I am a final year Electrical Engineering and Computer Sciences (EECS) PhD student at University of California, Berkeley advised by Professor Claire Tomlin. I work in the Hybrid Systems Lab, which is a part of the Berkeley Artificial Intelligence Research (BAIR) Lab. I am broadly interested in robotics, computer vision, and explainable AI, but most of my work has focused on the practical and safe deployment of learning-enabled robotics. Throughout my PhD, I have worked closely with the Intelligent Systems Center (ISC) at the Johns Hopkins Applied Physics Lab (APL) and am currently collaborating with research scientists and engineers in the Intelligent Networked Systems (INS) group at Microsoft Research (MSR). You can learn more about my research projects on my Research page.

In addition to research, I am passionate about improving the inclusivity and accessibility of AI education. While at Berkeley, I have served on the course staff for Linear System Theory (EE 221A), Optimization Models in Engineering (EECS 127/EECS 227AT), and Introduction to Machine Learning (CS 189/CS 289A). I am currently the head TA for Berkeley's Intro to ML class and am working to update the course to meet the needs of students in this new era of AI. During my time at Berkeley, I have also had the privilege of mentoring a number of high school students and introducing them to various aspects of AI. You can learn more about my teaching endeavors on my Teaching page.