank Noé: "Fundamentals of Artificial Intelligence and Machine Learning" (Part 2/2)

Benjamin Seibold: "Basic Traffic Models and Traffic Waves" (Part 2/2)

Dan Work: "Transportation Engineering for Connected and Automated Vehicles" (Part 1/2)
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