Zohaib Khan | Engineering and Technology | Excellence in Research Award

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Dr. Zohaib Khan | Engineering and Technology | Excellence in Research Award

Jiangsu University | China

Zohaib Khan is a PhD candidate in Control Science and Engineering at Jiangsu University, specializing in machine learning–driven perception and control for intelligent robotic systems. With over six years of research and applied experience, his work bridges deep learning, computer vision, and real-time robotic control, with a strong focus on agricultural robotics and precision farming. He has authored more than 10 high-impact SCI-indexed journal articles, achieving an h-index of 6, with 11 research documents and 121 citations. His research interests include object detection and segmentation (YOLO series, transformer-based models, RCNN), vision-guided navigation, precision spraying, and robust control of autonomous robots in unstructured environments. Zohaib has contributed as both first and co-author to leading journals such as Computers and Electronics in Agriculture, Agronomy, Sensors, and IEEE Transactions on Industrial Electronics. Alongside research, he has extensive experience supervising student projects and developing real-time AI pipelines using Python, PyTorch, OpenCV, ROS, and C/C++. His academic excellence is recognized through multiple national and international awards, including innovation, debate, and research excellence honors. Overall, Zohaib Khan represents a strong blend of theoretical rigor and practical AI deployment, aiming to advance large-scale industrial and agricultural perception systems.

Citation Metrics (Scopus)

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Ibrahim Khalil Kabir | Engineering and Technology | Best Researcher Award

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Mr. Ibrahim Khalil Kabir | Engineering and Technology | Best Researcher Award

King Fahd University of Petroleum and Minerals | Saudi Arabia

Ibrahim Khalil Kabir is a control and robotics researcher working at the intersection of control theory and artificial intelligence, with a strong focus on learning-based robotics, socially aware navigation, and human–robot interaction. He holds an MSc in Systems and Control Engineering and a BEng in Mechatronics Engineering, with a solid academic record and advanced training in autonomous systems. His research experience spans graduate teaching and research assistantships, where he contributed to robot path planning, navigation, and hands-on laboratory instruction using real robotic platforms. His scholarly output includes peer-reviewed journal and conference publications covering UAV control, mobile robot navigation, deep reinforcement learning, and socially aware robotic systems. According to Google Scholar, his research profile reflects an emerging h-index supported by multiple indexed documents and a steadily growing citation count, indicating increasing impact in robotics and intelligent control research. His work has appeared in reputable venues such as IEEE Access, Machine Learning and Knowledge Extraction, and IEEE conferences. He has received several academic honors, including national merit scholarships and highest GPA awards. Overall, his research trajectory demonstrates a strong foundation and growing influence in intelligent robotics, positioning him well for advanced doctoral research in learning-enabled autonomous systems.

Citation Metrics (Google Scholar)

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11

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2

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Florin Popister | Mechanical Engineering | Engineering Research Excellence Award

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Assoc. Prof. Dr. Florin Popister | Mechanical Engineering | Engineering Research Excellence Award

Technical University of Cluj-Napoca | Romania

Assoc. Prof. Dr. Eng. Florin Popișter is an accomplished researcher and academic in industrial engineering, mechanical design, and robotics. He earned his Ph.D. in Industrial Engineering from the Technical University of Cluj-Napoca, where he currently serves as an Associate Professor, contributing to teaching, research, and supervision in areas such as CAD/CAM systems, industrial robots, reverse engineering, and advanced manufacturing. His professional experience spans both academia and industry, including significant contributions as a design engineer at Gühring Romania, where he developed customized PCD/PCBN tools for the automotive sector. His research includes 3D printing technologies, robotic mechanisms, toolpath generation, workspace optimization, and digital manufacturing, with multiple Q1 journal publications in Polymers, Applied Sciences, and Mathematics. He has led and contributed to national and international research contracts, including H2020 Smart2 and projects focused on automation systems for the pharmaceutical industry. His achievements include a Best Paper Award, participation in Prototypes for Humanity, and invited professorships in Europe. His work continues to advance intelligent manufacturing, Industry 4.0 applications, and smart robotic systems.

