Suvansh Sanjeev

I am a first-year PhD student in the Robotics Institute at Carnegie Mellon University, where I am advised by Zico Kolter and Zac Manchester. I am interested in artificial intelligence, machine learning, and optimization. I graduated from UC Berkeley, where I worked with Professors Sergey Levine and Claire Tomlin in the Berkeley Artificial Intelligence Research (BAIR) Lab on deep RL and safe learning.

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Research

I am currently working on differentiable optimization and physics simulators. My past research worked towards bringing learning to the real world. I have worked on developing more natural means of task specification for deep RL to avoid the burden of manually engineered reward functions, as well as on developing data-efficient learning techniques that allow for safety guarantees throughout the learning process.

clean-usnob PaVE the Way for NFL Passing Analytics: Passing Value in Expectation
NFL Big Data Bowl 2021
[Code]
clean-usnob Ecological Reinforcement Learning
John D. Co-Reyes*, Suvansh Sanjeev*, Glen Berseth, Abhishek Gupta, Sergey Levine
Deep RL Workshop at NeurIPS, 2019
[Paper]
clean-usnob Guiding Policies with Language via Meta-Learning
John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev, Nick Altieri, Jacob Andreas, John DeNero, Pieter Abbeel, Sergey Levine
International Conference on Learning Representations, 2019
Best Paper at Meta-Learning Workshop at NeurIPS, 2018
[Paper]
Teaching
clean-usnob EECS 127 (Optimization Models and Applications), Spring 2019, Fall 2019 (Head TA)

EE 120 (Signals and Systems), Fall 2018 (Head TA)

CS 61C (Great Ideas in Computer Architecture (Machine Structures), Summer 2018

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