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Institute for Pure & Applied Mathematics (IPAM) ยท 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.

Episodes

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Dan Work: "Traffic Flow Smoothing at scale"

10 Nov 2020
42m
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Pierluigi Pisu: "On Resilient Control for Secure Connected Vehicles"

10 Nov 2020
38m
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Antonella Ferrara: "From connected & autonomous vehicles control to vehicular traffic control"

10 Nov 2020
44m
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Sam Coogan: "Mixed autonomy in ride-sharing networks"

10 Nov 2020
41m
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Katie Driggs-Campbell: "Insights from Inference & Prediction for Safe Vehicle-Pedestrian Interac..."

10 Nov 2020
42m
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Benjamin Seibold: "Energy Impact of Automated Vehicles used as Sparse Traffic Controllers"

10 Nov 2020
43m
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Cathy Wu: "Mixed Autonomy Traffic: A Reinforcement Learning Perspective"

10 Nov 2020
41m
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Jonathan Sprinkle: "Large-scale high-fidelity simulation for multi-vehicle applications"

10 Nov 2020
39m
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Ricardo Sanfelice: "Set-Based Hybrid Predictive Control for Collision Detection and Evasion in AVs"

10 Nov 2020
45m
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Luca Carlone: "Certifiable Perception for Robots & AVs: From Robust Algorithms to Robust Systems"

10 Nov 2020
43m
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Aaron Ames: "Safety-Critical Control of Autonomous Systems"

10 Nov 2020
40m
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Alexandre Alahi: "Socially-aware AI for Vehicle Autonomy"

20 Oct 2020
41m
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Shai Avidan: "Learning to Sample"

20 Oct 2020
40m
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Raquel Urtasun: "A future with affordable Self-driving vehicles"

20 Oct 2020
45m
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Dorsa Sadigh: "Interaction-Aware Planning: A Human-Centered Approach toward Autonomous Driving"

20 Oct 2020
49m
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Sharon Di: "Harnessing Mean-Field Game & Data Science for Mixed Autonomy"

20 Oct 2020
45m
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David Held: "Self-supervised learning for autonomous driving"

20 Oct 2020
41m
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Darius Burschka: "Visual Modeling & Collision Avoidance in Dynamic Environments from Monocular V..."

20 Oct 2020
37m
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Richard Tsai: "Line-of-sight optimization for moving agents in complex urban domains"

20 Oct 2020
43m
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Wolfram Burgard: "(Self-)Supervised Learning for Perception Tasks in Automated Driving"

20 Oct 2020
39m
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