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Author Information
Rowan McAllister (University of California Berkeley)
Nicholas Rhinehart (Carnegie Mellon University)
Nick Rhinehart is a Postdoctoral Scholar in the Electrical Engineering and Computer Science Department at the University of California, Berkeley, where he works with Sergey Levine. His work focuses on fundamental and applied research in machine learning and computer vision for behavioral forecasting and control in complex environments, with an emphasis on imitation learning, reinforcement learning, and deep learning methods. Applications of his work include autonomous vehicles and first-person video. He received a Ph.D. in Robotics from Carnegie Mellon University with Kris Kitani, and B.S. and B.A. degrees in Engineering and Computer Science from Swarthmore College. Nick's work has been honored with a Best Paper Award at the ICML 2019 Workshop on AI for Autonomous Driving and a Best Paper Honorable Mention Award at ICCV 2017. His work has been published at a variety of top-tier venues in machine learning, computer vision, and robotics, including AAMAS, CoRL, CVPR, ECCV, ICCV, ICLR, ICML, ICRA, NeurIPS, and PAMI. Nick co-organized the workshop on Machine Learning in Autonomous Driving at NeurIPS 2019, the workshop on Imitation, Intent, and Interaction at ICML 2019, and the Tutorial on Inverse RL for Computer Vision at CVPR 2018.
Li Erran Li (Scale AI)
Li Erran Li is the head of machine learning at Scale and an adjunct professor at Columbia University. Previously, he was chief scientist at Pony.ai. Before that, he was with the perception team at Uber ATG and machine learning platform team at Uber where he worked on deep learning for autonomous driving, led the machine learning platform team technically, and drove strategy for company-wide artificial intelligence initiatives. He started his career at Bell Labs. Li’s current research interests are machine learning, computer vision, learning-based robotics, and their application to autonomous driving. He has a PhD from the computer science department at Cornell University. He’s an ACM Fellow and IEEE Fellow.
More from the Same Authors
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2020 : Paper 1: Multimodal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network »
Rowan McAllister -
2020 : Paper 12: DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation »
Rowan McAllister -
2020 : Paper 15: Calibrating Self-supervised Monocular Depth Estimation »
Rowan McAllister -
2020 : Paper 22: RAMP-CNN: A Novel Neural Network for EnhancedAutomotive Radar Object Recognition »
Rowan McAllister -
2020 : Paper 57: Single Shot Multitask Pedestrian Detection and Behavior Prediction »
Rowan McAllister -
2021 : Hybrid Imitative Planning with Geometric and Predictive Costs in Offroad Environments »
Daniel Shin · Dhruv Shah · Ali Agha · Nicholas Rhinehart · Sergey Levine -
2022 : The Impact of Symbolic Representations on In-context Learning for Few-shot Reasoning »
Hanlin Zhang · yifan zhang · Li Erran Li · Eric Xing -
2022 : Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? »
Gunshi Gupta · Tim G. J. Rudner · Rowan McAllister · Adrien Gaidon · Yarin Gal -
2022 : Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? »
Gunshi Gupta · Tim G. J. Rudner · Rowan McAllister · Adrien Gaidon · Yarin Gal -
2022 : Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation »
yifan zhang · Hanlin Zhang · Zachary Lipton · Li Erran Li · Eric Xing -
2022 Workshop: Machine Learning for Autonomous Driving »
Jiachen Li · Nigamaa Nayakanti · Xinshuo Weng · Daniel Omeiza · Ali Baheri · German Ros · Rowan McAllister -
2021 : Learning to perceive objects by prediction »
Tushar Arora · Li Erran Li · Mingbo Cai -
2021 : Learning to perceive objects by prediction »
Tushar Arora · Li Erran Li · Mingbo Cai -
2021 Poster: Outcome-Driven Reinforcement Learning via Variational Inference »
Tim G. J. Rudner · Vitchyr Pong · Rowan McAllister · Yarin Gal · Sergey Levine -
2021 Poster: Information is Power: Intrinsic Control via Information Capture »
Nicholas Rhinehart · Jenny Wang · Glen Berseth · John Co-Reyes · Danijar Hafner · Chelsea Finn · Sergey Levine -
2021 Poster: A Causal Lens for Controllable Text Generation »
Zhiting Hu · Li Erran Li -
2020 Workshop: Machine Learning for Autonomous Driving »
Rowan McAllister · Xinshuo Weng · Daniel Omeiza · Nick Rhinehart · Fisher Yu · German Ros · Vladlen Koltun -
