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.

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.