I received the Ph.D. degree from the Artificial Intelligence Department, Xiamen University, Xiamen, China, in 2020, under the supervision of Prof. Shaozi Li. In 2019, I visited Kay Chen Tan’s group in the City University of Hong Kong for three months.
Email: zhangjia_gl@163.com
Mailing Address: Artificial Intelligence Department, School of Informatics, Haiyun Campus, Xiamen University, Xiamen, China, 361005
https://orcid.org/0000-0002-6079-2818Research & Publications
I am broadly interested in machine learning, data mining, and pattern recognition. I am generally interested in design, analysis, and implementation of algorithms for scientific computing problems. Now I am working on large-scale multi-label learning and various AI applications in medicine and education.
I have a few recent manuscripts available on-line: ( More Publications in Google Scholar Link)
- J. Zhang, S. Li, M. Jiang, K. C. Tan. Learning from weakly labeled data based on manifold regularized sparse model. IEEE Transactions on Cybernetics, in press. [code]
- Z.-A. Huang, J. Zhang, Z. Zhu, E. Q. Wu, K. C. Tan. Identification of autistic risk candidate genes and toxic chemicals via multi-label learning. IEEE Transactions on Neural Networks and Learning Systems, in press.
- J. Zhang, Y. Lin, M. Jiang, S. Li, Y. Tang, K. C. Tan. Multi-label feature selection via global relevance and redundancy optimization. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI’20), Yokohama, Japan, 2020, pp. 2512–2518. [code] [report] [poster]
- J. Zhang, Z. Luo, C. Li, C. Zhou, S. Li. Manifold regularized discriminative feature selection for multi-label learning. Pattern Recognition, 2019, 95: 136-150. [code]
- J. Zhang, C. Li, Z. Sun, Z. Luo, C. Zhou, S. Li. Towards a unified multi-source-based optimization framework for multi-label learning. Applied Soft Computing, 2019, 76: 425-435.
- J. Zhang, C. Li, D. Cao, Y. Lin, S. Su, L. Dai, S. Li. Multi-label learning with label-specific features by resolving label correlations. Knowledge-Based Systems, 2018, 159: 148-157.
- J. Zhang, C. Li, Y. Lin, Y. Shao, S. Li. Computational drug repositioning using collaborative filtering via multi-source fusion. Expert Systems with Applications, 2017, 84: 281-289.
- J. Zhang, Y. Lin, M. Lin, J. Liu. An effective collaborative filtering algorithm based on user preference clustering. Applied Intelligence, 2016, 45(2): 230–240.
Professional Activities
Journal Reviewer: IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition, Information Sciences, Knowledge-Based Systems, Applied Intelligence
PC Member: The International Joint Conference on Neural Networks (IJCNN 2021)
Useful Webpage Links
- Library: IEEE Xplore, Publications @DBLP, Publications @CNKI
- For reference purpose, there is a reasonable public CS conference ranking list provided by China Computer Federation: Ref.