Abbas Alili

I am an innovative and results-driven Electrical and Computer Engineer specializing in wearable robotic solutions aimed at enhancing mobility and balance. Currently, I am pursuing a Ph.D. at North Carolina State University, focusing on developing user-centric prosthetic and exoskeleton systems using advanced control algorithms and machine learning-based data analysis. My technical expertise includes proficiency in MATLAB, Python, C/C++, and Simulink, with a strong background in PID, Fuzzy Logic, Reinforcement Learning, and machine learning control systems.

In my recent work, I have led interdisciplinary projects that demonstrate significant advancements in assistive and rehabilitation applications. This includes employing machine learning techniques to analyze real-time sensor data, optimize control parameters, and enhance the performance of robotic systems in dynamic environments. I have successfully designed and implemented control architectures for bilateral hip exoskeletons and developed machine learning algorithms that improve gait balance in individuals with mobility impairments. My leadership in these projects has resulted in numerous publications and patents, showcasing my ability to translate research into practical engineering solutions.

I am passionate about leveraging my technical skills and research insights to improve the quality of life for individuals with mobility impairments. I thrive in dynamic environments, adeptly identifying issues, developing innovative solutions, and managing multiple projects to meet deadlines efficiently. My strong communication skills are demonstrated through my teaching and presentations, making complex concepts accessible to diverse audiences.

Additionally, I have a keen interest in human-machine interaction, human-robot coordination, and shared robotics control. My work in these areas includes developing intuitive user interfaces and control strategies that enhance the seamless integration of robotic systems with human users. This focus, coupled with data-driven insights from machine learning, ensures that the robotic solutions I develop are not only technically robust but also user-friendly and effective in real-world applications.