Summary
Yu Zheng is a control systems engineer with eight years of experience specializing in state estimation, sensor fusion, and resilient control for real-world robotics and electrified vehicles. He holds a PhD focused on incorporating probabilistic AI priors into robust state estimation, and his work has earned awards for real-time AI tracking and a parallel optimization framework for multi-sensor consensus. Yu has implemented multi-sensor localization and fusion at Ford and deployed resilient state and parameter estimation pipelines for BEV and FCEV powertrains at Nikola, tackling communication delays, stochastic data loss, and model uncertainty. His industrial work at Trimble includes observer design and back-stepping LQI controllers for off-road agricultural vehicles, while academic projects produced physics-assisted GANs for CPS vulnerability assessment and autonomous racing systems. Comfortable bridging simulation, embedded C/VxWorks implementations, and ROS2-based reselection frameworks, he routinely turns research prototypes into deployable algorithms. Based in Broomfield, Colorado, he shares simulation code and research artifacts on GitHub, signaling a pragmatic, open approach to reproducible engineering.
8 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, ece, 4, Doctor of Philosophy - PhD, ece, 4 at Florida State University
Master's degree, Naval Architecture and Ocean Engineering, Master's degree, Naval Architecture and Ocean Engineering at Huazhong University of Science and Technology
Bachelor of Engineering - BE, Marine Engineering, Bachelor of Engineering - BE, Marine Engineering at Wuhan University of Technology