• Ashesh Jain

    Director of Engineering

    Lyft Self-Driving Program

    Palo Alto, CA

    asheshjain399@gmail.com

    asheshjain@lyft.com

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    I am currently the Head of Perception at Lyft Self-Driving Program. My team is responsible for all on-vehicle perception capabilities of Lyft's Autonomous Vehicle. This incldues Computer Vision, 3D Perception, Tracking, Sensor Calibration, and the machine learning infrasturure to deploy real-time deep learned models on the vehicle platform. Prior to Lyft, I led the team for sensor fusion and 3D tracking for autonomous driving at Zoox.

    In my academic life, I obtained PhD in Computer Science from Cornell University. I was a visiting research scholar at the Stanford AI Lab where I started the Brain4Cars and RoboBrain projects. I also have a Bachelors degree in Electrical Engineering from IIT Delhi.

    News

    My recent talk @Scale Conference on self-supervised learning for autonomous driving

    • 网络加速器免 on self-supervised learning for Autonomous driving @Scale conference, October 2024

    • Blog post on sensor calibration for Autonomous Vehicle, August 2024

    • Lyft open sourced one of the largest 3D Perception and Prediction data set for Autonomous Vehicle, July 2024

    • Spotlight from Lyft on my journey, Feb 2024

    • One paper accepted to CVPR 2018.

    • Joined Lyft Self Driving Program, January 2018

    • Best student paper award at CVPR 2016 (Deep learning on spatio-temporal graphs)

    • PhD thesis, May 2016.

    • Structural-RNN accepted as an ORAL to CVPR 2016.

    • Our paper on sensory-fusion RNN-LSTM for driver activity anticipation is accepted to ICRA 2016

    • I recently gave talks at Oculus, University of Washington Seattle, Keynote at the ICCV workshop on Autonomous driving, BayLearn Symposium, Qualcomm, and Zoox Labs on: Deep Learning for Spatio-Temporal Problems: On Cars, Humans, and Robots (免费vpm全球网络加速器, 300MB) (pdf, 30MB)

    • Neuralmodels: A deep learning package for quick prototyping of structures of Recurrent Neural Networks and for deep learning over spatio-temporal graphs.

    • 网络加速器下载 driving data set and sensory-fusion RNN code.

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    My research interest lies at the intersection of machine learning, robotics, and computer vision. Broadly, I build machine learning systems & algorithms for agents – such as robots, cars etc. – to learn from informative human signals at a large-scale. Most of my work has been in multi-modal sensor-rich robotic settings, for which I have developed sensory fusion deep learning architectures. I have developed and deployed algorithms on multiple robotic platforms (PR2, Baxter etc.), on cars, and crowd-sourcing systems.

    Brain4Cars

    RoboBrain

    PlanIt

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    Learning From Natural Human Interactions For Assistive Robots

    PhD Thesis, Ashesh Jain, May 2016 [PDF]

    Journal Publications

    Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture

    Ashesh Jain, Hema S Koppula, Shane Soh, Bharad Raghavan, Avi Singh, Ashutosh Saxena

    Tech Report (under review), January 2016 [arXiv] [Code and Data set]

    Learning Preferences for Manipulation Tasks from Online Coactive Feedback.

    Ashesh Jain, Shikhar Sharma, Thorsten Joachims, Ashutosh Saxena

    IJRR 2015 [PDF]

    Conference Publications

    Structural-RNN: Deep Learning on Spatio-Temporal Graphs

    Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena

    CVPR 2016 (Full ORAL) (Best Student Paper) [PDF] [arXiv] [supplementary] [YouTube免费加速器] [网络加速器免费版]

    Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture

    Ashesh Jain, Avi Singh, Hema S Koppula, Shane Soh, Ashutosh Saxena

    ICRA 2016 [PDF] [arXiv] [Code]

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    Car That Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models

    学习贯彻落实习近平总书记重要讲话精神 三个“没有改变” 湖北 ...:2021-5-28 · 习近平总书记在参加湖北伕表团审议时表示,湖北经济长期向好的基本面没有改变,多年积累的综合优势没有改变,在国家和区域发展中的重要地位没有改变。这三个“没有改变”, 让湖北经济重振吃下了“定心丸”。那么,应该如何理解这三个“没有改变”呢?

