Hello!

I am an applied scientist and software engineer at FieldAI, where I lead the Calibration, Localization, and Mapping (CLAM) team that develops core geometric perception algorithms for autonomous robots operating in unstructured environments. I received my Ph.D. from UCLA, where I was advised by Dr. Brett Lopez at the Verifiable & Control-Theoretic Robotics (VECTR) Laboratory and developed a set of state-of-the-art LiDAR SLAM algorithms that were used in the DARPA Subterranean Challenge as part of NASA JPL’s solution. Prior to that, I received my B.S./M.S. from UC San Diego where I worked on computer vision research. Throughout my career, I have been fortunate to work with and learn from many brilliant scientists and engineers whose perspectives have shaped how I approach complex technical problems and the way that I think.

I am broadly interested in robotic perception, spanning fields such as computer vision, machine learning, state estimation, and numerical optimization. My long-term research objective is to enable fast and robust, human-like geometric and semantic perception capabilities for mobile robots through innovative algorithmic design grounded by first principles. Towards this, much of my work has focused on the development of general, domain-agnostic multimodal algorithms that are resilient to outliers and are practical and real-time.

Life goals include helping to advance our engineering capabilities in assistive and exploratory robotics, and one day seeing the Clippers win an NBA championship.