Eugene Vinitsky

Eugene Vinitsky

I'm looking for students for my new research group at NYU for Fall 2023. If the research below sounds interesting to you, shoot me an email. If you don't have the "right" background but have a demonstrated ability to pick up new skills, I'm just as interested as if you have the "right" background.

I'm an incoming Assistant Professor at NYU Tandon in 2023 based in Civil Engineering with a PhD in control from UC Berkeley with Alexandre Bayen. My research goal is to see complex, human-like behavior emerge from unsupervised interaction between groups of learning agents with an applications focus on enabling autonomous vehicles to operate in rich scenarios. Concretely this leads to a lot of questions I'm currently interested in:

  • How can we use RL to design models of human agents? How can we ensure that RL designed agents are human-compatible?
  • How can we synthesize environments that push and test the capabilities of our agents?
  • What algorithmic advances and software tools are needed to address these questions?
In practice this means working on understanding how to push the state of the art in multi-agent RL algorithms, designing new data-driven simulators, and trying to deploy simulator-designed controllers into real-world systems. For a sample of the type of work that I do / have done, check out the research section below.

I've spent time at Tesla, Deepmind, Facebook AI Research, and am a recipient of an NSF fellowship.

Email  /  CV  /  Google Scholar  /  LinkedIn

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  1. We've started a new conference on reinforcement learning! If you're excited about RL, RLC is a great place to send papers.
  2. I'm one of the workshop hosts for the Workshop on Lagrangian Control for Traffic Flow Smoothing in Mixed Autonomy Settings at CDC 2019 in Nice. Come by!
  3. Our work on developing new benchmarks for traffic control was covered in Science.
flowgo Graduate Student Instructor and Course Co-creator, EE290O, Fall 2018

Prof. Bayen, Prof. Wu, Yashar Zeynali, Aboudy Kriedieh, and I put together a course on the use of deep multi-agent reinforcement learning for the study of transportation systems. The lecture notes and homeworks are available above.

All source code stolen from this lovely guy source code,