Pengfei Wei | Computer Science | Best Researcher Award

Posted on by Phenomenological Research Awards

Dr. Pengfei Wei | Computer Science | Best Researcher Award 

Senior Engineer at Guangdong University of Technology | China

Dr. Pengfei Wei is a Senior Engineer at Guangdong University of Technology, recognized for his pioneering contributions to the field of computer science, particularly in multimodal learning, knowledge tracing, edge artificial intelligence, and task-oriented dialogue systems. He holds a Ph.D. in Computer Science, where his research focused on integrating deep learning models with practical applications in intelligent education and human–machine interaction. Combining academic rigor with industrial innovation, he brings substantial experience from both enterprise research and academic development, bridging the gap between theory and real-world technology deployment. His work encompasses advanced methods such as visual-enhanced transformers for multimodal named entity recognition, genetic-inspired relation extraction, and the introduction of Kolmogorov–Arnold representations in knowledge tracing, which have improved model interpretability and performance in AI-based learning systems. In addition to his theoretical advancements, he has successfully led projects on real-time lab-safety analytics and large-scale AI deployment using Huawei Ascend, Nvidia, and TPU platforms, contributing to the broader industrial adoption of edge AI technologies. Dr. Pengfei Wei has authored numerous peer-reviewed papers in top-tier international journals and conferences, including Neural Networks, ICMR, and IJCAI, and serves as a reviewer for several prestigious publications such as Neural Networks, Pattern Recognition Letters, AAAI, and IJCNN. His collaborative initiatives with research teams and institutions have fostered multidisciplinary innovation, emphasizing the integration of AI with blockchain, big data, and education systems. A dedicated mentor and research leader, he actively supports student-led research and fosters the development of next-generation AI scholars. His professional memberships with the China Computer Federation (CCF) and the Association for Computing Machinery (ACM) reflect his strong engagement in the global computing community. Dr. Pengfei Wei’s research continues to push the boundaries of multimodal understanding and intelligent systems, driving transformative progress in computational learning and applied artificial intelligence. Through his sustained contributions, he remains committed to advancing the capabilities of intelligent technologies that enhance human productivity, knowledge discovery, and digital transformation.

Profile: Orcid | Google Scholar

Featured Publications:

  • Liao, W., B. Zeng, Yin, X., & Wei, P. (2021). An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence, 51(6), 3522–3533.

  • Liao, W., Zeng, B., Liu, J., Wei, P., Cheng, X., & Zhang, W. (2021). Multi-level graph neural network for text sentiment analysis. Computers & Electrical Engineering, 92, 107096.

  • Liao, W., Zeng, B., Liu, J., Wei, P., & Fang, J. (2022). Image-text interaction graph neural network for image-text sentiment analysis. Applied Intelligence, 52(10), 11184–11198.

  • Liao, W., Zeng, B., Liu, J., Wei, P., & Cheng, X. (2022). Taxi demand forecasting based on the temporal multimodal information fusion graph neural network. Applied Intelligence, 52(10), 12077–12090.

  • Wei, P., Zeng, B., & Liao, W. (2022). Joint intent detection and slot filling with wheel-graph attention networks. Journal of Intelligent & Fuzzy Systems, 42(3), 2409–2420.

  • Wei, P., Ouyang, H., Hu, Q., Zeng, B., Feng, G., & Wen, Q. (2024). VEC-MNER: Hybrid transformer with visual-enhanced cross-modal multi-level interaction for multimodal NER. Proceedings of the International Conference on Multimedia Retrieval (ICMR 2024).

  • Wen, S., Zeng, B., Liao, W., Wei, P., & Pan, Z. (2021). Research and design of credit risk assessment system based on big data and machine learning. Proceedings of the IEEE 6th International Conference on Big Data Analytics (ICBDA 2021), 9–13.

Kai Jin | Computer Science | Best Researcher Award

Posted on by Phenomenological Research Awards

Dr. Kai Jin | Computer Science | Best Researcher Award

Lecturer at Sanya Research Institute of Hunan University of Science and Technology | China

Dr. Kai Jin is an accomplished researcher and academic whose work bridges the fields of computer science, artificial intelligence, and information engineering. With a strong educational foundation culminating in a Ph.D. in Computer Science and Technology from Hunan University, he has built a research career characterized by innovation, interdisciplinary collaboration, and practical impact. His professional experience spans both academia and industry, having served as a lecturer and researcher at the Sanya Research Institute of Hunan University of Science and Technology, as well as a software engineer in technology firms where he developed expertise in system architecture and Java-based solutions. Dr. Kai Jin’s scholarly contributions focus on network measurement, image recognition, and deep learning areas that are pivotal to advancing intelligent computing and data-driven technologies. He has authored six scientific papers published in high-impact journals and international conferences, including IEEE Transactions on Network Science and Engineering, Connection Science, and Scientific Reports. His work has earned 70 citations by 60 documents, with an h-index of 5, reflecting the growing influence of his research within the global academic community. In addition to publications, Dr. Kai Jin has secured four invention patents covering innovations in network traffic measurement, remote sensing image detection, brain tumor identification, and predictive maintenance for industrial IoT systems. His research projects, supported by national and provincial grants, such as the National Natural Science Foundation of China and the Hunan Provincial Key R&D Program, demonstrate a commitment to technological progress and societal benefit. Beyond his technical achievements, Dr. Kai Jin’s leadership in research collaborations and mentorship reflects his dedication to fostering the next generation of computer scientists. His current research continues to explore the integration of deep learning models with real-world systems, optimizing intelligent network management, and enhancing computational efficiency. Through his scientific rigor, creativity, and contributions to both theoretical and applied computing, Dr. Kai Jin has established himself as a leading voice in modern computer science, shaping innovations that address the complex challenges of today’s interconnected digital world.

Profile: Scopus

Featured Publications:

1. Jin, K., Xie, K., Wang, X., Tian, J., Xie, G., & Wen, J. (2022). Low-cost online network traffic measurement with subspace-based matrix completion. IEEE Transactions on Network Science and Engineering, 10(1), 53–67.

2. Jin, K., Xie, K., Tian, J., Liang, W., & Wen, J. (2023). Low-cost network traffic measurement and fast recovery via redundant row subspace-based matrix completion. Connection Science, 35(1), 2218069.

3. Jin, K., Banizaman, H., Gharehveran, S. S., & Jokar, M. R. (2025). Robust power management capabilities of integrated energy systems in the smart distribution network including linear and non-linear loads. Scientific Reports, 15(1), 6615.

4. Zhu, M., Rasheed, R. H., Albahadly, E. J. K., Zhang, J., Alqahtani, F., Tolba, A., & Jin, K.* (2025). Application of fixed and mobile battery energy storage flexibilities in robust operation of two-way active distribution network. Electric Power Systems Research, 244, 111556.

5. Wen, J., Chen, Y., & Jin, K.* (2023, June). Revolutionizing network performance: The active and passive service path performance monitoring analysis method. In 2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud) / 2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom) (pp. 1–6). IEEE.

6. Huo, Y., Jin, K., Cai, J., Xiong, H., & Pang, J. (2023). Vision Transformer (ViT)-based applications in image classification. In Proceedings of the 9th IEEE International Conference on High Performance and Smart Computing (HPSC 2023) (pp. 135–140). IEEE.

7. Jin, K., Xie, K., Tian, J., Liang, W., & Wen, J. (2024). A acylthiourea based ion-imprinted membrane for selective removal of Ag⁺ from aqueous solution. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 2024, Article 9 citations.