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Featured Publications

Popescu, D., Dragomir, D., Popișter, F., & Dragomir, M. (2025). AI alignment to enhance production processes performance and resilience. In Book chapter. Springer.

Dragomir, M., Apolțan, D., Cioșan, A., & Popișter, F. (2025). Assessing the potential of emerging digital technologies to transform the production sector. Annals of the Academy of Romanian Scientists Series on Economy, Law and Sociology.

Popișter, F., Ciudin, P., Dragomir, M., & Goia, H. Ș. (2025). From assistive to intelligent: The development of a low-cost smart crutch system. In Book chapter. Springer.

Popișter, F. (2025). Experimental study of comprehensive performance analysis regarding the dynamical/mechanical aspects of 3D-printed UAV propellers and sound footprint. Polymers, 17(11), 1466.

Popișter, F., Goia, H. Ș., & Ciudin, P. (2025). Influence of polymers surface roughness on noise emissions in 3D-printed UAV propellers. Polymers, 17(8), 1015.

Chih-Lyang Hwang | Electrical Engineering | Best Researcher Award

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Prof. Chih-Lyang Hwang | Electrical Engineering | Best Researcher Award

National Taiwan University of Science and Technology | Taiwan

Dr. Chih-Lyang Hwang (SM’08) is a distinguished researcher and academic in the field of electrical and mechanical engineering, currently serving as a Research Fellow at the Intelligent Robot Center, National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan. He earned his Ph.D. in Mechanical Engineering from Tatung Institute of Technology  and subsequently held professorial positions at Tatung Institute of Technology, Tamkang University, and NTUST. With an extensive academic career spanning over three decades, he has contributed significantly to robotics, fuzzy neural modeling, nonlinear control, and human–robot interaction. His research also encompasses distributed visual and wireless localization, UAV control, and emotion recognition. Dr. Hwang has been a Visiting Scholar at Georgia Institute of Technology and Auburn University, broadening his international academic collaborations. He has authored numerous influential journal and conference papers, amassing over 3,383 citations, 533 documents, and an H-index of 29. Recognized among the world’s top 2% scientists by Stanford University for multiple years, he has also received Excellent and Outstanding Research Awards from NTUST and 2024. His enduring contributions continue to advance intelligent robotics and control systems research globally.

Profile : Google Scholar

Featured Publications

Hwang, C.-L., Yang, C.-C., & Hung, J.-Y. (2017). Path tracking of an autonomous ground vehicle with different payloads by hierarchical improved fuzzy dynamic sliding-mode control. IEEE Transactions on Fuzzy Systems, 26(2), 899–914.

Hwang, C.-L., Jan, C., & Chen, Y.-H. (2001). Piezomechanics using intelligent variable-structure control. IEEE Transactions on Industrial Electronics, 48(1), 47–59.

Hwang, C.-L., Chang, L.-J., & Yu, Y.-S. (2007). Network-based fuzzy decentralized sliding-mode control for car-like mobile robots. IEEE Transactions on Industrial Electronics, 54(1), 574–585.

Hwang, C.-L., Chiang, C.-C., & Yeh, Y.-W. (2013). Adaptive fuzzy hierarchical sliding-mode control for the trajectory tracking of uncertain underactuated nonlinear dynamic systems. IEEE Transactions on Fuzzy Systems, 22(2), 286–299.

Hwang, C.-L. (2004). A novel Takagi–Sugeno-based robust adaptive fuzzy sliding-mode controller. IEEE Transactions on Fuzzy Systems, 12(5), 676–687

Marius Šumanas | Computer Vision | Best Researcher Award

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Assist. Prof. Dr Marius Šumanas | Computer Vision | Best Researcher Award

Vilnius Tech | Lithuania

Dr. Marius Šumanas is an Associate Professor in the Department of Mechatronics, Robotics, and Digital Manufacturing at Vilnius Gediminas Technical University (VILNIUS TECH).  His research interests encompass robotics, machine learning, and digital manufacturing. Dr. Šumanas has authored several publications, including a study on improving positioning accuracy of articulated robots using deep Q-learning algorithms. He has also participated in various conferences, presenting topics related to machine learning applications in robotics. In addition to his academic roles, Dr. Šumanas has practical experience in the industry. Since 2018, he has been working as a Process Engineer and ERP Systems Business Analyst at MB Pramones algoritmas. He has also held positions in marketing and sales management in previous years. Dr. Šumanas’ work bridges the gap between theoretical research and practical application, contributing to advancements in robotics and digital manufacturing.

