Hello! I am a PhD student in the CILVR lab at NYU Courant advised by Mengye Ren and supported by the NDSEG fellowship. I am broadly interested in advancing the visual perception, reasoning, and decision-making capabilities of AI systems to enable them to continuously operate in the complex real world. Towards this end, I am focused on the following directions:

1. Self-supervised learning from video and interaction
2. Test-time compute for exploration, planning, and adaptation
3. Hierarchical structure in long-horizon problems

Previously, I worked on the systematic equities research team at The Voleon Group as a machine learning engineer. Before that, 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.

	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.