计算机学院   软件学院

网络空间安全学院

School of Computer Science

个人信息教育信息获得荣誉项目论文其他

李云,男,工学博士,现为南京邮电大学计算机学院教授、博士生导师


研究方向:机器学习和数据挖掘的理论、算法及应用,特别关注高维复杂数据的维数约简、大规模数据的学习、安全机器学习和未标记数据的利用问题



教育背景:

2005年7月毕业于重庆大学计算机学院,获得计算机软件与理论专业博士学位
2005/7-2007/9,上海交通大学计算机系,博士后,合作导师:吕宝粮
2007/9-2013/8,南京邮电大学,计算机学院、软件学院,副教授
2012/8-2013/7,美国亚利桑那州立大学,电子、计算机和能量工程学院,访问学者
2013/8-至今,南京邮电大学,计算机学院、软件学院,教授

  • 江苏省大数据安全与智能处理重点实验室大数据挖掘方向带头人
  • 2012年江苏省“青蓝工程”优秀青年骨干教师
  • 2016年江苏省“青蓝工程”中青年学术带头人
  • IEEE数据挖掘和大数据分析技术委员会(DMBDTC)成员(2018)
  • 中国人工智能学会机器学习专委会委员
  • 中国计算机学会人工智能与模式识别专委会委员
  • 江苏省计算机学会大数据专家委员会副主任委员
  • 江苏省计算机学会人工智能专委会委员
  • 江苏省人工智能学会数据挖掘与应用专委会秘书长
  • 江苏省人工智能学会机器学习专委会常务委员
  • 国际神经信息处理会议ICONIP2011的共同出版主席
  • 国际智能系统与知识工程会议ISKE2017程序委员会主席

1. 国家重点研发计划课题,2018YFB1003702,大规模云数据中心运行能效评估与预测关键技术,2018/05-2021/04,在研,主持

2. 国家自然科学基金面上项目,61772284,隐私保护的对抗性特征选择及其拓展研究,2018/01-2021/12,在研,主持

3. 江苏省“青蓝工程”中青年学术带头人,2016年

4. 国家自然科学基金青年基金项目,61603197,基于局部学习的集成特征选择稳定性及其拓展研究,2017/01-2019/12,在研,参与(2/10)

5. 江苏省自然科学基金青年基金项目,BK20140885,稳定集成特征选择的分层过滤器模型研究,2014/07-2017/06,结题,参与(2/9)

6. 江苏省自然科学基金面上项目,BK20131378,基于多核Map-Reduce的半监督模块化集成学习研究,2013/07-2016/06,已结题,主持

7. 江苏省“青蓝工程”优秀青年骨干教师,2012年

8. 国家自然科学基金面上项目,61073114,基于能量学习的特征选择方法及其应用研究,2011/01-2013/12,已结题,主持

9. 江苏省高校自然科学基金,08KJB520008,大规模分类问题的集成学习方法研究,2009/01-2010/12,已结题,主持。

最佳论文奖 Best paper award

• Bin Xia, Yun Li, Qianmu Li, and Tao Li: Attention-based recurrent neural network for location recommendation, 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, China, 2017.11.24-2017.11.26

一、期刊论文

[1] Keji Han, Yun Li, and Jie Hang: Adversary Resistant Deep Neural Networks via Advanced Feature Nullification, Knowledge-Based Systems, 179, 108-116, 2019

[2] Kejia Chen, Hao Lu, Yun Li, and Bin Liu: On Relationship Formation in Heterogeneous Information Networks: An Inferring Method based on Multi-Label Learning, Statistical Analysis and Data Mining: The ASA Data Science Journal, 12(3), 157-167, 2019

[3] Yunyun Wang, Jie Zhai, Yun Li, Kejia Chen, and Hui Xue: Transfer Learning with partial related “instance-feature” knowledge. Neurocomputing, 310, 115-124, 2018

[4] Wei Ji, Yixiang Huang, Baohua Qiang, and Yun Li: Min-Max Ensemble Feature Selection, Journal of Intelligent & Fuzzy Systems, 33(6), 3441-3450, 2017

[5] Yunyun Wang, Yan Meng, Yun Li, Songcan Chen, Zhenyong Fu, and Hui Xue: Semi-supervised manifold regularization with adaptive graph construction, Pattern Recognition Letters, 98, 90-95, 2017

