I am a PhD student in Computer Science at Stanford University, where I work at the intersection of machine learning, computer vision, and robotics. Specifically I am interested in problems relating to self-supervised reinforcement learning and multi-task learning. I am co-advised by Professors Chelsea Finn and Silvio Savarese, and am funded by the National Science Foundation Graduate Fellowship.

I completed my Bachelors in Computer Science at the California Institute of Technology (Caltech), where I worked with Yisong Yue on multi-agent reinforcement learning. In the past I have worked at Google Brain and General Electric Current.

Github | CV | Google Scholar | Twitter
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News

Publications

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Causal Induction from Visual Observations for Goal Directed Tasks
Suraj Nair, Yuke Zhu, Silvio Savarese, Li Fei-Fei
Workshop on Causal Machine Learning, NeurIPS 2019
[paper] [website] [code]

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Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation
Suraj Nair, Chelsea Finn
ICLR, 2020
[paper] [website] [code]

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RoboNet: Large-Scale Multi-Robot Learning
Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, Chelsea Finn
CoRL, 2019
[paper] [website]

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Time Reversal as Self-Supervision
Suraj Nair, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, Vikash Kumar
ICRA, 2020
[paper] [website]

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Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstrations
De-An Huang*, Suraj Nair*, Danfei Xu*, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, Juan Carlos Niebles
CVPR, 2019 (Oral)
[paper]

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Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
Danfei Xu*, Suraj Nair*, Yuke Zhu, Julian Gao, Animesh Garg, Li Fei-Fei, Silvio Savarese
ICRA, 2018
[paper] [website] [video] [Two Minute Papers]

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Reliable Real-time Seismic Signal/Noise Discrimination with Machine Learning
Men-Andrin Meier, Zach Ross, Anshul Ramachandran, Ashwin Balakrishna, Suraj Nair, Peter Kundzicz, Zefeng Li, Jennifer Andrews, Egill Hauksson, Yisong Yue
Journal of Geophysical Research: Solid Earth 124
[paper]

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Annotated Reconstruction of 3D Spaces Using Drones
Suraj Nair, Anshul Ramachandran, Peter Kundzicz
IEEE MIT URTC, 2017, Best Paper Presentation
[paper]

Experience

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Research Intern / Student Researcher
June 2018 - September 2019 | Mountain View, CA
Google Brain

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Visiting Researcher
June 2017 - December 2017 | Stanford, CA
Stanford Vision and Learning Lab

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Machine Learning Consultant
March 2017 - September 2017 | Los Angeles, CA
Vizzario

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Student Researcher
April 2016 - June 2018 | Pasadena, CA
Decision, Optimization, and Learning at California Institute of Technology

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Software Development Intern
June - Sept 2016 | Mountain View, CA
General Electric, Current by GE

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Software Engineering Intern
June - Sept 2015 | Mountain View, CA
KloudData Inc.

More info

  • Google AI Blog - Introducing a New Framework for Flexible and Reproducible Reinforcement Learning Research
  • Towards Data Science. This is a very informative data science learning website. Most of the articles are published in the form of a case with code and drawings.
  • That site can help you find startup projects that's related to neural networks and artificial intelligence.