Xulei Cao | Computer Science | Research Excellence Award

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Mr. Xulei Cao | Computer Science | Research Excellence Award

University of Science and Technology of China | China

Mr. Xulei Cao research centers on advancing intelligent communication systems, large-scale machine learning, and adaptive networked environments, with a primary emphasis on vehicular ad hoc networks (VANETs), device–edge–cloud collaboration, and large language models. His work explores street-centric and microtopology-based routing strategies to address the challenges of dynamic mobility, frequent topology changes, and complex urban communication environments, proposing opportunistic routing protocols that leverage link correlation to enhance reliability, reduce packet loss, and optimize end-to-end performance. He has contributed to routing solutions grounded in urban road structure awareness, improving scalability and robustness in dense vehicular networks and supporting next-generation intelligent transportation systems. In parallel, his research extends into intelligent computing frameworks that integrate device, edge, and cloud layers to enable efficient distributed learning, resource-aware decision-making, and latency-sensitive AI applications. He also investigates algorithmic innovation within large language models, emphasizing scalability, deployment efficiency, and real-world applicability. Additionally, his work on biometric recognition, including palmprint feature extraction and direction coding, demonstrates expertise in pattern recognition and vision-based authentication systems. Supported by growing scholarly recognition, his work has been cited 212 times overall, including 101 citations since 2020, with an h-index of 3 and an i10-index of 2, underscoring the increasing impact and relevance of his contributions to networking, artificial intelligence, and intelligent mobility research.

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🟦 Citations    🟥 i10-index    🟩 h-index


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Featured Publications

 

Weidong Ji | Computer Science | Editorial Board Member

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Dr. Weidong Ji | Computer Science | Editorial Board Member

Harbin Normal University | China

Dr. Weidong Ji is a distinguished Chinese scholar known for his innovative contributions to artificial intelligence in education, learning analytics, and intelligent recommendation technologies, with a strong research presence reflected across international journals and conferences. His work integrates machine learning, knowledge graphs, deep learning models, and cognitive theories to enhance personalized learning, online education systems, student engagement assessment, and data-driven educational decision-making. With a Scopus record of 39 documents, 102 citations from 100 citing documents, and an h-index of 6, Dr. Ji’s scholarly influence continues to grow as his recent works advance emerging domains such as quantum-constructivism-based knowledge tracing, lightweight human–computer interaction models, and user-preference-driven recommendation algorithms. Google Scholar metrics (if available) would further extend his citation visibility, demonstrating the expanding global use of his models in adaptive learning, course recommendations, and real-time student behavior analysis. He also contributes to the academic community through participation in peer-review processes and editorial activities that support the development of high-quality research in AI-driven educational technology. Dr. Ji’s recent publications showcase cutting-edge computational frameworks such as neural knowledge graph reasoning, lightweight vision models for engagement analytics, and personalized prediction architectures for sparse learning environments. His contributions position him as an emerging leader at the intersection of educational psychology and computational intelligence, emphasizing practical applicability, algorithmic efficiency, and innovative pedagogical design, demonstrating excellence aligned with this award category as an Editorial Board Member.

Publication Profile

Scopus

Featured Publications

  • Authors unavailable. (2025). Knowledge graph convolutional networks with user preferences for course recommendation. Scientific Reports.

  • Authors unavailable. (2025). CQSA-KT: Research on personalized knowledge tracing based on quantum-constructivism in sparse learning environments. Knowledge Based Systems.

  • Authors unavailable. (2025). LightNet: A lightweight head pose estimation model for online education and its application to engagement assessment. Journal of King Saud University Computer and Information Sciences.

Sara Dankir | Computer Science | Best Researcher Award

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