Mr Al Jaber Mahmud | Human-Robot Collaboration | Best Researcher Award

Mr Al Jaber Mahmud , George Mason University , United States

Al Jaber Mahmud 🎓 is a dedicated researcher and Ph.D. candidate in Electrical and Computer Engineering at George Mason University, Virginia 🇺🇸, advised by Dr. Xuan Wang. With a strong academic background and research expertise in human-robot interaction 🤖🧑‍🤝‍🧑, Mahmud is passionate about enabling intelligent and adaptive collaboration between robots and humans. He has published in high-impact journals and conferences 📝📚. Currently, he is developing cutting-edge algorithms and deploying them on real robotic systems 🤖⚙️. Mahmud aims to bridge the gap between theoretical control strategies and real-world robotic applications 🌍🛠️.

Professional Profile

ORCID

Education & Experience

Al Jaber Mahmud earned his B.Sc. in Electrical and Electronic Engineering from Islamic University of Technology, Bangladesh 🇧🇩 in 2022 🎓, and his M.S. in Electrical Engineering (Controls & Robotics) from George Mason University 🇺🇸 in 2025 📘. He is currently pursuing a Ph.D. in Electrical and Engineering 🧠🔬, expected to complete in Dec 2027. Mahmud works as a Graduate Research Assistant 🧪🤖 at George Mason, focusing on advanced human-robot collaboration. He also served as a Graduate Teaching Assistant 👨‍🏫 for multiple engineering courses. His academic and professional journey highlights his commitment to robotics innovation 🔧📈.

Professional Development

Mahmud’s professional development has been shaped through hands-on robotics research 🔍🤖, teaching experiences 👨‍🏫, and technical proficiency in control theory and deep learning 🧠📊. At George Mason University, he contributed to real-world robot deployment using the Fetch Mobile Manipulator 🤖🦾. He has demonstrated excellence in both independent research and collaborative projects 🧑‍🔬🤝, presenting at top-tier robotics conferences like IROS and ICPS 🌐📢. Mahmud consistently integrates theory with application by optimizing robotic systems for safety, efficiency, and adaptability 🎯⚙️. His commitment to innovation and mentorship makes him a rising star in the field of intelligent robotics 🌟🛠️.

Research Focus

Mahmud’s research focus lies at the intersection of Human-Robot Interaction 🤝🤖, Deep Learning 🧠📚, Optimal Control 🎛️, and Reinforcement Learning 🎯. He designs robust control frameworks that model human uncertainty 🤔📈 and enable adaptive robotic behavior for collaborative tasks. His work tackles real-world challenges in human-robot co-transportation and manipulation using Model Predictive Control (MPC) and learning-based techniques 🔁⚙️. By integrating perception, decision-making, and interaction modeling, Mahmud advances autonomous systems capable of safe, effective collaboration with humans in uncertain environments 🧑‍🔬🌍. His approach blends theory with implementation for intelligent robotic autonomy 🚀🦾.

Awards and Honors

Mahmud’s excellence is reflected through his academic milestones and research achievements 🎓🏅. He successfully passed his Technical and Research Qualifying Exams in 2024 📚✅. His journal article in Electronics and conference papers at IROS 2025 and ICPS 2024 have gained wide recognition in the robotics community 🌟📝. His deep learning-driven robotic control systems have been implemented on real hardware 🤖🔧, showcasing innovation and impact. With a consistent academic record (CGPA > 3.8) 📊🎖️ and global collaboration with leading researchers 🌐🤝, Mahmud stands out as a promising scholar contributing significantly to the future of robotics 🚀🌍.

Publication Top Notes

1.Title: DARC: Disturbance-Aware Redundant Control for Human–Robot Co-Transportatio

Journal: Electronics, Vol. 14, No. 12, June 2025
DOI: 10.3390/electronics14122480
Contributors: Al Jaber Mahmud, Amir Hossain Raj, Duc M. Nguyen, Xuesu Xiao, Xuan Wang
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)

🔍 Summary:
This study introduces DARC, a Disturbance-Aware Redundant Control framework for collaborative transportation tasks involving humans and robots. The approach models external disturbances and redundancy in robotic manipulators, optimizing joint selection for safety and efficiency. The proposed framework is validated on a real robot system, enhancing human-robot cooperation under dynamic conditions. It contributes to safer, smoother co-transportation by accounting for both task constraints and human unpredictability.

2.Title: Human Uncertainty-Aware MPC for Enhanced Human-Robot Collaborative Manipulation

Conference: 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS)
Date: May 12, 2024
DOI: 10.1109/icps59941.2024.10640020
Contributors: Al Jaber Mahmud, Duc M. Nguyen, Filipe Veiga, Xuesu Xiao, Xuan Wang

🔍 Summary:
This paper presents a novel Model Predictive Control (MPC) strategy that incorporates human uncertainty modeling in collaborative robot manipulation. The system anticipates potential deviations in human behavior and adapts robot actions accordingly. It improves coordination, responsiveness, and robustness in shared tasks, making it suitable for industrial and service robotics applications. Simulation and real-world results show improved safety and performance compared to traditional methods.

3.Title: Optimal Control and Performance Enhancement of DC-DC Bidirectional SEPIC Converter
Conference: 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
Date: October 26, 2022
DOI: 10.1109/uemcon54665.2022.9965670
Contributors: Al Jaber Mahmud, Mehedi Hasan Mithun, Md. Ashik Khan, Fahim Faisal, Mirza Muntasir Nishat, Md. Ashraful Hoque

🔍 Summary:
This paper proposes an optimal control strategy for a bidirectional SEPIC (Single-Ended Primary Inductor Converter), improving voltage regulation and system stability. It compares performance under varying loads and control schemes. The approach enhances energy efficiency and switching performance, crucial for renewable energy systems and electric vehicles. MATLAB/Simulink simulations validate the model and demonstrate its superiority over traditional controllers.

4.Title: Performance and Comparative Analysis of PI and PID Controller-based Single Phase PWM Inverter Using MATLAB Simulink for Variable Voltage
Conference: 2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)
Date: June 1, 2022
DOI: 10.1109/icaect54875.2022.9807857
Contributors: Al Jaber Mahmud

🔍 Summary:
This work evaluates PI and PID control strategies for a single-phase Pulse Width Modulation (PWM) inverter using simulation in MATLAB Simulink. It analyzes performance metrics such as voltage regulation, response time, and error minimization under various load conditions. Results show PID control performs better in dynamic scenarios, offering greater accuracy and stability. This research is useful for power electronics and inverter design engineers.

5.Title: Firefly Algorithm Based Optimized PID Controller for Stability Analysis of DC-DC SEPIC Converter
Conference: 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
Date: January 10, 2022
DOI: 10.1109/uemcon53757.2021.9666555
Contributors: Al Jaber Mahmud

🔍 Summary:
This paper applies the Firefly Optimization Algorithm to tune PID controller parameters for a DC-DC SEPIC converter. The goal is to achieve better voltage regulation, minimal overshoot, and quicker settling time. Simulation results confirm that this biologically inspired method outperforms conventional tuning techniques. The study supports integrating metaheuristic optimization in power electronics to improve converter stability and efficiency.

Conclusion:

Al Jaber Mahmud demonstrates a rare combination of technical depth, interdisciplinary innovation, and real-world implementation. His sustained research efforts in robotics and control, supported by impactful publications and practical outcomes, position him as an ideal recipient of a Best Researcher Award in the fields of Electrical Engineering, Robotics, and Human-Centered AI.