Summary
Aaron Huang is an AI and robotics engineer with 11 years of experience applying machine learning to autonomous systems, currently driving AI research at a stealth startup in the San Francisco Bay Area. He built production-capable reinforcement, imitation, and value-learning systems at Zoox and previously developed learning-based planning and decision-making methods during his M.S. in Robotics at CMU under Deva Ramanan and John Dolan. His background spans end-to-end autonomy: perception and realtime CNN deployment, simulation and failure-analysis tooling for CARLA, and embedded vehicle stacks on small-scale testbeds. Aaron’s early work at MIT and NASA includes novelty-detection research and high-fidelity flight-software simulation, while internships across Aurora and Starsky highlight practical model-optimization and deployment on constrained hardware. Known for blending rigorous research with shipping-ready systems, he often focuses on bridging simulation-to-hardware gaps and speeding up computationally heavy algorithms without sacrificing safety.
11 years of coding experience
9 years of employment as a software developer
Bachelor of Science - BS, Aerospace Engineering and Autonomous Systems, Bachelor of Science - BS, Aerospace Engineering and Autonomous Systems at Massachusetts Institute of Technology
High School Diploma, High School Diploma at Lynbrook High School
Master of Science - MS, Robotics, Master of Science - MS, Robotics at Carnegie Mellon University
English, Chinese