Hello!

I am an applied scientist and software engineer at Field AI leading the development of high-performance, long-term localization and mapping algorithms for autonomous robots operating in unstructured environments. I received my Ph.D. from UCLA in 2023, 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. Throughout my career, I have been fortunate to work with and learn from many brilliant scientists and engineers – all of whom have influenced my approach to complex technical problems through conversations that have shaped the way I view the world.

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 2D/3D 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.