Chris Hoang

Hello! I am a first-year PhD student in the CILVR lab at NYU Courant working with Mengye Ren. My research goal is to advance the visual perception capabilities of AI agents to enable them to adeptly operate in real-world settings. I am currently exploring the following research directions:

  1. Scaling to real-world complexity by using in-the-wild visual data
  2. Learning spatiotemporal concepts from video and 3D data
  3. Improving adaptability by integrating interaction with perception

More broadly, I am interested in self-supervised learning, representation learning, and computer vision.

Previously, I worked on the systematic equities research team at The Voleon Group as a machine learning engineer. I completed my bachelor's and master's in computer science at the University of Michigan. There, I was fortunate to work with Honglak Lee on reinforcement learning and representation learning, and Michael P. Wellman on multi-agent systems.

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ch3451 [at] nyu.edu

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News

🚨 Summer 2024 🚨

I am looking for collaborators to work on projects in self-supervised learning and visual representation learning. Please send me an email with your CV and transcript if you are interested!

March 2024

I have been selected to receive the NDSEG Fellowship to support my PhD at NYU.

September 2023

I started my computer science PhD at NYU advised by Mengye Ren.
Publications
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning
Chris Hoang, Sungryull Sohn, Jongwook Choi, Wilka Carvalho, Honglak Lee
NeurIPS, 2021
NeurIPS Workshop on Deep Reinforcement Learning, 2020
project page / arXiv / video

We leverage successor features to formulate a graph-based planning framework and goal-conditioned policy, enabling long-horizon goal-reaching in visual environments.

Spoofing the Limit Order Book: A Strategic Agent-Based Analysis
Xintong Wang, Chris Hoang, Yevgeniy Vorobeychik, Michael P. Wellman,
Games, 2021
paper

We use an agent-based model and empirical game-theoretic analysis to study price manipulation in financial markets and propose mitigation mechanisms.

Learning-Based Trading Strategies in the Face of Market Manipulation
Xintong Wang, Chris Hoang, Michael P. Wemman,
ICAIF, 2020
ICML Workshop on AI in Finance, 2019
paper

We design learning-based trading strategies that have improved robustness to market manipulation and evaluate them with agent-based simulation.


Last updated May 28, 2024


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