Gary Wong | Computer Science | Best Researcher Award

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Prof. Dr. Gary Wong | Computer Science | Best Researcher Award

Prof. Dr. Gary Wong | Computer Science | The University of Hong Kong | Hong Kong

Prof. Dr. Gary Wong is a highly accomplished scholar in computer science education whose research contributions have significantly influenced the fields of computational thinking, digital literacy, technology-enhanced learning, and K–12 AI education. With a strong academic foundation supported by advanced degrees from leading institutions, his educational background reflects rigorous training in both pedagogy and computer science, enabling Him to bridge disciplinary boundaries with expertise and innovation. Throughout his professional career, Prof. Dr. Gary Wong has held key academic and leadership roles, contributing to major international research initiatives, collaborating with renowned global scholars, and publishing impactful studies that guide educational technology policies and classroom practices. His research interests encompass computational thinking development, digital literacy assessment, educational data analysis, teacher professional development, AI-in-education integration, and immersive learning design, and he demonstrates strong methodological skills in both qualitative and quantitative research approaches. His scholarly output includes numerous Scopus-indexed and IEEE publications that are extensively cited worldwide, shaping frameworks for digital skills assessment, pedagogical innovation, and technology adoption among educators. His research skills include large-scale data analysis, cross-cultural educational research, experimental design, research instrument development, and systematic reviews with meta-analytic rigor. Prof. Dr. Gary Wong has received multiple recognitions and honors for academic excellence, peer-review contributions, and impactful research, reflecting his prominence in the global education technology community. He has collaborated with leading universities and contributed to funded international research projects focused on curriculum innovation and computational thinking education. Additionally, he is actively involved in academic service, editorial review roles, and professional memberships in major associations such as IEEE and ACM, demonstrating strong engagement with scholarly communities and educational leadership. His substantial citation metrics further reflect the global influence of his work across multiple research domains. In conclusion, Prof. Dr. Gary Wong stands out as a leading expert whose research excellence, innovative contributions, and strong academic leadership continue to advance the global understanding of technology-enhanced learning and computational thinking education, positioning Him as a driving force for future advancements in education research worldwide.

Academic Profile: ORCID | Scopus | Google Scholar

Featured Publications:

  1. A global framework of reference on digital literacy skills for indicator 4.4.2. (2018). Citation Count: 662

  2. Broadening artificial intelligence education in K-12: Where to start? (2020). Citation Count: 294

  3. Designing unplugged and plugged activities to cultivate computational thinking: An exploratory study in early childhood education. (2020). Citation Count: 260

  4. Exploring children’s perceptions of developing twenty-first century skills through computational thinking and programming. (2020). Citation Count: 153

  5. The behavioral intentions of Hong Kong primary teachers in adopting educational technology. (2016). Citation Count: 149

 

 

Xinyue Xu | Computer Science | Research Excellence Award

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Ms. Xinyue Xu | Computer Science | Research Excellence Award

Ms. Xinyue Xu | Computer Science | University of Chinese Academy of Sciences | China

Ms Xinyue Xu is a dedicated researcher affiliated with the University of Chinese Academy of Sciences, where she advances her work in intelligent defect detection, robust deep-learning architectures, and computer vision applications for industrial automation. Ms Xinyue Xu completed her doctoral studies in a computing-related discipline at the University of Chinese Academy of Sciences, focusing on lightweight neural network optimization, blur-robust models, and intelligent inspection frameworks that address real-world engineering challenges. Her professional experience includes academic research responsibilities within her institution, participation in collaborative laboratory initiatives, and involvement in international research efforts related to machine vision and high-precision defect-detection systems. Her primary research interests include computer vision, artificial intelligence, defect detection in industrial components, lightweight convolutional models, and the development of high-efficiency architectures suitable for real-time inspection tasks. Ms Xinyue Xu possesses strong research skills in model design, data-driven algorithm development, image processing, neural network compression, performance benchmarking, and experimental evaluation across diverse industrial datasets. Her ORCID profile documents her contribution to the Scopus-indexed journal article FDC-YOLO: A Blur-Resilient Lightweight Network for Engine Blade Defect Detection, where she introduces an enhanced lightweight framework capable of handling blur-induced recognition challenges. This publication demonstrates her technical rigour, methodological clarity, and ability to address practical industrial problems through advanced machine-learning models. Although early in her career, Ms Xinyue Xu exhibits promising scholarly potential and continues to strengthen her academic record. Her research visibility and contributions make her eligible for participation in professional associations such as IEEE and ACM, and her academic accomplishments position her well for future recognition in emerging researcher and innovation-focused award categories. Her honors include her contribution to high-quality peer-reviewed research published in an international journal indexed in Scopus. Moving forward, Ms Xinyue Xu is well positioned to expand her publication portfolio, engage in broader interdisciplinary collaboration, strengthen her global research presence, and contribute to the development of next-generation intelligent inspection technologies. With her commitment to scientific advancement, growing research output, and expertise in applied AI, she continues to demonstrate strong potential for leadership and long-term impact in her field.

Academic Profile: ORCID

Featured Publications:

  1. Xu, X., Li, F., Xiong, L., He, C., Peng, H., Zhao, Y., & Song, G. (2025). FDC-YOLO: A blur-resilient lightweight network for engine blade defect detection. Algorithms.