在国内外发表科研论文30余篇，其中SCI收录17篇，EI收录4篇，Google Scholar 他引超过300次，单篇最高他引66次。主要学术成果如下：
 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, Yokohama, Japan, 2020, pp. 2512–2518. [code]
 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.
Jia Zhang received the Ph.D. degree from the Artificial Intelligence Department, Xiamen University, Xiamen, China, in 2020. He is broadly interested in machine learning, data mining, and artificial intelligence. Now he is working on large-scale multi-label learning and various AI applications in medicine and education. He has published over 30 academic papers in prestigious journals and conferences, such as IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition, Information Sciences, Expert Systems with Applications, and IJCAI. His papers have been cited more than 300 times (by Google scholar).
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