Chris Hoang

Hello! I am a PhD student in the CILVR lab at NYU Courant working with Mengye Ren. My research is supported by the NDSEG fellowship. 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. Self-supervised learning methods for in-the-wild visual data
2. Learning semantic, spatial, and contextual representations from video
3. Extracting world model capabilities from generative models

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.

ch3451 [at] nyu.edu

CV  /   /   /   / 

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 May 28, 2024

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