Dimitris Ziouzios | Robotics | Best Researcher Award

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Dr. Dimitris Ziouzios | Robotics | Best Researcher Award

Dr. Dimitris Ziouzios | Researcher | University of Western Macedonia | Greece

Dr. Dimitris Ziouzios is a dedicated researcher at the University of Western Macedonia, whose work spans robotics, embedded systems, and FPGA-based applications. His research emphasizes the integration of intelligent systems with real-world challenges such as environmental sustainability, smart waste management, and educational robotics. With over 23 completed and ongoing research projects, Dr. Ziouzios has made impactful contributions through innovations that merge automation, machine learning, and IoT technologies. His work has led to one patent, numerous collaborations with research institutions and industry partners including CERTH, the University of Wuppertal, and local municipalities, and over 14 publications indexed in SCI and Scopus journals. His research influence is reflected in a Google Scholar record of 514 citations with an h-index of 12, and a Scopus record of 345 citations with an h-index of 10. Beyond his technical research, Dr. Ziouzios contributes to advancing smart city infrastructures and robotics education, promoting environmental awareness and empathy through technology-driven learning. His consistent scholarly output and multidisciplinary collaborations highlight his strong commitment to innovation and applied research excellence.

Publication Profile

Google Scholar

Featured Publications

Ziouzios, D., Tsiktsiris, D., Baras, N., & Dasygenis, M. (2020). A distributed architecture for smart recycling using machine learning. Future Internet, 12(9), 141.

Ziouzios, D., Karlopoulos, E., Fragkos, P., & Vrontisi, Z. (2021). Challenges and opportunities of coal phase-out in western Macedonia. Climate, 9(7), 115.

Ziouzios, D., Baras, N., Balafas, V., Dasygenis, M., & Stimoniaris, A. (2022). Intelligent and real-time detection and classification algorithm for recycled materials using convolutional neural networks. Recycling, 7(1), 9.

Ziouzios, D., Rammos, D., Bratitsis, T., & Dasygenis, M. (2021). Utilizing educational robotics for environmental empathy cultivation in primary schools. Electronics, 10(19), 2389.

Ziouzios, D., Dasygenis, M. (2023). Effectiveness of the IoT in regional energy transition: The smart bin case study. Recycling, 8(1), 28.

 

Mr. Shen Tingli | Mimo Radar | Best Researcher Award

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Mr. Shen Tingli | Mimo Radar | Best Researcher Award

Mr. Shen Tingli | Naval University of Engineering | China

Academic Background

Shen Tingli completed his undergraduate studies in Navigation Engineering at Naval Aviation University, where he built a strong foundation in aerospace and maritime navigation technologies. He pursued advanced studies in Electronic Information at Naval University of Engineering, focusing on cognitive waveform design for MIMO radar systems. His academic work has been widely cited and is accessible through multiple platforms including Scopus, reflecting a growing recognition in radar signal processing research. His publications and conference documents demonstrate both theoretical innovation and practical applications in multi-target detection, and his h-index underscores the influence of his research contributions.

Research Focus

Shen Tingli’s research centers on cognitive waveform design for MIMO radar systems, with an emphasis on adaptive and intelligent signal optimization. He investigates techniques that improve radar detection performance, enhance multi-target resolution, and reduce interference in complex environments. His work integrates machine learning strategies with classical signal processing, advancing both theoretical frameworks and practical radar applications.

Work Experience

Shen Tingli has applied his expertise in electronic information and radar systems across academic and applied research roles. He has contributed to projects involving waveform design optimization and cognitive radar development, collaborating with interdisciplinary teams to enhance detection capabilities. His experience spans algorithm development, simulation studies, and performance evaluation in advanced radar systems, bridging the gap between theoretical research and engineering implementation.

Key Contributions

Shen Tingli is recognized for developing novel approaches to cognitive MIMO radar waveform design. He has contributed algorithms that improve adaptive detection accuracy and efficiency, particularly in multi-target scenarios. His work has facilitated the integration of gradient-based optimization with genetic algorithms, enabling more effective signal design under varying operational constraints. These contributions provide a foundation for future advancements in intelligent radar systems and defense applications.

Awards & Recognition

Shen Tingli has received commendations for his research excellence and innovation in radar signal processing. His contributions to adaptive waveform design have been acknowledged in peer-reviewed journals and by professional research communities, highlighting the impact and originality of his work.

Professional Roles & Memberships

He actively participates in scientific and engineering communities, contributing to the development of radar and electronic information research. His professional roles include collaborative research projects, peer review activities, and membership in relevant technical societies, promoting knowledge exchange and innovation within the field.

Publication Profile

Scopus | ORCID

Featured Publications

Shen, T., Lu, J., Zhang, Y., Wu, P., & Li, K. Waveform Design of a Cognitive MIMO Radar via an Improved Adaptive Gradient Descent Genetic Algorithm. Applied Sciences.

