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.

 

 

Shilong Lin | Computer Science | Best Researcher Award

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Mr. Shilong Lin | Computer Science | Best Researcher Award

Mr. Shilong Lin | Computer Science | Guangxi Normal University | China

Mr Shilong Lin is a dedicated researcher in computer science, specializing in graph neural networks, representation learning, and advanced machine learning methods, and he is currently affiliated with the School of Computer Science and Engineering at Guangxi Normal University, where he is engaged in both academic research and teaching activities; Mr Shilong Lin has pursued his education within the same institution, building a strong foundation in artificial intelligence, machine learning, and data-driven modeling, and his academic journey has enabled him to develop deep expertise in designing algorithms for node classification, graph similarity learning, contrastive learning, and adaptive feature fusion; throughout his professional experience, Mr Shilong Lin has collaborated with notable international scholars such as Guangquan Lu, Longqing Du, Shichao Zhang, Cuifang Zou, and Xuxia Zeng, contributing to impactful research projects that address challenges in imbalanced data learning, multi-hop contrastive approaches, multi-scale graph feature fusion, and cross-level interaction modeling; his research interests span graph representation learning, deep learning architectures, imbalance-aware modeling, scalable graph algorithms, and AI-driven knowledge discovery, and these interests have shaped his contribution to several high-quality journal and conference publications across reputed platforms including Neurocomputing, Information Processing & Management, and the International Joint Conference on Artificial Intelligence (IJCAI); in terms of research skills, Mr Shilong Lin demonstrates strong capabilities in algorithm design, graph neural network development, experimental evaluation, data preprocessing, model optimization, and collaborative scientific writing, supported by his competence in programming, machine learning frameworks, and scientific communication; while building his research trajectory, Mr Shilong Lin has also contributed to academic community participation through teamwork, collaborative problem solving, and involvement in multi-author research projects that enhance the scientific visibility of his institution; his awards and honors reflect his ongoing contribution to high-quality research, including recognition for publications in top-tier AI and computational intelligence venues and his involvement in internationally indexed works listed under ORCID; overall, Mr Shilong Lin continues to advance his research career with strong dedication, intellectual rigor, and a vision of contributing to the development of efficient, explainable, and scalable graph learning systems, and his growing academic output positions him as a promising scholar who is steadily building an impactful presence in the global artificial intelligence research community.

Academic Profile: ORCID

Featured Publications:

  1. Lin, S., Lu, G., Zhang, S., Du, L., & Luo, Z. (2025). Synthesizing stronger nodes for minority classes in imbalanced node classification.

  2. Zeng, X., Lu, G., Zou, C., Lin, S., Du, L., & Zhang, S. (2025). Multi-hop contrastive learning with feature augmentation for node classification.

  3. Zou, C., Lu, G., Zhang, W., Zeng, X., Lin, S., Du, L., & Zhang, S. (2025). Enhanced graph similarity learning via adaptive multi-scale feature fusion.

  4. Zou, C., Lu, G., Du, L., Zeng, X., & Lin, S. (2025). Graph similarity learning for cross-level interactions.