Prof. Dr. Gary Wong | Computer Science Education | Research Excellence Award

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Prof. Dr. Gary Wong | Computer Science Education | Research Excellence Award

Faculty of Education | The University of Hong Kong | Hong Kong

Prof. Dr. Gary Ka Wai Wong is a leading scholar in computational thinking, computer science education, and artificial intelligence (AI)–enhanced learning, widely recognized for his interdisciplinary contributions to digital literacy, immersive learning environments, and K–12 STEM innovation. His research explores how constructionist pedagogies, data-driven precision learning, and extended reality experiences foster cognitive development, problem-solving, and twenty-first century competencies in learners. He has played a pivotal role in shaping international frameworks for digital literacy and AI education, including large-scale studies that examine teachers’ technological adoption, students’ computational thinking development, and school-level implementation of coding and STEM initiatives. His body of work reflects strong methodological diversity, spanning design-based research, meta-analysis, psychometric assessment, and mixed-methods evaluations of learning technologies. His scholarly influence is demonstrated by substantial global citations: Scopus records over 1,218 citations across 1,071 citing documents with an h-index of 18, while Google Scholar reports more than 3,111 citations with an h-index of 24 and 48 i10-index publications. His contributions extend to immersive virtual reality in science learning, digital competence assessment, and early-age unplugged and plugged computational thinking activities. His research continues to inform international policy, curriculum innovations, and empirical understandings of how AI, coding, and digital tools can be meaningfully integrated into education to cultivate future-ready learners.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications 

Wong, G. K. W., Ma, X., Dillenbourg, P., & Huan, J. (2020). Broadening artificial intelligence education in K–12: Where to start? ACM Inroads, 11(1), 20–29.

Saxena, A., Lo, C. K., Hew, K. F., & Wong, G. K. W. (2020). Designing unplugged and plugged activities to cultivate computational thinking: An exploratory study in early childhood education. The Asia-Pacific Education Researcher, 29(1), 55–66.

Wong, G. K. W., & Cheung, H. Y. (2020). Exploring children’s perceptions of developing twenty-first century skills through computational thinking and programming. Interactive Learning Environments, 28(4), 438–450.

Law, N. W. Y., Woo, D. J., De la Torre, J., & Wong, K. W. G. (2018). A global framework of reference on digital literacy skills for indicator 4.4.2. UNESCO Institute for Statistics.

Lui, A. L. C., Not, C., & Wong, G. K. W. (2023). Theory-based learning design with immersive virtual reality in science education: A systematic review. Journal of Science Education and Technology, 32(3), 390–432.

Mr. Ro-Yu Wu | Computer Science | Research Excellence Award

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Mr. Ro-Yu Wu | Computer Science | Research Excellence Award

Professor | Lunghwa University of Science and Technology | Taiwan

Mr. Ro-Yu Wu is a Taiwan-based researcher recognized for his contributions to combinatorial algorithms, graph theory, and efficient data structures, particularly within the domains of ranking and unranking methods, Hamiltonian graph properties, and algorithmic generation of combinatorial objects. As a productive scholar in theoretical computer science and industrial management, his work emphasizes the design of loopless, lexicographic, and Gray code–based algorithms that enhance computational efficiency and fault-tolerant communication in complex networks. His research is well acknowledged in the global academic community, as reflected in his Scopus record with 45 documents, 308 citations across 189 citing works, and an h-index of 10. On ResearchGate, he maintains an active profile with 47 publications, over 6,300 reads, and 367 citations. These metrics highlight his growing influence, especially in areas involving structured graph traversal, spanning-tree generation, and the analytical foundations supporting optimization and data broadcasting systems. His work frequently explores practical algorithmic strategies for high-performance computing environments, providing innovative insights for fault-tolerant network design and combinatorial enumeration. Mr. Wu’s collaborations span multi-author research teams, contributing to advancements published in high-impact venues such as Theoretical Computer Science, The Journal of Supercomputing, Journal of Combinatorial Optimization, and Optimization Letters. His ongoing research continues to shape efficient computational paradigms for combinatorial structures, making him a relevant contributor to the future of theoretical and applied algorithmic studies.

