李晓翠副教授简介

发表时间：2026-02-03 点击次数：

姓名：李晓翠

所属学科：计算机科学与技术

职称：副教授

E-mail:xiaocuiworld@163.com

研究领域：数据挖掘、多模态融合、机器学习

教育背景：

2006.9~2010.6中国矿业大学 本科

2010.9~2013.6中国矿业大学 硕士

2016.9~2020.12华中科技大学 工学博士

2018.4~2019.4澳大利亚 昆士兰大学

主要科研课题：

[1] 国家自然科学基金青年项目，2026.1-2028.12，主持，在研。

[2]湘江实验室开放基金，2025/11-2026/11，主持，在研。

[3] 国家科技部重点研发子课题，2023/10-2026/10，骨干成员，在研。

[4] 湖南省自然科学基金项目，2023/01-2025/12，主持，结题。

[5] 湖南省教育厅优秀青年项目，2022/01-2025/01，主持，结题。

[6] 湘江实验室开放基金，2022/11-2023/11，主持，结题。

[7] 湘江实验室重大项目，2022/11-2025/11，参与，结题。

主要论文：

[1] Xiaocui Li, Guoliang Li, Xinyu Zhang, Jie Wen, Lian Wu. CrossNet-VGA: Variational Collaboration and Graph Attention Fusion for Incomplete Multi-View Clustering, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 34, 2025: 7448-7461. (CCF A类期刊)

[2] Yangtao Wang, Xingwei Deng, Yanzhao Xie, Xiaocui Li*. End-to-End Knowledge Distillation for Unsupervised Domain Adaptation with Large Vision-language ModelsDomain Adaptation with Large Vision-language Models. AAAI 2026. (CCF A类会议)

[3] Hongxing You, Yangtao Wang,*, Xiaocui Li. Graph-guided Cross-image Correlation Learning withAdaptive Global-local Feature Fusion for Fine-grained Visual Representation, INFORMATION FUSION, 2026. (中科院一区)

[4] Guodong Yi, Jianxu Zhang, Xinyu Zhang, Jian Ren, Wei Liang, Xiaocui Li*. FedCD: Contrastive-Distillation Regularization for Heterogeneous Data in Federated Learning. PATTERN RECOGNITION, 2026. (中科院一区)

[5] Xiaocui Li, Dan He, Xinyu Zhang: Efficient Algorithms for Approximate k-Radius Coverage Query on Large-Scale Road Networks. IEEE Trans. Intell. Transp. Syst. 2025, 26(2): 1631-1644. (中科院一区)

[6] 李晓翠, 陈伟立, 张新玉*, 文杰, 李晶晶. RDS-Net: 采用双感知形状迁移的递归结构细化网络实现点云补全. 《电子学报》，2026.

[7] Xiaocui Li, Guoliang Li, Xinyu Zhang, Yangtao Wang, Qingyu Shi, Wei Liang: Incomplete Multi-view Clustering via Local Reasoning and Correlation Analysis. WSDM 2025 (CCF B类会议)

[8] Xinyu Zhang, Xiao-yuan Jing, 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] Yi Xie, Yangtao Wang, Yanzhao Xie, Xin Tan, Jingjing Li, Xiaocui Li*, Weilong Peng, Maobin Tang, Meie Fang: Image-text Retrieval with Main Semantics Consistency. CIKM 2024: 2629-2638 (CCF B类会议)

[10] 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)

[11] 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)

[12] 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) 

[13] 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) 

[14] 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)

[15] 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）

[16] 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)

[17] 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)  

[18] 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)

[19] 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.

[20] 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.

[21] 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。