CORE Lab · University of California, Davis
Ph.D. Candidate in Mechanical Engineering (Robotics & Control)
“Ph.D. candidate in Robotics and Control at UC Davis, developing real-time game-theoretic MPC methods for interaction-aware autonomous systems. My work enables autonomous vehicles and robots to reason about the reactions of other agents and plan safe, efficient decisions or trajectories in shared environments.”
Autonomous Systems & Planning · Fall 2025
Developed reactive control and explored LiDAR, IMU, and wheel-encoder state estimation (EKF, SLAM, and an MLP estimator) for an autonomous racing stack in the AutoDRIVE simulator.
Key Result In simulation, the LiDAR-based reactive controller completed 10 consecutive collision-free laps on the Qualification Track; the team qualified in the AutoDRIVE RoboRacer Sim Racing qualification round.
Control, Dynamics & Estimation · Fall 2025
Analyzed how execution errors in one player's feedback Nash strategy affect the other player's performance, and developed a disturbance-aware compensation law for finite-horizon LQ games.
Key Result In a simulated two-cart system, compensated feedback reduced Player 1's cost by 10.7% and integrated squared error by 41.3% relative to uncompensated feedback under the same actuator lag.
Autonomous Systems & Planning · Spring 2025
Embedded a Python deep-Koopman vehicle model in MATLAB/Simulink for trajectory-tracking MPC of a high-fidelity CarSim vehicle.
Implementation Cross-language Python–Simulink MPC integration with the CarSim-modeled vehicle; the work was presented at IROS 2025.
Autonomous Systems & Planning · Summer 2023 – Summer 2024
Developed nonlinear and linearized MPC controllers in Julia for constrained vehicle racing and collision avoidance, including a dynamic-bicycle demonstration with six stationary obstacles.
Key Result In a 25 s closed-loop simulation, nonlinear MPC with a six-state dynamic bicycle model completed 2.29 laps past six stationary obstacles, with all 250 MPC updates accepted and no physical contact.
