[1] Xiaocui Li, Yu Xiao, Xinyu Zhang, Qingyu Shi, Xiance Tan: ImMC-CSFL: Imbalanced Multi-view Clustering Algorithm Based On Common-Specific Feature Learning, PAKDD 2024. (CCF C类会议)

[2] Xiaocui Li, Hongzhi Yin, Ke Zhou, Xiaofang Zhou. Semi-supervised clustering with deep metric learning and graph embedding, World Wide Web Journal 2020, 23(2): 781-798. (CCF B类期刊)

[3] Xiaocui Li, Zhou Ke, Li Chunhua, Zhang Xinyu, Liu Yu, Wang Yangtao. Multi-view clustering via neighbor domain correlation learning, Neural Computing and Applications, 2021, 33(8): 3403-3415. (中科院二区)

[4] Xiaocui Li, Yangtao Wang, Jingkuan Song, Yu Liu, Xinyu Zhang, Ke Zhou, Chunhua Li, A low cost and un-cancelled laplace noise based differential privacy algorithm for spatial decompositions, World Wide Web Journal 2020, 23(1): 549-572.(CCF B类期刊)

[5] Xiaocui Li, Hongzhi Yin, Ke Zhou, Hongxu Chen, Shazia W. Sadiq, Xiaofang Zhou. Semi-supervised Clustering with Deep Metric Learning, DASFAA 2019. ( CCF B类会议)

[6] Zhang Xinyu, Xiaocui Li^#, Jing Xiao-Yuan, and Cheng Li. Simultaneous Image Reconstruction and Feature Learning with 3D-CNNs for Image Set Based Classification, ACM Transactions on Data Science 2021, 2(2): 12:1-12:13. (共同第一作者）

[7] Xiaocui Li, Yangtao Wang, Xinyu Zhang, Ke Zhou, Chunhua Li. A More Secure Spatial Decompositions Algorithm via Indefeasible Laplace Noise in Differential Privacy. ADMA 2018. (最佳学生论文，CCF C类会议)

[8] Xinyu Zhang, Jing Xiao-Yuan, Xiaocui Li*, Jiagang Liu: Multi-class Imbalanced Data Classification by Deep Multi-set Discriminant Metric Learning with Optimal Balance Sampling, DASFAA 2024. (CCF B类会议)

[9] Zhixiao Wang, Chengcheng Sun, Jingke Xi, Xiaocui Li*. Influence maximization in social graphs based on community structure and node coverage gain, Future Generation Computer Systems 2021, 118: 327-338. (中科院一区)

[10] Yangtao Wang, Yanzhao Xie, Yu Liu, Ke Zhou, Xiaocui Li. Fast Graph Convolution Network Based Multi-label Image Recognition via Cross-modal Fusion. CIKM 2020. (CCF B 类会议)

[11] 李晓翠, 孟凡荣, 周勇.一种基于代表点的快速聚类算法. 南京大学学报:自然科学版. 2012. 48(4):504-512.

[12] 孟凡荣, 李晓翠, 周勇. 一种基于代表点的增量聚类算法. 计算机应用研究. 2012.29(8): 2865-2867.

[13] 李晓翠, 张新玉, 罗庆云, 任长安．基于统计特征向量的时序符号化改进算法．计算机工程,2015,41(010):155-159.

招生说明：

课题组主要研究方向包括人工智能、数据挖掘、机器学习、数据隐私保护等，并与华中科技大学武汉光电国家研究中心保持长期学术交流与合作。希望申请本人方向的学生具有以下基本素养：1）积极乐观，勤奋好学，具有较强的解决问题的能力；2）与老师、同学沟通顺畅，无不良嗜好；3）具有较较强的编程能力；4）英语水平较高，能够阅读及撰写英文论文。对有志于攻读硕士的本科同学或攻读博士的硕士同学，请尽早与我联系，请你在电子邮件中详细说明你的各方面情况，包括你选择读研（读博）的原因，以及你的目标及未来打算等，本人将给予最中肯的建议和全力帮助。