Jia Zhang's Homepage

Short Biography: I am currently a Lecturer in the College of Information Science and Technology of Jinan University. I got my PhD 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, Hong Kong SAR, in 2019, supervised by Prof. Kay Chen Tan.

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 TCM health management and human-computer interactions.

Email: jiazhang@jnu.edu.cn

Selected Publications [Google Scholar Citations]

Li, Y., et al. “Consistent and specific multi-view multi-label learning with correlation information.” Information Sciences, 2025, 687: 121395.
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. [code]
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. [Supplement]
Du, G., et al. “Semi-supervised imbalanced multi-label classification with label propagation.” Pattern Recognition, 2024, 150: 110358.
Wu, H., et al. “Simplicial complex neural networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46 (1): 561-575.
Wu, H., et al. “High-order proximity and relation analysis for cross-network heterogeneous node classification.” Machine Learning, 2024, 113: 6247-6272. [code]
Zhang, J., et al. “Group-preserving label-specific feature selection for multi-label learning.” Expert Systems with Applications, 2023, 213: 118861. [code]
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.
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.
Wu, H., et al. “Cold-start next-item recommendation by user-item matching and auto-encoders.” IEEE Transactions on Services Computing, 2023, 16 (4): 2477-2489. [code]
Zhang, J., et al. “Learning from weakly labeled data based on manifold regularized sparse model.” IEEE Transactions on Cybernetics, 2022, 52 (5): 3841-3854. [code]
Liu, S., et al. “Subject adaptation convolutional neural network for EEG-based motor imagery classification.” Journal of Neural Engineering, 2022, 19 (6): 066003.
Tan, A., et al. “Semi-supervised partial multi-label classification via consistency learning.” Pattern Recognition, 2022, 131: 108839.
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.
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 Recognition, 2019, 95: 136-150. [code]
Zhang, J., et al. “Towards a unified multi-source-based optimization framework for multi-label learning.” Applied Soft Computing, 2019, 76: 425-435.

Native Publication:

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:

Program Committee: AAAI; IJCNN…

Teaching Experience

Introduction to Artificial Intelligence (for undergraduate students), Fall, 2024
AI Principles (for undergraduate students), Fall, 2021, 2022, 2023, 2024
Case Study of Software Systems (for undergraduate students), Fall, 2022, 2023, 2024
Computer Fundamentals (for undergraduate students, in English), Fall, 2021

Talks

“Discriminative Feature Learning Algorithms for Multilabel Classification”, Minnan Normal University, Zhangzhou, June 18, 2023
“Study on Several Multilabel Learning Topics”, Huaqiao University, Xiamen, May 19, 2022
“Identification of Health State Based on Large-Scale Traditional Chinese Medicine Data”, Jinan University, Guangzhou, June 19, 2021
“Learning from Multilabel Data under Complex Environments”, Zhejiang Ocean University, Zhoushan, June 6, 2020