I am currently a Lecturer at the College of Information Science and Technology of Jinan University. I received my Ph.D. degree at the School of Informatics, Xiamen University, Xiamen, China, in 2020, supervised by Prof. Shaozi Li. In 2019, I was a visiting student at the Department of Computer Science of City University of Hong Kong, Hong Kong SAR, supervised by Prof. Kay Chen Tan. I am an IEEE member (2023 - data).
Research Interests: My research interests include machine learning and data mining. In particular, I am interested in multi-label learning, weak label learning, feature selection, and information fusion. I am also interested in various machine learning applications, such as human-computer interaction and TCM health management.
Email: jiazhang@jnu.edu.cn
Publications and Citations: at DBLP (2016 - date) and by Google Scholar
- Xu, G., et al. “Probability distribution alignment and low-rank weight decomposition for source-free domain adaptive brain decoding.” In AAAI, Singapore, 2026, pp. 27233-27241.
- Zhang, Z., et al. “ORAL: Adaptive gap increasing for advantage learning via Occam’s Razor principle.” IEEE Trans. Neural Netw. Learn. Syst., 2026, 37 (4): 1904-1918.
- Zhang, J., et al. “EEG feature selection in emotion recognition using a fuzzy information-theoretic based optimization approach.” IEEE Trans. Fuzzy Syst., 2025, 33 (8): 2675-2688.
- Ye, Q., et al. “SMLE: Semi-supervised multi-label learning with label enhancement.” IEEE Trans. Knowl. Data Eng., 2025, 37 (9): 5613-5626. [code]
- Li, Y., et al. “Consistent and specific multi-view multi-label learning with correlation information.” Inf. Sci., 2025, 687: 121395.
- Wu, H., et al. “Cold-start user recommendation via heterogeneous domain adaptation.” ACM Trans. Inform. Syst., 2025, 43 (5): 1-26. [code]
- Zhang, J., et al. “Toward cross-brain-computer interface: A prototype-supervised adversarial transfer learning approach with multiple sources.” IEEE Trans. Instrum. Meas., 2024, 73: 1-13. [code]
- Zhang, J., et al. “Fast multilabel feature selection via global relevance and redundancy optimization.” IEEE Trans. Neural Netw. Learn. Syst., 2024, 35 (4): 5721-5734. [Supplement]
- Du, G., et al. “Semi-supervised imbalanced multi-label classification with label propagation.” Pattern Recognit., 2024, 150: 110358.
- Wu, H., et al. “Simplicial complex neural networks.” IEEE Trans. Pattern Anal. Mach. Intell., 2024, 46 (1): 561-575.
- Wu, H., et al. “High-order proximity and relation analysis for cross-network heterogeneous node classification.” Mach. Learn., 2024, 113: 6247-6272. [code]
- Zhang, J., et al. “Group-preserving label-specific feature selection for multi-label learning.” Expert Syst. Appl., 2023, 213: 118861. [code]
- Du, G., et al. “Graph-based class-imbalance learning with label enhancement.” IEEE Trans. Neural Netw. Learn. Syst., 2023, 34 (9): 6081-6095.
- Liu, D., et al. “Multi-source transfer learning for EEG classification based on domain adversarial neural network.” IEEE Trans. Neural Syst. Rehabil. Eng., 2023, 31: 218-228.
- Wu, H., et al. “Cold-start next-item recommendation by user-item matching and auto-encoders.” IEEE Trans. Serv. Comput., 2023, 16 (4): 2477-2489. [code]
- Zhang, J., et al. “Learning from weakly labeled data based on manifold regularized sparse model.” IEEE Trans. Cybern., 2022, 52 (5): 3841-3854. [code]
- Liu, S., et al. “Subject adaptation convolutional neural network for EEG-based motor imagery classification.” J. Neural Eng., 2022, 19 (6): 066003.
- Tan, A., et al. “Semi-supervised partial multi-label classification via consistency learning.” Pattern Recognit., 2022, 131: 108839.
- Huang, Z.-A., et al. “Identification of autistic risk candidate genes and toxic chemicals via multi-label learning.” IEEE Trans. Neural Netw. Learn. Syst., 2021, 32 (9): 3971-3984.
- Zhang, J., et al. “Multi-label feature selection via global relevance and redundancy optimization.” In IJCAI, Yokohama, Japan, 2020, pp. 2512–2518. [code] [report] [poster]
- Zhang, J., et al. “Manifold regularized discriminative feature selection for multi-label learning.” Pattern Recognit., 2019, 95: 136-150. [code]
Native Publications:
- 杜国栋, 王敖, 路鹏伟, 叶倩芝, 张佳. 复杂数据背景下的多标记学习研究进展. 计算机工程与应用, 2026, 62 (11): 41-61. [paper]
- 赵文, 张佳, 徐佳君, 辛基梁, 周常恩, 李绍滋, 李灿东. 四诊合参智能化发展现状及实现路径. 中医杂志, 2020, 61 (1): 58-62, 67.
Selected Funded Research Projects
- “Multilabel Classification Modeling with Ultrahigh Dimensional Label and Feature Data”, National Natural Science Foundation of China (2022-2024), PI
- “Weakly Supervised Multilabel Classification Modeling with Multi-Modal Data”, Natural Science Foundation of Guangdong Province, China (2022-2024), PI
Professional Activities
Journal Reviewer:
- Machine Learning: IEEE Trans. Artif. Intell.; IEEE Trans. Cybern.; IEEE Trans. Emerg. Topics Comput. Intell.; IEEE Trans. Evol. Comput.; IEEE Trans. Fuzzy Syst.; IEEE Trans. Neural Netw. Learn. Syst.; IEEE Trans. Pattern Anal. Mach. Intell.; Mach. Learn.; Neural Netw.; Pattern Recognit.…
- Data Mining: ACM Trans. Knowl. Discov. Data; IEEE Trans. Big Data; IEEE Trans. Knowl. Data Eng.; Inform. Process. Manag.; Inf. Sci.; Knowl. Inf. Syst.…
- Human-Computer Interaction: IEEE J. Biomed. Health Inform.; IEEE Trans. Affective Comput.; IEEE Trans. Autom. Sci. Eng.; IEEE Trans. Biomed. Eng.; IEEE Trans. Cognit. Dev. Syst.; IEEE Trans. Hum.-Mach. Syst.; IEEE Trans. Neural Syst. Rehabil. Eng.; IEEE Trans. Syst. Man Cybern., Syst.; J. Neural Eng.…
- Others: IEEE Trans. Circuits Syst. Video Technol.; IEEE Trans. Consum. Electron.; IEEE Trans. Image Process.; IEEE Trans. Ind. Informat.; IEEE Trans. Multimedia…
- Native Journals: IEEE-CAA J. Automatica Sin.; Sci. China Inf. Sci.…
PC Members: NeurIPS; AAAI; IJCNN; ChineseCSCW…