Hello! I am a PhD student in the CILVR lab at NYU Courant advised by Mengye Ren and supported by the NDSEG fellowship. My research goal is to advance the visual perception and reasoning capabilities of AI agents to enable them to robustly operate in the complex real world. I'm currently exploring:

1. World models from videos and experience
2. Verifiers and test-time planning

I've also been thinking about:

1. Task horizons, hierarchy, memory
2. Exploration, data curation

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.

News

Summer 2025	I joined Meta FAIR as a research scientist intern in Seattle.
March 2024	I have received the NDSEG Fellowship to support my PhD at NYU.
September 2023	I started my computer science PhD at NYU advised by Mengye Ren.

Publications

	Discrete JEPA: Learning Discrete Token Representations without Reconstruction Junyeob Baek, Hosung Lee, Chris Hoang, Mengye Ren, Sungjin Ahn ICML Tokenization Workshop, 2025 paper We introduce Discrete-JEPA, which learns discrete semantic tokens for improved symbolic reasoning and long-horizon planning.
	PooDLe: Pooled and dense self-supervised learning from naturalistic videos Alex N. Wang*, Chris Hoang*, Yuwen Xiong, Yann LeCun, Mengye Ren ICLR, 2025 project page / arXiv We propose a self-supervised learning framework that combines pooled and dense objectives to learn representations with spatial and semantic understanding from naturalistic videos.
	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.