Pengfei Wei | Computer Science | Best Researcher Award

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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.

Fei Li | Next-generation informatic | Leading Research Award

Posted on by Phenomenological Research Awards

prof. Fei Li | Next-generation informatic | Leading Research Award

Research scientist Institute of Grassland Research, Chinese Academy of Agricultural Sciences China

Dr. Fei Li is a prominent research professor at the Institute of Grassland Research, Chinese Academy of Agricultural Sciences (CAAS). Specializing in the remote sensing of grassland ecology and big data, Dr. Li leverages satellite and UAV remote sensing along with AI algorithms to advance ecological research. With over 40 academic papers published in top-tier journals, Dr. Li’s contributions have significantly impacted the field of ecological and biological sciences.

 

Profile

Scopus

Education 🎓

Dr. Li earned his Ph.D. in Cartography and Geographic Information Systems from the University of Chinese Academy of Sciences in 2014. Prior to this, he completed his M.S. in Cartography and Geographic Information Systems and his B.S. in Geographic Information Systems from Northwest Normal University in 2009 and 2006, respectively.

Experience 🏆

Dr. Li’s extensive experience includes his current role as a research professor at CAAS since 2020. He previously served as a research assistant at the University of Tennessee, Michigan State University, and the Chinese Academy of Sciences. His diverse experience in both academic and research institutions has equipped him with a robust understanding of ecological processes and remote sensing technologies.

Research Interests 🔬

Dr. Li’s research interests lie in integrating ecological process models with remote sensing observations and machine learning approaches. He focuses on simulating global-regional carbon-water cycles and investigating their response mechanisms. Additionally, he is dedicated to utilizing big data from remote sensing for effective grassland resource monitoring and management.

Awards 🏅

Dr. Li has been recognized with numerous awards and grants from prestigious organizations such as NSF, NASA, DOE, NSA, and ESA. His groundbreaking work in remote sensing and ecological modeling has earned him accolades and funding for various high-impact projects.

Publications Top Notes 📚

Dr. Li has an impressive portfolio of publications, including:

Li, H., Li, F.*, Xiao, J., Chen, J., Lin, K., Bao, G., … & Wei, G. (2024). A machine learning scheme for estimating fine-resolution grassland aboveground biomass over China with Sentinel-1/2 satellite images. Remote Sensing of Environment, 311, 114317. Cited by 10 articles.

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Li, F., Xiao, J., Chen, J., Ballantyne, A., Jin, K., Li, B., … & John, R. (2023). Global water use efficiency saturation due to increased vapor pressure deficit. Science, 381(6658), 672-677. Cited by 25 articles.

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Yan, H., Li, F.*, & Liu, G. (2023). Diminishing influence of negative relationship between species richness and evenness on the modeling of grassland α-diversity metrics. Frontiers in Ecology and Evolution, 11, 154. Cited by 15 articles.

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Ouyang, Z., Sciusco, P., Jiao, T., Feron, S., Lei, C., Li, F., … & Chen, J. (2022). Albedo changes caused by future urbanization contribute to global warming. Nature Communications, 13(1), 3800. Cited by 30 articles.

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Brabazon, H., DeBruyn, J. M., Lenaghan, S. C., Li, F., Mundorff, A. Z., Steadman, D. W., & Stewart Jr, C. N. (2020). Plants to Remotely Detect Human Decomposition?. Trends in Plant Science, 25(10), 947-949. Cited by 20 articles.

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