Mustafa Mohammed Mustafa, PhD

Introduction

Mustafa Mohammed Mustafa holds a Ph.D. in Electrical Engineering with a specialization in robotics and control systems. At the University of Florida’s Center for Intelligent Machines and Robotics (CIMAR) he investigated active and passive perception frameworks that quantify sensing and state-estimation uncertainty and embed it in motion-planning algorithms, enabling robots to reason about risk and act confidently. His contributions include Lyapunov-based nonlinear, robust, and adaptive control laws that provide formal stability guarantees under modeled uncertainties.

Current research blends artificial intelligence, machine learning, deep visual perception, and reinforcement learning across the perception–planning–control pipeline to enhance the reliability, efficiency, and safety of field robots operating in unstructured environments. He also supervises graduate and undergraduate projects in these areas.

Professional practice spans renewable-energy systems, industrial automation projects, vehicle X-ray inspection platforms, and agricultural UAV deployments. Building-sector experience covers full electrical load design, including load calculations, cable sizing, protective-device selection together with lighting and power layouts, panel schedules, and documentation for commercial and residential facilities.

Education

• - Ph.D. (Collaborative Program) in Electrical Engineering, Robotics and Control Systems, Salahaddin University-Erbil and University of Florida, 2021
• - M.Sc. in Electrical Engineering, Electronics and Telecommunications, Salahaddin University-Erbil, 2015
• - B.Sc. in Electrical Engineering, Electronics and Telecommunications, Salahaddin University-Erbil, 2009

Research

Dr. Mustafa’s research focuses on perception-driven autonomy for field robots. Core topics include probabilistic modeling of sensing and state-estimation uncertainties; integrating deep visual perception and reinforcement learning into the perception–planning–control loop; and designing Lyapunov-based nonlinear, robust, and adaptive control laws that guarantee stability in uncertain, dynamic environments. Further interests span risk-aware motion planning, robotic perception with AI, cyber-physical systems, multi-robot coordination, multi-sensor fusion, and data-driven methods that enhance reliability and safety across ground, aerial, and other robotic platforms.

Professional Membership

• Member, Institute of Electrical and Electronics Engineers (IEEE)
• Member, IEEE Robotics and Automation Society (RAS)
• Member, IEEE Control Systems Society (CSS)
• Member, IEEE RAS Technical Committee on Computer & Robot Vision