
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


Pierluigi Pisu: "On Resilient Control for Secure Connected Vehicles"

Antonella Ferrara: "From connected & autonomous vehicles control to vehicular traffic control"

Sam Coogan: "Mixed autonomy in ride-sharing networks"

Katie Driggs-Campbell: "Insights from Inference & Prediction for Safe Vehicle-Pedestrian Interac..."

Benjamin Seibold: "Energy Impact of Automated Vehicles used as Sparse Traffic Controllers"

Cathy Wu: "Mixed Autonomy Traffic: A Reinforcement Learning Perspective"

Jonathan Sprinkle: "Large-scale high-fidelity simulation for multi-vehicle applications"

Ricardo Sanfelice: "Set-Based Hybrid Predictive Control for Collision Detection and Evasion in AVs"

Luca Carlone: "Certifiable Perception for Robots & AVs: From Robust Algorithms to Robust Systems"

Aaron Ames: "Safety-Critical Control of Autonomous Systems"

Alexandre Alahi: "Socially-aware AI for Vehicle Autonomy"

Shai Avidan: "Learning to Sample"

Raquel Urtasun: "A future with affordable Self-driving vehicles"

Dorsa Sadigh: "Interaction-Aware Planning: A Human-Centered Approach toward Autonomous Driving"

Sharon Di: "Harnessing Mean-Field Game & Data Science for Mixed Autonomy"

David Held: "Self-supervised learning for autonomous driving"

Darius Burschka: "Visual Modeling & Collision Avoidance in Dynamic Environments from Monocular V..."

Richard Tsai: "Line-of-sight optimization for moving agents in complex urban domains"

Wolfram Burgard: "(Self-)Supervised Learning for Perception Tasks in Automated Driving"
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