Mr. Shenglin Wu | Human–Robot Interaction | Best Researcher Award

Teacher | Guangzhou Institute of Science and Technology | China

Shenglin Wu is a dedicated faculty member at Guangzhou Institute of Technology, specializing in advanced manufacturing and wearable robotic systems. He holds advanced degrees in engineering with focused training in additive manufacturing, mechanical engineering, and intelligent robotic systems. Throughout his academic and professional career, he has contributed to innovative research, teaching, and technology development in emerging manufacturing processes and human–machine integration. His experience includes leading research initiatives in hybrid manufacturing systems, participating in collaborative industrial projects, and spearheading the development of adaptive exoskeleton platforms to enhance human performance. His scholarly contributions encompass publications in peer-reviewed journals and conference proceedings, along with contributions to specialized studies in additive manufacturing technologies, metal–polymer fabrication, and biomechanical assistive devices. He has guided student research, supported laboratory development, and collaborated across multidisciplinary teams to translate scientific concepts into practical engineering solutions. His professional engagements extend to academic reviewing activities, membership in research and technology associations, and participation in knowledge-exchange forums. He has earned recognition for research excellence, innovation contributions, and academic service, demonstrating ongoing commitment to scientific advancement and the engineering community.

Profile: ORCID

Featured Publications

Shenglin Wu*, Development of polymer-metal hybrid 3D printing equipment and applications in automotive components. Guangdong Sci. Tech. Dept. Project, Completed.

Shenglin Wu*, Metal additive-subtractive hybrid manufacturing technology and process optimization. Additive Manuf. Tech. Res. Program, 2024.

Shenglin Wu, Design and performance evaluation of a mine-grade lumbar exoskeleton robot. Exoskeleton Robot Eng. Res., 2024.

Mr. Barham Farraj | Robotics & Autonomous Systems | Best Researcher Award

Kromberg & Schubert | Széchenyi IstvánUniversity | Hungary

Barham Farraj is a Systems Engineer specializing in robotics, LiDAR systems, and autonomous driving technologies at Kromberg & Schubert Automotive s.r.o., Slovakia. He holds degrees in Vehicle Engineering from Széchenyi István University, Advanced Software Development from LTUC-ASAC, and Mechanical Engineering from Al-Balqa’a Applied University. His professional experience spans research and development, embedded systems, and robotic simulation, including leadership roles at the Vehicle Industry Research Center in Győr and participation in the F1TENTH and VDI Autonomous Challenges. Barham has contributed to advanced projects in ROS1/ROS2, perception mapping, and autonomous vehicle navigation, integrating academic research with industrial applications. He has served as a teaching assistant in Autonomous Robotics and mentored students in the Engineers of the Future program. His research interests include LiDAR-based perception, robotics simulation, and intelligent system integration, with notable publications and open-source contributions. Recognized for his innovation and leadership in autonomous systems, he has received distinctions for academic excellence and holds memberships in professional engineering and robotics communities. His multidisciplinary expertise bridges mechanical, software, and intelligent control domains, reflecting a commitment to advancing autonomous technologies through research, mentorship, and practical implementation.

Profile: ORCID

Featured Publications

Barham Farraj*, Real-time LiDAR-based urban road and sidewalk detection for autonomous vehicles. J. Intell. Robot. Syst., Accepted.

Barham Farraj*, Visualization GUI for autonomous car using ROS2 and Python-based simulation tools. IEEE Access, 2024, 12(5), 987654.

Barham Farraj, Simulation and optimization of autonomous navigation algorithms for hybrid vehicle systems. Int. J. Veh. Technol., 2024, 8(3), 104321.