Impact Statement / Vision

Shen Tingli aims to advance the field of cognitive radar by developing intelligent waveform design methods that enhance detection and operational efficiency. His vision is to contribute to next-generation radar technologies that integrate adaptive learning, robust performance, and multi-target precision, driving innovation in both defense systems and civilian radar applications.

Zaid Allal | Machine Learning | Best Researcher Award

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Dr. Zaid Allal | Machine Learning | Best Researcher Award

Dr. Zaid Allal | LISTIC (Laboratory of Computer Science, Systems, Information and Knowledge Processing) | Morocco

Zaid Allal is a Moroccan researcher and doctoral candidate in computer science specializing in artificial intelligence applications for energy systems. With a solid foundation in mathematics and computing, he has built his academic and professional journey through a blend of education, research, and teaching. His work integrates machine learning with renewable energy systems, focusing on optimizing hydrogen energy technologies. Currently affiliated with the University of Savoie Mont Blanc and the LISTIC Laboratory in France, his research explores intelligent solutions for predictive maintenance, fault detection, and system stability. His dedication lies in bridging sustainable energy with advanced AI technologies.

Publication Profile

Scopus

ORCID

Google Scholar

Education Background

Zaid Allal holds a Master’s degree in Advanced Information Technology and Computing Applications from the University of Franche-Comté in France, graduating with distinction and honors. He earned a Bachelor’s degree in Mathematics and IT Systems from Mohammed First University in Oujda. Before his higher education, he received his Baccalaureate in Physical Sciences and Chemistry with honors. Additionally, he completed a certified training in Mathematics Education, coordinated with the Moroccan Ministry of Education. His strong academic background in both theoretical and applied domains provides a firm base for his research in AI and renewable energy integration.

Professional Experience

Zaid has over seven years of experience in mathematics education under the Moroccan Ministry of Education. Transitioning into research, he engaged in machine learning projects focused on renewable energy systems and hydrogen technologies at the University of Franche-Comté. Currently, he is a Ph.D. researcher at the University of Savoie Mont Blanc and contributes to the LISTIC Laboratory. His projects span predictive analytics, power consumption forecasting, and anomaly detection in smart grids. His work integrates theoretical AI models with practical energy sector challenges, contributing to research publications, international conferences, and innovative academic-industrial collaborations.

Awards and Honors

Zaid Allal has consistently demonstrated academic excellence throughout his career, receiving distinction and honors during both his undergraduate and postgraduate studies. His Master’s program recognized his outstanding performance with academic distinction. In addition to his formal qualifications, he has participated in several high-impact training initiatives, including NASA Space Apps competitions and AI ambassador programs. These accolades reflect his commitment to excellence in education, innovation, and technological advancement, highlighting his dedication to exploring and applying cutting-edge artificial intelligence methods within the energy and environmental sectors.

Research Focus

Zaid’s research centers on applying machine learning and deep learning techniques to address challenges in renewable energy systems and the hydrogen value chain. He focuses on areas such as predictive maintenance, fault and anomaly detection, power forecasting, and system optimization. His expertise extends to smart grids, hydrogen storage systems, and photovoltaic energy solutions. He employs explainable AI and reinforcement learning to develop sustainable, efficient, and interpretable models. By combining theoretical AI approaches with real-world energy applications, he aims to contribute to the advancement of intelligent and sustainable energy infrastructures.

Top  Publications

Explainable AI of Tree-Based Algorithms for Fault Detection and Diagnosis in Grid-Connected PV Systems
Published Year: 2025
Citation: 14

Review on ML Applications in Hydrogen Energy Systems
Published Year: 2025
Citation: 11

Power Consumption Prediction in Warehouses Using Variational Autoencoders and Tree-Based Regression Models
Published Year: 2024
Citation: 9

Efficient Health Indicators for RUL Prediction of PEM Fuel Cells
Published Year: 2024
Citation: 7

Machine Learning Algorithms for Solar Irradiance Prediction: A Comparative Study
Published Year: 2024
Citation: 6

Conclusion

Zaid Allal exemplifies the fusion of academic excellence, professional dedication, and research-driven innovation. With a strong foundation in mathematics and computing, he has evolved into a researcher committed to applying artificial intelligence in solving pressing energy challenges. His work across renewable energy, hydrogen systems, and smart grid technologies positions him as a valuable contributor to the evolving energy-tech landscape. Through ongoing research, publication, and collaboration, he continues to push the boundaries of sustainable innovation, striving to create data-driven and explainable solutions for the future of energy management and system optimization.

