ch3451 [at] nyu.edu

Chris Hoang

Hello! I am a PhD student in the CILVR lab at NYU Courant advised by Mengye Ren and supported by the NDSEG fellowship. My areas of interest are machine learning and computer vision. 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. Towards this end, I am exploring the following directions:

1. World models from videos and interaction
2. Self-supervised learning for in-the-wild visual data

I've also been thinking about:

1. Exploration, data curation
2. Test-time planning and optimization

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.

We introduce Discrete-JEPA, which learns discrete semantic tokens for improved symbolic reasoning and long-horizon planning.

We propose a self-supervised learning framework that combines pooled and dense objectives to learn representations with spatial and semantic understanding from naturalistic videos.

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

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

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

Last updated June 14, 2025

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