Profile

Scopus

Featured Publications 

Xie, Z., Wu, R.-Y., & Shi, L. (2025). Ranking and unranking algorithms for derangements based on lexicographical order. Theoretical Computer Science.

Pai, K.-J., Wu, R.-Y., Peng, S. L., & Chang, J. M. (2023). Three edge-disjoint Hamiltonian cycles in crossed cubes with applications to fault-tolerant data broadcasting. The Journal of Supercomputing.

Chang, Y. H., Wu, R.-Y., Chang, R. S., & Chang, J. M. (2022). Improved algorithms for ranking and unranking (k, m)-ary trees in B-order. Journal of Combinatorial Optimization.

Wu, R.-Y., Tseng, C. C., Hung, L. J., & Chang, J. M. (2022). Generating spanning-tree sequences of a fan graph in lexicographic order and ranking/unranking algorithms. International Symposium on Combinatorial Optimization.

Chang, Y. H., Wu, R.-Y., Lin, C. K., & Chang, J. M. (2021). A loopless algorithm for generating (k, m)-ary trees in Gray code order. Optimization Letters.

Dr. Ehsan Adibnia | Computer Science | Editorial Board Member

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Dr. Ehsan Adibnia | Computer Science | Editorial Board Member

University of Sistan and Baluchestan | Iran

Dr. Ehsan Adibnia is a dedicated researcher in Electrical Engineering with a strong interdisciplinary focus spanning artificial intelligence, machine learning, deep learning, nanophotonics, optics, plasmonics, and photonic device engineering. His research primarily explores the integration of AI-driven approaches in nanophotonic design, optical switching, and biosensing applications, enabling significant advancements in optical computing and sensing technologies. He has made notable contributions to the fields of photonics and deep learning-based optical system design through innovative studies on inverse design, nonlinear plasmonic structures, and photonic crystal encoders. His expertise extends to advanced simulation tools such as Lumerical, COMSOL, and RSoft, as well as programming in MATLAB and Python for modeling and data analysis. Dr. Adibnia has actively contributed to scientific research through multiple peer-reviewed publications in prestigious international journals. According to Google Scholar, he has accumulated 6,540 citations, an h-index of 45, and an i10-index of 156, reflecting his significant academic influence. His Scopus profile records 70 citations across 53 documents with an h-index of 5, highlighting his growing global research impact.

Profiles

Scopus | ORCID | Google Scholar

Featured Publications

Adibnia, E., Mansouri-Birjandi, M. A., & Ghadrdan, M. (2024). A deep learning method for empirical spectral prediction and inverse design of all-optical nonlinear plasmonic ring resonator switches. Scientific Reports, 14, 5787.

Adibnia, E., Ghadrdan, M., & Mansouri-Birjandi, M. A. (2024). Nanophotonic structure inverse design for switching application using deep learning. Scientific Reports, 14, 21094.

Adibnia, E., Ghadrdan, M., & Mansouri-Birjandi, M. A. (2025). Chirped apodized fiber Bragg gratings inverse design via deep learning. Optics & Laser Technology, 181, 111766.

Jafari, B., Gholizadeh, E., Jafari, B., & Adibnia, E. (2023). Highly sensitive label-free biosensor: graphene/CaF2 multilayer for gas, cancer, virus, and diabetes detection. Scientific Reports, 13, 16184.

Soroosh, M., Al-Shammri, F. K., Maleki, M. J., Balaji, V. R., & Adibnia, E. (2025). A compact and fast resonant cavity-based encoder in photonic crystal platform. Crystals, 15, 24.

Dr. Han Zhang | Computer Science | Best Researcher Award

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Han Zhang | Computer Science | Best Researcher Award