Wenwei Luo | Automation | Best Researcher Award

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Mr. Wenwei Luo | Automation | Best Researcher Award

Mr. Wenwei Luo at Institute of Logistics Science and Engineering, Shanghai Maritime University, China

Wenwei Luo is a passionate robotics researcher pursuing his Master’s degree at Shanghai Maritime University, specializing in control science and engineering. With a strong foundation in robotics engineering from Zhejiang Normal University, he has demonstrated academic excellence, technical proficiency, and innovative thinking in reinforcement learning and evolutionary robotics. Wenwei has published impactful research, led interdisciplinary projects, and earned recognition in national competitions. He possesses a unique combination of embedded systems expertise and AI-based control strategies, positioning him as a rising talent in intelligent robotics. His vision is to bridge adaptive learning and real-world robotics for autonomous systems. 🤖📚🔍

Publication Profile

Orcid

Academic Background

Wenwei Luo is currently pursuing a Master’s degree in Control Science and Engineering at Shanghai Maritime University under Associate Professor Bo Li, with a GPA of 3.84/4.0 and double First Academic Scholarships. His research interests span reinforcement learning, adaptive control, and evolutionary robotics. Previously, he earned his Bachelor’s degree in Robotics Engineering from Zhejiang Normal University under Associate Professor Hu Lan, graduating with a GPA of 3.40/4.0 and receiving a Third Academic Scholarship. Wenwei’s academic background blends strong theoretical knowledge with hands-on experience in intelligent systems and control engineering. 🧠🎓📈

Professional Experience

Wenwei has led and contributed to various high-impact robotics projects. As Principal Investigator, he developed a novel inner-outer loop framework for modular robots using reinforcement learning and evolutionary optimization. As Co-Investigator, he worked on intelligent drone navigation and pursuit-evasion for port defense. He also led a RoboMaster project, designing embedded software for a wheeled robot with Mecanum wheels and a shooting mechanism. His work integrates control algorithms, real-time systems, and AI-based decision-making, validated through both simulations and real-world experiments. His diverse project roles highlight both leadership and deep technical acumen. 🤖🧪🧑‍🔬

Awards and Honors

Wenwei has received several prestigious awards and honors throughout his academic career. At Shanghai Maritime University, he won the Third Prize in the 2022 “Huawei Cup” China Post-Graduate Mathematical Contest in Modeling. During his undergraduate years, he received the National Third Prize in the 2021 National College Students Robotics Competition (RoboMaster Event). He has also been awarded the First Academic Scholarship twice during his master’s program and the Third Academic Scholarship during his bachelor’s. These recognitions reflect his commitment to excellence and contributions to engineering and robotics research. 🥇🎖️📜

Research Focus

Wenwei’s research centers on intelligent control and adaptive robotics, specifically focusing on reinforcement learning-based control, evolutionary robotics, and adaptive dynamic programming. He has pioneered a hierarchical framework integrating genetic algorithms and deep RL (PPO) for optimizing morphology and control of modular robots. His work extends to autonomous UAV path planning and pursuit-evasion strategies using fuzzy logic, neural networks, and Lyapunov-based verification. His research leverages advanced tools such as JAX and GPU parallelism for real-time learning and optimization. Wenwei aims to develop scalable, autonomous systems capable of intelligent behavior in complex environments. 🧠📡🚀

Publication Top Notes

📄  Inner–Outer Loop Intelligent Morphology Optimization and Pursuit–Evasion Control for Space Modular Robot

 📅Year: 2025 | 📚 Journal: Actuators, Volume 14

Conclusion

Wenwei Luo is a highly promising early-career researcher whose academic excellence, innovative research, and practical contributions make him a strong contender for a Best Researcher Award. With a Master’s GPA of 3.84/4.0 and a strong undergraduate foundation, he has demonstrated consistent academic achievement. His research focuses on cutting-edge areas such as modular robotics, reinforcement learning, and evolutionary optimization, exemplified by his novel inner–outer loop architecture combining genetic algorithms and PPO for pursuit–evasion tasks. He has authored peer-reviewed publications, including a journal article in Actuators, and holds a patent alongside software copyrights, reflecting both theoretical and applied innovation. His technical skill set spans AI frameworks, embedded systems, and robotics platforms, and his leadership roles in multiple projects showcase his capability for independent and collaborative research. Combined with national competition awards and scholarships, Luo’s profile embodies the qualities celebrated by the Best Researcher Award.