Profile : Orcid

Featured Publications

Andrijauskas, I., Šumanas, M., Dzedzickis, A., Tanaś, W., & Bučinskas, V. (2025). Computer vision-based optical odometry sensors: A comparative study of classical tracking methods for non-contact surface measurement. Sensors.

Šumanas, M., Treciokaite, V., Čerškus, A., Dzedzickis, A., Bučinskas, V., & Morkvenaite-Vilkonciene, I. (2022). Sitting posture monitoring using Velostat based pressure sensors matrix. In Smart Sensor Systems (pp. 1–12). Springer.

Bučinskas, V., Dzedzickis, A., Šumanas, M., Sutinys, E., Petkevicius, S., Butkiene, J., Virzonis, D., & Morkvenaite-Vilkonciene, I. (2022). Improving industrial robot positioning accuracy to the microscale using machine learning method. Machines, 10(10), 940.

Šumanas, M., Urbonis, D., Petronis, A., Stankaitis, S., Januškevičius, T., Iljin, I., & Dzedzickis, A. (2021). Evaluation of motion characteristics using absolute sensors. In Advances in Mechatronics and Intelligent Robotics (pp. 1–12). Springer.

Šumanas, M., Petronis, A., Urbonis, D., Januskevicius, T., Rasimavicius, T., Morkvenaite-Vilkonciene, I., Dzedzickis, A., & Bučinskas, V. (2021, April 22). Determination of excavator tool position using absolute sensors. In 2021 Open Conference of Electrical, Electronic and Information Sciences (eStream). IEEE.

Alessio Fasano | Robotics | Best Researcher Award

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Dr Alessio Fasano | Robotics | Best Researcher Award

Research Engineer in Don Carlo Gnocchi Foundation ONLUS at Italy

Alessio Fasano is a dedicated research engineer specializing in biomedical engineering and neurorobotics. Born on June 20, 1991, in Italy, he is currently affiliated with the Fondazione Don Carlo Gnocchi ONLUS, where he focuses on advancing rehabilitation technologies. With a robust academic background, including a Ph.D. in BioRobotics from Scuola Superiore Sant’Anna, Fasano’s work is marked by a commitment to enhancing patient care through innovative robotics and data analysis. He has co-authored numerous peer-reviewed publications and participates actively in international conferences, emphasizing collaboration and research excellence.

Profile

Orcid

Strengths for the Award

  1. Extensive Research Experience:
    • Alessio has a robust background in biomedical engineering and neurorobotics, demonstrated by his roles in prominent projects such as the Human Brain Project and Fit4MedicalRobotics. His ongoing contributions to innovative research in rehabilitation robotics underline his commitment to advancing the field.
  2. Strong Academic Credentials:
    • With a Ph.D. in BioRobotics and multiple degrees with honors from reputable institutions, Alessio showcases a solid academic foundation that supports his research activities. His educational achievements are complemented by a consistent record of excellence.
  3. Publication Record:
    • Alessio has co-authored several impactful publications in peer-reviewed journals, indicating his active engagement in cutting-edge research. His work on postural control in children with movement disorders and rehabilitation protocols after stroke highlights significant contributions to both clinical and theoretical aspects of biomedical engineering.
  4. Technical Proficiency:
    • Proficient in advanced programming (Python, MATLAB), data analysis, and computational modeling, Alessio possesses the technical skills essential for conducting high-level research. His familiarity with neurophysiological signal analysis and machine learning further enhances his research capabilities.
  5. Collaboration and Leadership:
    • His involvement in interdisciplinary projects and collaboration with clinical professionals exemplifies his ability to work effectively within teams. Alessio’s experience in managing projects and supervising students demonstrates his leadership potential.