    ICCV 2015 [PDF] [Code and Data set] [arXiv]

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    Brain4Cars: Sensory-Fusion Recurrent Neural Models for Driver Activity Anticipation

    Ashesh Jain, Shane Soh, Bharad Raghavan, Avi Singh, Hema S Koppula, Ashutosh Saxena

    BayLearn Symposium 2015 [Extended abstract] (Full ORAL)

    PlanIt: A Crowdsourcing Approach for Learning to Plan Paths from Large Scale Preference Feedback.

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    ICRA 2015 [PDF]

    学习贯彻落实习近平总书记重要讲话精神 三个“没有改变” 湖北 ...:2021-5-28 · 习近平总书记在参加湖北伕表团审议时表示,湖北经济长期向好的基本面没有改变,多年积累的综合优势没有改变,在国家和区域发展中的重要地位没有改变。这三个“没有改变”, 让湖北经济重振吃下了“定心丸”。那么,应该如何理解这三个“没有改变”呢?

    Ashutosh Saxena, Ashesh Jain, Ozan Sener, Aditya Jami, Dipendra K Misra, Hema S Koppula

    ISRR 2015 [arXiv]

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    Anticipatory Planning for Human-Robot Teams.

    Hema S Koppula, Ashesh Jain, Ashutosh Saxena

    ISER 2014 [PDF]

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    Beyond Geometric Path Planning: Learning Context-Driven Trajectory Preferences via Sub-optimal Feedback.

    Ashesh Jain, Shikhar Sharma, Ashutosh Saxena

    ISRR 2013 [PDF]

    Learning Trajectory Preferences for Manipulators via Iterative Improvement.

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    NIPS 2013 [PDF]

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    SPG-GMKL: Generalized multiple kernel learning with a million kernels.

    Ashesh Jain, S. V. N. Vishwanathan, Manik Varma

    SIGKDD 2012 [PDF | 网络加速器下载]

    云墙vp

    Brain4Cars: Sensory-fusion deep learning for smart-cars

    Interactive human-robot learning from coactive feedback

    Anticipating driver maneuvers few seconds in advance

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    • Invited talk at Oculus (Facebook), April 2016

    • Keynote at the ICCV workshop on Autonomous driving. Title: 南京未来科技城定制云资源免费 - CRI:2021-6-25 · 原标题:一朵“云”助力千余企业腾飞 南京未来科技城定制云资源免费服务入园企业 针对园区企业需求,“内部”定制云资源,同时积极发挥园区综合协调作用,众“店小二”式服 Dec 2015

    • Invited talk at Zoox Labs (autonomous driving startup), Dec 2015

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    • Oral at BayLearn 2015 on Brain4Cars: Sensory-fusion Recurrent Neural Networks (Video)

    • Invited Talk at RSS Workshop on Model Learning for Human-Robot Communication, July 2015

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    • Invited Talk at ICRA Tutorial on Planning, Control, and Sensing for Safe Human-Robot Interaction, May 2015

    • Invited Talk at IIT Kanpur Department of Computer Science. RoboBrain and Learning from Weak Signals, Feb 2015

    • Stanford Semantics and Geometry Seminar. RoboBrain and Learning from Weak Signals, Feb 2015

    • Stanford Robotics Seminar. Learning from Weak Signals, Nov 2014

    • Introductory talk at LPCHS workshop RSS 2014. Learning from Humans. (Slides)

    • Cornell AI Seminar and ISRR 2013. Beyond Geometric Path Planning. (免费加速器上网)

    • ICML Robot Learning workshop 2013. (Slides)

    • Oral at SIGKDD 2012. (Video) (Slides)

    • Invited spotlight at Mysore Park Workshop on Machine Learning 2012. (Video) (Slides)

    • Lecture at Indo-German Winter Acadmey 2010. (Slides)