 

Han-Jui Chang | Mechatronics Engineering | Best Researcher Award

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Prof Dr. Han-Jui Chang | Mechatronics Engineering | Best Researcher Award

Mechatronics Engineering, Shantou University, China

Han-Jui Chang (Henry Chang) is an accomplished engineer and academic with over 20 years of experience in the engineering and manufacturing industries. Currently an Associate Professor at Shantou University, he specializes in industrial production processes, engineering materials, and molding simulation. Henry has extensive experience in project design, customer service, and sales in the field of injection molding, as well as in research and development of new technologies. His career is marked by his dedication to both practical industry applications and academic excellence. 🌍🎓

Publication Profile

Strengths for the Award

  1. Extensive Experience in Research and Development: Han-Jui Chang has over 20 years of experience in engineering and manufacturing, particularly in the field of injection molding machines. His background in developing new products and technologies, combined with his expertise in analyzing and optimizing production processes, demonstrates a strong foundation in research and practical application.
  2. Notable Academic and Industrial Contributions: As an associate professor at various universities, Han-Jui Chang has contributed significantly to academia through teaching and mentoring students in subjects related to industrial production processes, engineering materials, and molding simulation. Additionally, he has published several research papers in high-impact journals (such as “Polymers” and “The International Journal of Advanced Manufacturing Technology”), which showcase his contributions to advancing knowledge in mechanical engineering and materials science.
  3. Recognition and Awards: Han-Jui Chang has been recognized for his expertise and contributions, as evidenced by his membership in the USA Sigma Xi Scientific Research Honor Society and multiple invitations as a guest speaker at renowned universities. His role as a patent holder and his numerous certifications in the field further highlight his innovation and leadership in research.
  4. Interdisciplinary Skills and Global Experience: His diverse skill set includes expertise in mechanical and electrical engineering, project management, and international trade. He has experience in both academic and industrial settings, spanning various countries and cultural contexts, making him a well-rounded candidate for the award.

Areas for Improvement

  1. Focus on Expanding Research Publications: Although Han-Jui Chang has several notable publications, expanding the volume and diversity of his research output could strengthen his case for the award. Collaborating with more researchers internationally and targeting a broader range of high-impact journals would enhance his visibility and influence in the research community.
  2. Strengthen Research Impact: While his research outputs are published in reputable journals, enhancing the citation impact and exploring interdisciplinary research areas could further demonstrate the broader relevance and applicability of his work. Engaging in more collaborative research projects with industry and academia could help achieve this.
  3. Further Involvement in Professional Organizations: While he is already a member of a prestigious research society, increasing his involvement in professional organizations (e.g., serving on editorial boards or leading committees) could demonstrate his commitment to contributing to the research community beyond his own work.

🎓 Education:

Doctorate in Industrial Education from Universidad Metropolitana (2015-2018) 🏫. Master of Science in Mechanical & Electrical Engineering from National Formosa University (2005-2007) 🔧📐.

💼 Experience:

Henry Chang has a diverse professional background with over 20 years in engineering and manufacturing. His roles have spanned from Service Engineer to Associate Professor, demonstrating his versatility in technical customer service, research and development, project management, and teaching. Henry has significant experience in stress analysis, plant layout, and developing innovative engineering solutions, particularly in the field of injection molding and mechanical systems. His international experience includes roles across Taiwan, China, and Europe, with a focus on customer relationship management, product development, and market expansion. 🌏💼

🔍 Research Focus:

Henry Chang’s research interests are centered on optimizing manufacturing processes, particularly through the application of advanced algorithms like NSGA-II and Kriging models. He is passionate about improving the quality and efficiency of manufacturing, specifically in areas such as injection molding, five-axis machine tools, and innovative material applications. His research combines practical engineering applications with advanced theoretical methods, contributing to the development of new technologies in the field. 🧬🔬

🏆 Awards and Honors:

Best Paper Award at the IEEE International Conference on Advanced Manufacturing (2018) 🏅. Recognized for his innovative contributions to the evaluation of five-axis machine tools and manufacturing optimization methods. 🥇

📚 Publication Highlights:

Chang, H., Sun, Y., Lu, S., & Zhang, G. (2023). Application of the NSGA-II Algorithm and Kriging Model to Optimize the Process Parameters for the Improvement of the Quality of Fresnel Lenses. Polymers, 15, 3403. Polymers Journal (SCI/Q1, IF=5.0) 🔍

Chang, H. (2018). Using a Cone Frustum Part in the Evaluation of Multiple Types of Five-Axis Machine Tools with the Taguchi Method. IEEE International Conference on Advanced Manufacturing. IEEE Conference (Best Paper Award) 🏅

Chang, H., Chen, S.-L., & Lee, P.-Y. (2017). Applying a Pyramid Part in the Performance Evaluation of Multiple Types of Five-Axis Machine Tools. The International Journal of Advanced Manufacturing Technology, pp1–7. 📖

Conclusion

Han-Jui Chang’s extensive experience in engineering and manufacturing, combined with his academic contributions and recognition in the field, make him a strong candidate for the Best Researcher Award. He has demonstrated expertise in developing innovative solutions, optimizing manufacturing processes, and contributing to the academic community. However, further enhancing his research impact through increased publications, citations, and broader professional engagement could solidify his standing as a top contender for the award. Overall, his achievements and contributions align well with the criteria for the award, and he has the potential to make an even more significant impact in the future.