About

I earned my Ph.D. in Electrical and Computer Engineering from the University of Michigan–Dearborn, specializing in machine learning for autonomous driving and robotics.

My research focuses on developing and evaluating learning-based systems for autonomous and teleoperated vehicles, particularly under practical challenges such as perception and communication latency, uncertainty, and system integration. My work combines machine learning with control, robotics, and simulation-based evaluation to study how autonomous systems behave under these constraints.

My experience spans:

• Machine learning for autonomous driving, including imitation learning, end-to-end learning, predictive modeling, and world models

• Vision-based control, including lane keeping, lateral control, and trajectory tracking

• Robotics and autonomous-system development using ROS/ROS2

• Closed-loop simulation and validation using CARLA and Gazebo

• Computer vision and perception, including camera–LiDAR calibration and sensor fusion

I am particularly interested in developing machine learning systems that move beyond model-level performance to reliable operation within complete robotic and autonomous systems.

Feel free to reach out regarding opportunities or collaborations in machine learning, robotics, and autonomous systems.