Xingchen Hu
Xingchen Hu has been intensively engaged in data analysis and data-driven modeling, focusing on advancing the fields of Computational Intelligence, Multi-view Learning, and Granular Computing. These areas represent significant advancements in the design and analysis of intelligent and human-centric systems. His research encompasses data clustering, fuzzy sets and systems, neural networks, and evolutionary computing. His group is pioneering the development of distributed and collaborative data-driven modeling for multi-modal information fusion, with promising applications in object recognition, trajectory prediction, and anomaly detection.
His prolific contributions to the field are evidenced by the publication of over 30 papers in esteemed journals, including IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, Information Sciences, and Knowledge-Based Systems. His work has been recognized with prestigious awards, including the First Prize for Science and Technology Progress in the Wu Wenjun Artificial Intelligence Awards from the Chinese Association for Artificial Intelligence, and two First Prizes for Science and Technology Progress from the Chinese Institute of Command and Control.
We are seeking highly motivated and self-driven students to join our research team. If you are interested in these research areas, please contact him at xhu4@ualberta.ca or xingchenhu@nudt.edu.cn.
Education
- Ph.D. Software Engineering and Intelligent Systems, University of Alberta, Edmonton, Canada, 2017
- M.E. Control Science and Engineering, National University of Defence Technology, Changsha, China, 2013
- B.E. Flight Vehicle Design and Engineering, Beihang University, Beijing, China, 2011
Professional Experience
- 2020–present: National University of Defence Technology, Associate Professor, College of Systems Engineering
- 2018–2020: National University of Defence Technology, Assistant Professor, College of Systems Engineering
Research Areas
- Computational intelligence
- Granular computing
- Multi-view clustering
- Federated learning
- Evolutionary optimization
Selected Publications
- 2024: Xingchen Hu, Xiubin Zhu, Lan Yang, Witold Pedrycz, Zhiwu Li. "A Design of Fuzzy Rule-based Classifier for Multi-class Classification and its Realization in Horizontal Federated Learning." IEEE Transactions on Fuzzy Systems (2024). [LINK]
- 2024: Huimin Zhang, Xingchen Hu, Xiubin Zhu, Xinwang Liu, Witold Pedrycz. "Application of Gradient Boosting in the Design of Fuzzy Rule-Based Regression Models." IEEE Transactions on Knowledge and Data Engineering (2024). [LINK]
- 2023: Xingchen Hu, Jindong Qin, Yinghua Shen, Witold Pedrycz, Xinwang Liu, and Jiyuan Liu. "An Efficient Federated Multi-view Fuzzy C-Means Clustering Method." IEEE Transactions on Fuzzy Systems (2023). [LINK] [CODE]
- 2022: Xingchen Hu, Xinwang Liu, Witold Pedrycz, Qing Liao, Yinghua Shen, Yan Li, and Siwei Wang. "Multi-view fuzzy classification with subspace clustering and information granules." IEEE Transactions on Knowledge and Data Engineering (2022). [LINK] [CODE]
- 2021: Xingchen Hu, Yinghua Shen, Witold Pedrycz, Yan Li, and Guohua Wu. "Granular Fuzzy Rule-Based Modeling With Incomplete Data Representation." IEEE Transactions on Cybernetics, 52, no. 7: 6420-6433. [LINK] [CODE]
- 2021: Xingchen Hu, Yinghua Shen, Witold Pedrycz, Xianmin Wang, Adam Gacek, and Bingsheng Liu. "Identification of fuzzy rule-based models with collaborative fuzzy clustering." IEEE Transactions on Cybernetics 52, no. 7 (2021): 6406-6419. [LINK]
- 2021: Xingchen Hu, Witold Pedrycz, Keyu Wu, and Yinghua Shen. "Information granule-based classifier: A development of granular imputation of missing data." Knowledge-Based Systems, 214 (2021): 106737. [LINK] [CODE]
- 2018: Xingchen Hu, Witold Pedrycz, and Xianmin Wang. "Fuzzy classifiers with information granules in feature space and logic-based computing." Pattern Recognition, 80, 156-167. [LINK]
- 2017: Xingchen Hu, Witold Pedrycz, Oscar Castillo, and Patricia Melin. "Fuzzy rule-based models with interactive rules and their granular generalization." Fuzzy Sets and Systems, 307, 1-28. [LINK]
- 2016: Xingchen Hu, Witold Pedrycz, and Xianmin Wang. "Granular fuzzy rule-based models: A study in a comprehensive evaluation and construction of fuzzy models." IEEE Transactions on Fuzzy Systems, 25(5), 1342-1355. [LINK] [CODE]