I am an Assistant Professor at the School of Interactive Computing at Georgia Tech and a (part-time) Research Scientist at NVIDIA AI. I work at the intersection of Robotics and Machine Learning.
I received my Ph.D. in CS from Stanford University advised by Fei-Fei Li and Silvio Savarese (2015-2021) and B.S. from Columbia University (SEAS'15). I've spent time at DeepMind UK (2019), ZOOX (2017), Autodesk Research (2016), CMU RI (2014), and Columbia Robotics Lab (2013-2015).
Ph.D. applicants: I'm hiring 2022 - 2023! Apply through the official application portal for the CS Ph.D. program (School of Interactive Computing) and and make sure that you mention my name in your application. Research opportunities: If you are a student at GT, please fill out this questionnaire and email me once submitted.
We especially encourage applications from students of traditionally underrepresented groups in robotics and AI such as BIPOC (black, indigenous, and people of color), women, and LGBTQ+ communities.
My research goal is to enable physical autonomy in everyday human environments with minimum expert intervention. Towards this goal, my works draw equally from Robotics, Machine Learning, and Computer Vision, including topics such as imitation & reinforcement learning, representation learning, manipulation, and human-robot interaction. My current research focuses on visuomotor skill learning, structured world models for long-horizon planning, and data-driven approaches to human-robot collaboration.
An algorithm framework that simultaneously addresses the reward delusion problem in supervised reward learning and the overfitting discriminator problem in adversarial imitation learning.
We propose an end-to-end model that jointly infers object category, location, and relationships. The model learns to iteratively improve its prediction by passing messages on a scene graph.