Areas for Improvement

  1. Broader Networking:
    • While Alessio has a solid publication record, expanding his professional network could enhance his visibility in the global research community. Engaging in more international collaborations may open up additional funding opportunities and broaden the impact of his work.
  2. Grant Writing Experience:
    • Although Alessio has experience in grant applications, further development in this area could strengthen his capacity to secure funding for future projects. Participating in workshops or mentorship programs focused on grant writing could be beneficial.
  3. Public Engagement:
    • Increasing his engagement with public audiences and non-academic stakeholders could enhance the societal impact of his research. Alessio might consider public talks or outreach initiatives to share his findings and promote the importance of rehabilitation technologies.

Education

Alessio Fasano holds a Ph.D. in BioRobotics from Scuola Superiore Sant’Anna, awarded with merits in November 2022. Prior to this, he completed his Master’s in Biomedical Engineering at the University of Naples Federico II in 2018, graduating with top honors (110/110 cum laude). His academic journey began with a Bachelor’s degree in Biomedical Engineering, also from the University of Naples Federico II, where he graduated with the same distinction. Throughout his education, Fasano has developed a strong foundation in robotics, neural systems, and biomedical applications.

Experience

Currently a research engineer at Fondazione Don Carlo Gnocchi ONLUS, Alessio Fasano engages in projects related to robotic rehabilitation. His previous role as a research associate at the Instituto di BioRobotica involved collaboration on significant projects like the Human Brain Project, where he analyzed EEG data and developed neural network models. His earlier experience includes an internship at Maastricht University, focusing on functional magnetic resonance imaging. Throughout his career, Fasano has managed projects, supervised students, and contributed to various research initiatives, emphasizing his commitment to advancing medical technologies.

Research Focus

Alessio Fasano’s research focuses on the integration of robotics in neurorehabilitation, utilizing advanced technologies like EEG and biomechanical modeling to enhance patient recovery. He explores the interplay between human neural systems and robotic interfaces, aiming to develop customized rehabilitation protocols. His work extends to analyzing biomechanical movement patterns and investigating the efficacy of digital rehabilitation tools in clinical settings. Through collaboration on international projects, Fasano seeks to bridge the gap between technology and clinical practice, ultimately improving outcomes for patients with movement disorders.

Publication Top Notes

  • Restoring of Interhemispheric Symmetry in Patients with Stroke Following Bilateral or Unilateral Robot-Assisted Upper-Limb Rehabilitation: A Pilot Randomized Controlled Trial 🤖🧠
  • Rehabilitation with and without Robot and Allied Digital Technologies (RADTs) in Stroke Patients: A Study Protocol for a Multicentre Randomised Controlled Trial on the Effectiveness, Acceptability, Usability, and Economic-Organizational Sustainability of RADTs from Subacute to Chronic Phase (STROKEFIT4) 🏥📊
  • Implementation of a Robot-Mediated Upper Limb Rehabilitation Protocol for a Customized Treatment after Stroke: A Retrospective Analysis 🦾📈
  • Assessment of Postural Control in Children with Movement Disorders by Means of a New Technological Tool: A Pilot Study 👶⚖️
  • Modeling Vestibular Afferents for Neuromorphic Sensing and Eye Movement Control 🧪👁️
  • Maximum Downward Slope of Sleep Slow Waves as a Potential Marker of Attention-Deficit/Hyperactivity Disorder Clinical Phenotypes 💤🧠
  • Reaching and Grasping Movements in Parkinson’s Disease: A Review ✋📖
  • Modeling the Brain and Its Pathologies 🧠🔍
  • Maximum Downward Slopes of Sleep Slow Waves as a Potential Marker of Attention Deficit Hyperactivity Disorder Clinical Phenotypes 💤📉
  • The Neurorobotics Platform: Virtual Bodies for Biologically Realistic Brain Models and Vice Versa 🤖🌐

Conclusion

Alessio Fasano stands out as a candidate for the Best Researcher Award due to his extensive research experience, strong academic background, and impressive publication record. His technical skills and collaborative spirit further bolster his qualifications. By focusing on expanding his network, enhancing grant writing capabilities, and engaging with the public, Alessio can continue to elevate his contributions to biomedical engineering and neurorobotics, ultimately benefiting a wider audience and enhancing his career trajectory.