Research Institute of Petroleum Exploration and Development, China

Dr. Han Zhang is a young and dedicated researcher at the China National Petroleum Corporation Research Institute of Petroleum Exploration and Production, where he focuses on advancing intelligent reservoir development and optimization for the future of the energy industry. With a strong educational foundation, he earned his bachelor’s degree in Marine Oil and Gas Engineering from a prestigious petroleum university in China, majoring in reservoir and oil production engineering, before continuing his master’s and doctoral studies in Oil and Gas Field Development Engineering at the same institute. His research centers on the development of advanced mathematical and numerical models that address key challenges in petroleum engineering, particularly intelligent reservoir management. Dr. Zhang has contributed to one national-level and one provincial-level research project and has also taken part in an industry consultancy project, demonstrating his ability to bridge academic research with practical applications. He has published peer-reviewed articles, including a notable study on gated recurrent unit-based dynamic characterization methods for horizontal wells in carbonate reservoirs, as well as a paper on closed-loop optimization systems for evaluating development potential with water-alternating gas flooding. With three patents under process and active membership in the Society of Petroleum Engineers, Dr. Zhang has positioned himself as a rising scholar committed to innovation. His contributions include refining the analytic hierarchy process through coupling with entropy weight methods for more objective production evaluation, as well as pioneering predictive models that enhance reservoir characterization. He aspires to continue developing transformative technologies that promote efficiency, sustainability, and innovation in petroleum exploration and production.

Profile: ORCID 

Featured Publications

Zhang, H. (2025). A closed-loop optimization system for evaluating the development effect and potential of producers with water alternating gas flooding. Processes.

Zhang, H. (2025). A dynamic characterization method for horizontal wells based on the gated recurrent unit: A case study of a carbonate reservoir in the Middle East. In Springer Series in Geomechanics and Geoengineering. Springer.

Hsin-Yuan Chen | Computer Science | Best Researcher Award

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Prof. Hsin-Yuan Chen | Computer Science | Best Researcher Award

Zhejiang University | China

Prof. Hsin-Yuan Chen is a distinguished scholar and technology leader known for his extensive contributions to artificial intelligence, robotics, and digital technology innovation. He currently serves as the Changjiang Scholar Professor and Director at Zhejiang University’s Institute of Wenzhou, Center of Digital Technology Entrepreneurship and Innovation in China, as well as Adjunct Distinguished Professor at Patil University in India. With an academic and professional journey spanning universities, research institutes, and top technology companies, Prof. Chen has built a reputation for pioneering research, impactful industry collaborations, and leadership in advancing global technology ecosystems.

Publication Profile

Scopus

ORCID

Education Background

Prof. Hsin-Yuan Chen pursued his academic studies at National Cheng Kung University, where he earned both his Bachelor’s and Ph.D. degrees in Aerospace Engineering, completing his doctoral program directly after undergraduate study. His rigorous academic foundation combined with a strong focus on applied research shaped his career path, enabling him to bridge advanced engineering knowledge with emerging fields like artificial intelligence and big data. His educational achievements not only established him as a capable researcher but also laid the groundwork for his future endeavors in academia, technology innovation, and international collaborations across multiple institutions and disciplines.

Professional Experience

Prof. Hsin-Yuan Chen has held numerous leadership and academic roles across diverse sectors. He served as Dean and Professor at Fujian Normal University’s School of Big Data and Artificial Intelligence, and also held CTO positions at GEOSAT Technology and Mobiletron Electronics, leading artificial intelligence applications in industry. His early career included academic appointments at Feng Chia University and National Taiwan Ocean University, alongside international experience as Visiting Professor at Washington University in St. Louis. Additionally, he contributed to public service as a Patent Examiner at the Intellectual Property Office and worked with Delta Electronics as Technical Advisor, balancing academia with industrial innovation.

Awards and Honors

Prof. Hsin-Yuan Chen has been widely recognized with prestigious national and international awards. His accolades include the ScienceFather International Outstanding Scientist Award, the Electronics Best Paper Award, and fellowship honors from IET and ASEAN. He has also received multiple innovation and creativity awards for projects in virtual reality, artificial intelligence, and cloud technology, particularly in digital cultural heritage applications. Earlier distinctions include the Global Top Hundred Engineers Medal, Youth Medal of the Republic of China, and recognition as one of the Top Ten Outstanding Young Women in the Republic of China. His achievements highlight his dedication to research, teaching, and technological innovation.

