Gaoyue (Kathy) Zhou

I am a first-year Computer Science Ph.D. student at NYU Courant advised by Professors Lerrel Pinto and Yann LeCun.

Prior to NYU, I was a MS in Robotics student at CMU Robotics Institute advised by Professor Abhinav Gupta. I worked on enhancing robots' generalization capabilities and benchmarking. My thesis can be found here.

Prior to CMU, I did my undergraduate in Computer Science and Applied Mathematics at UC Berkeley. During my time at Berkeley, I had the pleasure to work with Professors Sergey Levine and John DeNero in the Berkeley Artificial Intelligence Research (BAIR) Lab.

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I am broadly interested in Robotics, Reinforcement Learning, and NLP. My motivation is to build agents with cognitive capabilities that can learn and infer like humans.

We introduce a new benchmark for tabletop manipulation: Train Offline, Test Online (TOTO) and showed performance of state-of-the-art models on two tasks.

In this work, we evaluate Offline RL algorithms' performance on real robot, on in-domain tasks and transfer learning settings.

In this paper, we collect and release a dataset for identifying deceptive actors through the game of Mafia, as well as train models to identify such actors and reveal features of their language.

We proposed a method for pre-training a behavioral prior for reinforcement learning using data from a diverse range of tasks, and used this behavioral prior to speed up learning of new tasks.

We proposed various ways to use CAD Tools for the Procedural Generation of 2-Manifold Sculpture Geometries with high-level control.

10725A: Convex Optimization , Fall 2022

CS 189: Introduction to Machine Learning , Fall 2020

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