2020 : Welcome »
Rowan McAllister -
2019 : Poster Session »
Matthia Sabatelli · Adam Stooke · Amir Abdi · Paulo Rauber · Leonard Adolphs · Ian Osband · Hardik Meisheri · Karol Kurach · Johannes Ackermann · Matt Benatan · GUO ZHANG · Chen Tessler · Dinghan Shen · Mikayel Samvelyan · Riashat Islam · Murtaza Dalal · Luke Harries · Andrey Kurenkov · Konrad Żołna · Sudeep Dasari · Kristian Hartikainen · Ofir Nachum · Kimin Lee · Markus Holzleitner · Vu Nguyen · Francis Song · Christopher Grimm · Felipe Leno da Silva · Yuping Luo · Yifan Wu · Alex Lee · Thomas Paine · Wei-Yang Qu · Daniel Graves · Yannis Flet-Berliac · Yunhao Tang · Suraj Nair · Matthew Hausknecht · Akhil Bagaria · Simon Schmitt · Bowen Baker · Paavo Parmas · Benjamin Eysenbach · Lisa Lee · Siyu Lin · Daniel Seita · Abhishek Gupta · Riley Simmons-Edler · Yijie Guo · Kevin Corder · Vikash Kumar · Scott Fujimoto · Adam Lerer · Ignasi Clavera Gilaberte · Nicholas Rhinehart · Ashvin Nair · Ge Yang · Lingxiao Wang · Sungryull Sohn · J. Fernando Hernandez-Garcia · Xian Yeow Lee · Rupesh Srivastava · Khimya Khetarpal · Chenjun Xiao · Luckeciano Carvalho Melo · Rishabh Agarwal · Tianhe Yu · Glen Berseth · Devendra Singh Chaplot · Jie Tang · Anirudh Srinivasan · Tharun Kumar Reddy Medini · Aaron Havens · Misha Laskin · Asier Mujika · Rohan Saphal · Joseph Marino · Alex Ray · Joshua Achiam · Ajay Mandlekar · Zhuang Liu · Danijar Hafner · Zhiwen Tang · Ted Xiao · Michael Walton · Jeff Druce · Ferran Alet · Zhang-Wei Hong · Stephanie Chan · Anusha Nagabandi · Hao Liu · Hao Sun · Ge Liu · Dinesh Jayaraman · John Co-Reyes · Sophia Sanborn -
2019 Workshop: Machine Learning for Autonomous Driving »
Rowan McAllister · Nicholas Rhinehart · Fisher Yu · Li Erran Li · Anca Dragan -
2018 : Deep Imitative Models for Flexible Inference, Planning, and Control (Nicholas Rhinehart) »
Nicholas Rhinehart -
2018 : Contributed Talks »
Nicholas Rhinehart · Amar Shah -
2018 : Poster Session 1 »
Kyle H Ambert · Brandon Araki · Xiya Cao · Sungjoon Choi · Hao(Jackson) Cui · Jonas Degrave · Yaqi Duan · Mattie Fellows · Carlos Florensa · Karan Goel · Aditya Gopalan · Ming-Xu Huang · Jonathan Hunt · Cyril Ibrahim · Brian Ichter · Maximilian Igl · Zheng Tracy Ke · Igor Kiselev · Anuj Mahajan · Arash Mehrjou · Karl Pertsch · Alexandre Piche · Nicholas Rhinehart · Thomas Ringstrom · Reazul Hasan Russel · Oleh Rybkin · Ion Stoica · Sharad Vikram · Angelina Wang · Ting-Han Wei · Abigail H Wen · I-Chen Wu · Zhengwei Wu · Linhai Xie · Dinghan Shen -
2018 : Opening Remark »
Li Erran Li · Anca Dragan -
2018 Workshop: NIPS Workshop on Machine Learning for Intelligent Transportation Systems 2018 »
Li Erran Li · Anca Dragan · Juan Carlos Niebles · Silvio Savarese -
2018 : Coffee Break and Poster Session I »
Pim de Haan · Bin Wang · Dequan Wang · Aadil Hayat · Ibrahim Sobh · Muhammad Asif Rana · Thibault Buhet · Nicholas Rhinehart · Arjun Sharma · Alex Bewley · Michael Kelly · Lionel Blondé · Ozgur S. Oguz · Vaibhav Viswanathan · Jeroen Vanbaar · Konrad Żołna · Negar Rostamzadeh · Rowan McAllister · Sanjay Thakur · Alexandros Kalousis · Chelsea Sidrane · Sujoy Paul · Daphne Chen · Michal Garmulewicz · Henryk Michalewski · Coline Devin · Hongyu Ren · Jiaming Song · Wen Sun · Hanzhang Hu · Wulong Liu · Emilie Wirbel -
2018 Poster: Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models »
Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine -
2018 Spotlight: Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models »
Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine -
2017 Workshop: 2017 NIPS Workshop on Machine Learning for Intelligent Transportation Systems »
Li Erran Li · Anca Dragan · Juan Carlos Niebles · Silvio Savarese -
2017 Workshop: ML Systems Workshop @ NIPS 2017 »
Aparna Lakshmiratan · Sarah Bird · Siddhartha Sen · Christopher Ré · Li Erran Li · Joseph Gonzalez · Daniel Crankshaw -
2017 Poster: Predictive-State Decoders: Encoding the Future into Recurrent Networks »
Arun Venkatraman · Nicholas Rhinehart · Wen Sun · Lerrel Pinto · Martial Hebert · Byron Boots · Kris Kitani · J. Bagnell -
2017 Poster: Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs »
Rowan McAllister · Carl Edward Rasmussen -
2016 Workshop: Machine Learning Systems »
Aparna Lakshmiratan · Li Erran Li · Siddhartha Sen · Sarah Bird · Hussein Mehanna -
2016 Workshop: Machine Learning for Intelligent Transportation Systems »
Li Erran Li · Trevor Darrell