About

I am a Ph.D. candidate in Mechanical Engineering (Robotics & Control) at the MAE Department in University of California, Davis, working in the CORE Lab on interaction-aware trajectory planning and decision-making for non-cooperative multi-agent systems, with a primary focus on autonomous driving and racing.

My research develops real-time game-theoretic control methods that enable autonomous agents to explicitly reason about how surrounding agents will react to their actions. Specifically, I am developing a computationally efficient methodology to approximate Nash equilibrium solutions and embed it within a receding-horizon/MPC framework for interaction-aware closed-loop trajectory planning.

Alongside algorithm design, I place strong emphasis on implementation and evaluation. I build simulation pipelines and autonomy stacks using Python, ROS2, and C++, and validate my methods in realistic environments such as CARLA and AutoDrive simulator, including custom traffic scenarios and racing environments.

While my work is motivated by autonomous vehicles, the underlying ideas extend naturally to robotic systems operating in shared, interactive environments, including multi-robot coordination, mobile robotics, and human–robot interaction, where agents must make decisions under mutual influence and uncertainty.

Before pursuing my Ph.D., I studied Mechanical Engineering at the University of Tehran, where I mostly worked on mechatronics and control. In my B.Sc. thesis, I designed and prototyped a soft magnetic tactile sensor for robotics applications. I also designed and built embedded control and calibration systems (STM32/Arduino), and developed electronics and firmware for real-time sensing and control.

I also enjoy mentoring and technical leadership, including supervising student teams in autonomous racing and behavior prediction projects.