Research Focus

Prof. Hsin-Yuan Chen’s research primarily spans artificial intelligence, robotics, big data, digital innovation, and human-centered computing. He has extensively explored AI applications in fields such as healthcare, education, and cultural heritage digitalization. His work includes developing hybrid positioning systems, AI-driven recognition technologies, and bibliometric studies in AI applications. He has also focused on advancing industry-academia collaboration and integrating emerging technologies like VR, AR, and IoT into practical solutions. Through his contributions, Prof. Chen has advanced both theoretical research and applied science, strengthening connections between innovation, entrepreneurship, and real-world societal impact in the digital era.

Publication Notes

  1. Evaluating Machine Learning Algorithms for Alzheimer’s Detection: A Comprehensive Analysis
    Published Year: 2025
    Citation: 1

  2. Impact of Industry-Academia Collaboration in Engineering Education: A Case Study
    Published Year: 2025
    Citation: 3

  3. Recursive Queried Frequent Patterns Algorithm: Determining Frequent Pattern Sets from Database
    Published Year: 2025
    Citation: 2

  4. Mapping the Evolution: A Bibliometric Analysis of Employee Engagement and Performance in the Age of AI-Based Solutions
    Published Year: 2025
    Citation: 1

  5. Advancements in Handwritten Devanagari Character Recognition: A Study on Transfer Learning and VGG16 Algorithm
    Published Year: 2024
    Citation: 1

Conclusion

Prof. Hsin-Yuan Chen’s career exemplifies the synergy between academic excellence and industrial innovation. With a solid foundation in aerospace engineering, he has consistently expanded his expertise into artificial intelligence, robotics, and digital transformation. His leadership roles across universities, research institutions, and technology enterprises demonstrate his global influence, while his awards reflect recognition for outstanding achievements in both research and practice. As an educator, innovator, and scientist, Prof. Chen continues to inspire through his contributions to emerging technologies and his efforts in building bridges between academia and industry to shape the future of digital transformation.

Ke Wu | Computer Science | Best Dissertation Award

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Prof. Ke Wu | Computer Science | Best Dissertation Award

professor, China University of Geosciences (Wuhan), China

Dr. Ke Wu is a distinguished professor at the China University of Geosciences, specializing in hyperspectral remote sensing and its applications in geosciences 🌏. Born on October 2, 1981, in Hubei, China, Dr. Wu has established himself as a leading expert in his field, contributing significantly to research and education 📚. Fluent in both Chinese and English, he excels in both written and spoken communication, making him a valuable asset to the academic community.

Profile

ORCID

 

Education

Dr. Ke Wu holds a Ph.D. in Photogrammetry and Remote Sensing from Wuhan University (2008) 🎓, where he also completed his B.S. in Information Engineering (2002) 🏫. His advanced education has provided a strong foundation for his research and teaching career in remote sensing and geophysics.

Experience

Since January 2020, Dr. Ke Wu has been a professor at the China University of Geosciences 👨‍🏫. Prior to this, he served as an associate professor from 2011 to 2019 and as a postdoctoral researcher in geophysics from 2009 to 2011. His extensive experience in academia has enabled him to mentor many students and contribute to numerous research projects.

Research Interests

Dr. Ke Wu’s research interests focus on hyperspectral remote sensed image processing and its applications in geosciences 🔬. He has led several significant research projects funded by the National Natural Science Foundation of China and other prestigious organizations. His work aims to advance the understanding and practical applications of remote sensing technologies.

Awards

In recognition of his contributions to the field, Dr. Ke Wu and his team have received numerous awards 🏆. Notably, in 2022, they won the third prize in the National Hyperspectral Satellite Remote Sensing Image Intelligent Processing and Industry Application Competition of the “Obit Cup”. His group also secured the third prize in the South Division of the “Yuan Chuang Cup” Innovation and Creativity Competition in 2019 and the first prize of the Surveying and Mapping Science and Technology Progress Award of the China Society of Surveying, Mapping, and Geographic Information in 2017.

