Li Xiao | Engineering | Best Industrial Research Award

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Mr. Li Xiao | Engineering | Best Industrial Research Award

Northwest Normal University | China

Mr. Li Xiao, in Lanzhou, Gansu Province, China, is a dedicated researcher currently pursuing a master’s degree at Northwest Normal University after completing his undergraduate studies at Anyang Normal University . Throughout his academic journey, he has demonstrated exceptional excellence, earning multiple scholarships including the National Encouragement Scholarship, school-level scholarships, and awards such as the third prize  Lanqiao Algorithm Competition for University Group B and the Third Prize Team Award in the Computer Competition for Chinese Universities – Programming Ascension Contest in Henan Province. Professionally, Li Xiao has actively engaged in research projects, notably securing Annual Research Funding Project at Northwest Normal University, and has contributed to publications including a paper titled “Industrial Prediction Method Based on Graph Sampling and Aggregation of Temporal Features” in The Canadian Journal of Chemical Engineering. His research interests center on industrial prediction methods, algorithmic modeling, and the application of computational techniques to solve practical engineering challenges. Li Xiao has developed strong research skills in data analysis, temporal feature aggregation, graph-based sampling methods, and predictive modeling, reflecting a robust ability to integrate theoretical frameworks with real-world industrial applications. Beyond technical expertise, he has demonstrated leadership and collaboration skills through team competitions and research projects, positioning him as an emerging leader in engineering research. His awards and honors underscore both his academic excellence and innovative contributions to the field. In conclusion, Mr. Li Xiao’s consistent achievements, research capabilities, and proactive engagement in scholarly and industrial projects make him a highly deserving recipient of the Best Industrial Research Award, as his work not only advances engineering research but also promises significant impact on industrial practices and global scientific collaboration.

Profile: Orcid

Featured Publications

Gao, S., Li, X., Yang, W., Xie, J., & Yun, P. (2025). Industrial prediction method based on graph sampling and aggregation of temporal features. The Canadian Journal of Chemical Engineering. Advance online publication.

Fanfan Lin | Electrical Engineering | Young Scientist Award

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Dr. Fanfan Lin | Electrical Engineering | Young Scientist Award

Zhejiang University | China

Dr. Fanfan Lin is an accomplished researcher in electrical engineering whose academic and professional journey reflects a blend of technical expertise, international collaboration, and entrepreneurial leadership. She earned her joint Ph.D. in Power Engineering from Nanyang Technological University, Singapore, and the Technical University of Denmark, Copenhagen , supported by a four-year interdisciplinary research scholarship, with her doctoral thesis focusing on the dual active bridge converter design with artificial intelligence. Earlier, she completed her B.E. in Electrical Engineering and B.A. in English Language and Literature at Harbin Institute of Technology, China , and further enriched her academic background through an exchange program at Peter the Great St. Petersburg Polytechnic University, Russia. Professionally, Dr. Lin currently serves as a Postdoctoral Research Fellow and Assistant Professor at the Zhejiang University–University of Illinois Urbana-Champaign Institute, where she leads projects funded by the China Postdoctoral Science Foundation, the National Natural Science Foundation of China (Young Scientists Fund), and the Intellectual Property Fund of Zhejiang University. Her research interests center on power electronics design integrated with artificial intelligence and responsible AI for power systems, and her skills span advanced converter modeling, physics-informed machine learning, system optimization, and interdisciplinary innovation. Dr. Lin has published extensively in top-tier journals such as IEEE Transactions on Industrial Electronics and IEEE Journal of Emerging and Selected Topics in Power Electronics, with several ESI highly cited papers and over 800 citations. She has been recognized with prestigious awards including the IEEE Industry Applications Society Prize Paper Award, NTU Graduate College Innovation and Entrepreneurship Award, and Geneva Inventions Silver Award, alongside multiple entrepreneurial and social impact recognitions. With her strong foundation in education, research, innovation, and global collaboration, Dr. Lin is highly deserving of the Young Scientist Award, as her expertise and leadership potential are set to significantly advance the future of power electronics and artificial intelligence in sustainable energy systems.

Profile: Orcid | Google Scholar

Featured Publications

Li, X., Lin, F., Rodríguez-Andina, J. J., Guerrero, J. M., Mantooth, H. A., & Ma, H. (2025). NeurPecs: Physics-informed AI-based adaptive circuit simulator for power converters. IEEE Transactions on Industrial Electronics. Advance online publication.

Dai, X., Zhou, R., Zhang, J., He, K., Lin, F., & Ma, H. (2025). SocNet: A physics-guided neural network for battery state-of-charge estimation robust to temperature variations and sensor noises. IEEE Transactions on Transportation Electrification. Advance online publication.

Lin, F., Li, X., Lei, W., Rodríguez-Andina, J. J., Guerrero, J. M., Wen, C., Zhang, X., & Ma, H. (2025, April). PE-GPT: A new paradigm for power electronics design. IEEE Transactions on Industrial Electronics. Advance online publication.

Li, X., Lin, F., Sun, C., Zhang, X., Ma, H., Wen, C., Blaabjerg, F., & Mantooth, H. A. (2025, February). A generic modeling approach for dual-active-bridge converter family via topology transferrable networks. IEEE Transactions on Industrial Electronics. Advance online publication.

Li, X., Lin, F., Wang, H., Zhang, X., Ma, H., Wen, C., & Blaabjerg, F. (2024). Temporal modeling for power converters with physics-in-architecture recurrent neural network. IEEE Transactions on Industrial Electronics. Advance online publication.