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.
New paper accepted to Robotics and Automation Letters, "Learning Perceptual Concepts by Bootstrapping from Human Queries". In this work, we ask for human input to learn a low-dimensional variant of the perceptual concept, then use it to generate a larger data set for learning the concept in the high-dimensional space.
I gave a talk at Apple's AI/ML seminar.
I was named a Robotics: Science and Systems Pioneer!
Our paper on Aligning Robot Representations with Humans has been accepted to the Workshop on Collaborative Robots and the Work of the Future, at ICRA 2022.
Our paper "Learning Perceptual Concepts by Bootstrapping from Human Queries" has been accepted as an oral presentation at the Scaling Robot Learning workshop at ICRA 2022.
New paper on arXiv, "Teaching Robots to Span the Space of Functional Expressive Motion", where we enable people to efficiently teach robots expressive motions during task execution.
Our paper "Inducing Structure in Reward Learning via Feature Learning" was accepted at the International Journal of Robotics Research.
I gave a talk at MIT's Interactive Robotics Group.
I was invited to speak at University of Chicago's Topics in Human-Robot Interaction graduate course.
I gave a talk at the Workshop on Human-AI Collaboration in Sequential Decision-Making at ICML.