张佳，暨南大学信息科学技术学院讲师，硕导。于2020年6月获厦门大学人工智能系博士学位，师从李绍滋教授。博士毕业后，加入暨南大学信息科学技术学院从事教学科研工作。研究方向为机器学习，数据挖掘，智能人机交互。主持国家自然科学基金1项、广东省面上项目1项，广州市科技计划项目1项，并参与了多项国家级/省部级课题研究，包括：国家重点研发计划子课题，国家自然科学基金联合基金重点项目，广东省基础与应用基础研究基金重点项目等。在人工智能国际权威期刊：IEEE TCYB、IEEE TNNLS、IEEE TPAMI、PR；脑科学国际权威期刊：IEEE TNSRE、JNE；及国际顶会：IJCAI等发表学术论文50余篇，其中SCI收录40余篇，ESI高被引论文4篇。据 Google Scholar 统计，论文被引用次数超1400次，第一作者单篇最高引用247次。现为IEEE会员（2023-），CCF专业会员（2020-），CCF人工智能与模式识别专委会委员，CCF协同计算专委会执委；担任国家自然科学基金评议专家，广州市科技局入库专家；担任TPAMI、TKDE、TCYB、TNNLS、TBD等IEEE汇刊、TKDD、PR、KAIS、INS等国际权威期刊的审稿人，AAAI、IJCNN等国际知名会议的程序委员会委员。

联系邮箱：jiazhang@jnu.edu.cn

通讯地址: 广东省广州市天河区黄埔大道西601号暨南大学（石牌校区）南海楼615室

研究方向是机器学习和数据挖掘。研究侧重点是多标记学习、弱标记学习、特征选择、以及信息融合。本人也对机器学习在人机交互、健康管理、以及生物信息学中的应用感兴趣。有志于未来从事相关研究的硕士研究生和保研本科生可与我邮件联系。

[1] Li, Y., et al. “Consistent and specific multi-view multi-label learning with correlation information.” Information Sciences, 2025, 687: 121395.

[2] Zhang, J., et al. “Toward cross-brain-computer interface: A prototype-supervised adversarial transfer learning approach with multiple sources.” IEEE Transactions on Instrumentation and Measurement, 2024, 73: 1-13.

[3] Zhang, J., et al. “Fast multilabel feature selection via global relevance and redundancy optimization.” IEEE Transactions on Neural Networks and Learning Systems, 2024, 35 (4): 5721-5734.

[4] Du, G., et al. “Semi-supervised imbalanced multi-label classification with label propagation.” Pattern Recognition, 2024, 150: 110358.

[5] Wu, H., et al. “Simplicial complex neural networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46 (1): 561-575.

[6] Zhang, J., et al. “Group-preserving label-specific feature selection for multi-label learning.” Expert Systems with Applications, 2023, 213: 118861. 

[7] Liu, D., et al. “Multi-source transfer learning for EEG classification based on domain adversarial neural network.” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, 31: 218-228.

[8] Du, G., et al. “Graph-based class-imbalance learning with label enhancement.” IEEE Transactions on Neural Networks and Learning Systems, 2023, 34 (9): 6081-6095.

[9] Zhang, J., et al. “Learning from weakly labeled data based on manifold regularized sparse model.” IEEE Transactions on Cybernetics, 2022, 52 (5): 3841-3854.

[10] Huang, Z.-A., et al. “Identification of autistic risk candidate genes and toxic chemicals via multi-label learning.” IEEE Transactions on Neural Networks and Learning Systems, 2021, 32 (9): 3971-3984.

[11] Zhang, J., et al. “Multi-label feature selection via global relevance and redundancy optimization.” In IJCAI, Yokohama, Japan, 2020, pp. 2512–2518. 

[12] Zhang, J., et al. “Manifold regularized discriminative feature selection for multi-label learning.” Pattern Recognition, 2019, 95: 136-150.

[1] 国家自然科学基金青年科学基金项目: 基于超高维标记与特征数据的多标记分类建模关键技术研究 (2022-2024), 62106084, PI

[2] 广东省自然科学基金面上项目: 融合多模态数据的弱监督多标记分类学习关键技术研究 (2022-2024), 2022A1515010468, PI

[1] 人工智能导论（本科课程，暨大通识课），秋季学期，2024

[2] 人工智能原理（本科课程），秋季学期，2021，2022，2023，2024

[3] 软件系统分析（本科课程），秋季学期，2022，2023，2024

[4] 软件系统分析实验（本科课程），秋季学期，2022，2023，2024

[5] 计算机文化（本科课程，英语授课），秋季学期，2021

期刊审稿：

Artificial Intelligence: IEEE Transactions on Pattern Analysis and Machine Intelligence; IEEE Transactions on Cybernetics; IEEE Transactions on Evolutionary Computation; IEEE Transactions on Neural Networks and Learning Systems; IEEE Transactions on Emerging Topics in Computational Intelligence; IEEE Transactions on Artificial Intelligence; Pattern Recognition; Neural Networks…
Data Mining: IEEE Transactions on Knowledge and Data Engineering; ACM Transactions on Knowledge Discovery from Data; Knowledge and Information Systems; IEEE Transactions on Big Data; Information Sciences…
Other Fields: IEEE Transactions on Multimedia; IEEE Transactions on Circuits and Systems for Video Technology; IEEE Journal of Biomedical and Health Informatics; IEEE Transactions on Industrial Informatics…

Program Committee: AAAI；IJCNN...

I am now a lecturer at the College of Information Science and Technology, Jinan University. I received the Ph.D. degree from the School of Informatics, Xiamen University, Xiamen, China, in 2020, supervised by Prof. Shaozi Li. I was a visiting student at the City University of Hong Kong, supervised by Prof. Kay Chen Tan.

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 in health care, bioinformatics, and brain-computer interactions. I have published over 50 academic papers in some prestigious journals and conferences, such as IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Pattern Recognition, and IJCAI.