Publications

Junfei Zhong, Ke Wu, Ying Xu* (2024). “Deep Spatial Feedback Refined Network with Multi-Level Feature Fusion for Hyperspectral Image Sub-pixel Mapping,” IEEE Transactions on Geoscience and Remote Sensing. DOI: 10.1109/TGRS.2024.3419157 – Cited by: 3 articles

Ke Wu, Fan Yang, Huize Liu, Ying Xu* (2024). “Detection of coral reef bleaching by multitemporal Sentinel-2 data using the PU-bagging algorithm: A feasibility study at Lizard Island,” Remote Sens. DOI: 10.3390/rs16132473 – Cited by: 5 articles

Ke Wu, Yanting Zhan, Ying An, Suyi Li* (2024). “Multiscale Feature Search-Based Graph Convolutional Network for Hyperspectral Image Classification,” Remote Sens. DOI: 10.3390/rs16132328 – Cited by: 4 articles

Wenjie Tang, Ke Wu, Yuxiang Zhang, Yanting Zhan* (2023). “A Siamese Network Based on Multiple Attention and Multilayer Transformer for Change Detection,” IEEE Transactions on Geoscience and Remote Sensing. DOI: 10.1109/TGRS.2023.3325220 – Cited by: 6 articles

Yanting Zhan, Ke Wu, Yanni Dong* (2022). “Enhanced Spectral–Spatial Residual Attention Network for Hyperspectral Image Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. DOI: 10.1109/JSTARS.2022.3197934 – Cited by: 8 articles

Tidjani Négadi | Computer Science | Best Researcher Award

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Dr. Tidjani Négadi | Computer Science | Best Researcher Award

recently retired, Physics Department, Faculty of Exactand Applied Science, University Oran 1 Ahmed Ben Bella, Oran 31100, Algeria,

📅 Born on January 26, 1950, in Tlemcen, Algeria, Tidjani Négadi is a distinguished Maître de Conférence at the Physics Department, Faculty of Exact and Applied Science, University Oran 1 Ahmed Ben Bella, Oran, Algeria. With a profound interest in theoretical and mathematical biology, Négadi has significantly contributed to various fields, especially in exploring the connections between physics and biological systems.

Profile

Google Scholar

Education

🎓 Tidjani Négadi earned his Doctorat de 3ème Cycle in Nuclear Physics in 1976 and a Doctorat d’Etat Es-Science Physiques in Theoretical Physics in 1988, both from the Institut de Physique Nucléaire IN2P3, Université Claude Bernard Lyon-I, France. His extensive education laid the foundation for his interdisciplinary research spanning nuclear physics, theoretical physics, and mathematical biology.

Experience

💼 Négadi’s academic journey began in 1976, teaching Quantum Mechanics and its applications until 1989. He later taught Atomic and Molecular Physics, and Group Theory until 2002, after which he focused solely on research, particularly in Mathematical Biology. His teaching portfolio also includes Special Relativity, Astronomy, and Astrophysics from 2015 to 2018. His editorial roles and contributions to esteemed journals and conferences highlight his expertise and dedication to advancing scientific knowledge.

Research Interests

🔬 Négadi’s research interests are vast and interdisciplinary, focusing on the mathematical modeling of biological systems, particularly the genetic code. He has explored the symmetries in the genetic code, the use of Fibonacci and Lucas numbers, and the application of quantum-like approaches to biological systems. His work bridges the gap between physics and biology, offering novel insights into genetic information and its underlying structures.

Awards

🏆 Tidjani Négadi’s contributions to science have been recognized with several prestigious awards and honors. He has served as a member of the Executive Board and Advisory Board of the International Symmetry Association (ISA) and the Advisory and Editorial Board of NeuroQuantology. His role as a guest editor for various special issues in prominent journals showcases his leadership in the scientific community.

Publications

1976: Lifetimes of levels in 64Zn from Doppler shift measurements via 61Ni(a,n) 64Zn reaction, Phys. Rev. C13, cited by 10 articles.

1983: On the connection between the hydrogen atom, and the harmonic oscillator, Lett. Nuovo Cimento, cited by 15 articles.

1983: On the connection between the hydrogen atom, and the harmonic oscillator: the continuum case, J. Phys. A16, cited by 12 articles.

1984: Connection between the hydrogen atom, and the harmonic oscillator: the zero-energy case, Phys. Rev. A29, cited by 9 articles.

1984: Hydrogen atom in a uniform electromagnetic field as an anharmonic oscillator, Lett. Nuovo Cimento, cited by 7 articles.