I am a Ph.D. student at UC Berkeley, where I work with Anca Dragan in the InterACT Lab. My interests lie at the intersection of machine learning, robotics, and human-robot interaction, with a focus in robot learning with uncertainty. In my research, I tackle ways in which autonomous systems’ models of the world and of other agents (e.g. humans) can go wrong, and improve them for enhanced interaction between people and robots.
I previously received a B.S. in Computer Science and Engineering at MIT in 2017, where I was fortunate to work with Professors Polina Golland and Stefanie Jegelka, and Dr. Adrian Dalca on probabilistic models for medical image analysis.
I was named a 2021 Apple Scholar in AI and Machine Learning (AI/ML).
Our journal paper "Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Corrections" was selected for Honorable Mention for the 2020 IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award.
Our paper "Dynamically Switching Human Prediction Models for Efficient Planning" was accepted at the IEEE International Conference on Robotics and Automation 2021.
Our paper "Situational Confidence Assistance for Lifelong Shared Autonomy" was accepted at the IEEE International Conference on Robotics and Automation 2021.
Our paper on reward learning using feature traces, a novel type of human input, was accepted at the ACM/IEEE International Conference on Human-Robot Interaction 2021.
Co-organizing a workshop on "Advances and Challenges in Imitation Learning for Robotics" at Robotics: Science and Systems 2020.
Our extended abstract "Detecting Hypothesis Space Misspecification in Robot Learning from Human Input" was accepted at the Human-Robot Interaction Pioneers Workshop 2020.