Hi, I'm a CS Ph.D. student at the University of Pennsylvania advised by Dinesh Jayaraman. My goal is to develop robots can learn and act by themselves. I received my BS/MS in CS from the University of Southern California, where I worked with Joseph J. Lim. I also developed software for nonprofits at Code The Change, and interned at Tesla and Intel.

I aim to develop robot learning algorithms that can curate their own data through autonomous exploration, leverage easy to obtain priors, and learn efficiently through model-based reinforcement learning.

• 🔭 Exploration: The key bottleneck to robot learning is data. If we want a robot to learn many different behaviors, a diverse dataset is needed for training it. Therefore, exploration, or the process of collecting the data, is of paramount importance.
• 🗃️ Convenient Priors: Giving prior knowledge can be very powerful but the most helpful priors are typically the most expensive. I am interested in designing priors that are easy to specify and methods that can maximally exploit them.
• 🔮 World Models: Endowing robots with a world model, or a model that predicts the dynamics of the world, is very useful. A robot can generate data by predicting outcomes, or leverage the model to make informed decisions not achievable by a model-free method.