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张佳。于2020年获厦门大学人工智能系博士学位,师从李绍滋教授 。攻读博士学位期间,受厦门大学资助在香港城市大学Kay Chen Tan 教授团队进行了为期三个月的学术交流。电子邮件:zhangjia_gl@163.com

研究兴趣:机器学习,数据挖掘,模式识别。目前主要关注:大规模多标记学习,以及人工智能在医疗和教育中的应用。

项目经历:目前/曾参与国家重点研发计划子课题,国家自然科学基金联合重点项目,国家自然科学基金面上项目,福建省 2011 中医健康管理协同创新中心项目等。

英文主页:https://jiazhang-ml.pub/

主要成果

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76次。主要学术成果如下:

[1] 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]

[2] 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.

[3] 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]

[4] 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]

[5] 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.

主要荣誉

2020年获中华中医药学会科学技术奖(科技进步类)一等奖(排名:10/15)

2020年获厦门大学优秀毕业生(博士)

Biography

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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