[6] Yun Li, Tao Li, and Huan Liu: Recent advances in feature selection and its applications Knowledge and Information Systems, 53(3): 551-577, 2017

[7] Yun Li, Jun Yang, Wei Ji: Local learning-based feature weighting with privacy preservation, Neurocomputing, 174, 1107-1115, 2016

[8] Wei Ji, Kejia Chen, Guojing Zhou, Yun Li: Local energy-basedframework for feature ranking, Journal of Intelligent & Fuzzy Systems, 28(4), 1565-1575, 2015

[9] Yun Li, Jennie Si, Guojing Zhou, Shasha Huang, and Songcan Chen: FREL:A stable feature selection algorithm, IEEE Transactions on Neural Networks and Learning Systems, 26(7), 1388-1402, 2014

[10] Wei Ji, and Yun Li: Energy-based feature ranking for assessing the dysphonia measurements in Parkinson detection, IET Signal Processing, 6(4), 300-305, 2012

[11] Yun Li, and Baoliang Lu: Feature selection based on loss-margin of nearest neighbor classification, Pattern Recognition, 42(9), 1914-1921, 2009

[12] Yun Li, and Baoliang Lu: Teng-Fei Zhang: Combining feature selection withextraction: unsupervised feature selection based on principal component analysis, International Journal on Artificial Intelligence Tools, 18(06), 883-904, 2009

[13] Yun Li, and Zhongfu Wu: Fuzzy feature selection based on min-max learning rule and extension matrix, Pattern Recognition, 41(1), 217-226, 2008

[14] Yun Li, Baoliang Lu, and Zhongfu Wu: Hierarchical fuzzy filtermethod for unsupervised feature selection, Journal of Intelligent & FuzzySystems, 18(2), 157-169, 2007

二、会议论文

[1] Yu Wang, Yun Li, Ziye Zhu, Bin Xia, and Zheng Liu, SC-NER: A Sequence-to-Sequence Model with SentenceClassification for Named Entity Recognition, Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD), Macau, China, 2019.4.14.-2019.4.17

[2] Jie Hang, Keji Han, and Yun Li Delving into Diversity in Substitute Ensemblesand Transferability of Adversarial Examples, International Conference on Neural Information Processing ICONIP 2018, LNCS 11303, pp. 175–187, Siem Reap, Cambodia, 2018.12.13-2018.12.16

[3] Miaomiao Wu, and Yun Li: Adversarial mRMR against Evasion Attacks, Proceedings of International Joint Conference on Neural Networks IJCNN2018, Rio, Brazil, 2018.7.8-2018.7.13

[4] Zhongfeng Liu, Yun Li and Wei Ji: Differential Private ensemble feature selection, Proceedings of International Joint Conference on Neural Networks IJCNN2018, Rio, Brazil, 2018.7.8-2018.7.13

[5] Xuedong Hou, Yun Li and Tao Li: Weakly-supervised Dual Generative Adversarial Networks for Makeup-removal, International Conference on Neural Information Processing (ICONIP), Guangzhou, China, 2017.11.14-2017.11.18.

[6] Peng Yan, and Yun Li: Graph-margin based multi-label feature selection, European Conference on Machine Learning (ECML), Riva del Garda, Italy, 2016.9.19-2016.9.23

[7] Yixiang Huang, Yun Li, and Baohua Qiang: Internet traffic classificationbased on min-max ensemble feature selection, International Joint Conference onNeural Networks (IJCNN), Vancouver, Canada, 2016.7.24-2016.7.29

[8] Wei Ji, and Yun Li: Stable dysphonia measures selection for Parkinsonspeech rehabilitation via diversity regularized ensemble, 41st IEEEInternational Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 2016.3.20-2016.3.25

[9] Yanping Wu, and Yun Li: Semi-supervised min-max modular SVM, International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 2015.7.11-2015.7.16

[10] Jun Yang, and Yun Li: Differentially private feature selection, International Joint Conference on Neural Networks (IJCNN), Beijing, China, 2014.7.6-2014.7.11

[11] Yun Li, Shasha Huang, Songcan Chen, and Jennie Si: Stable L2-regularized ensemble feature weighting, International Workshop on Multiple Classifier Systems (MCS), Lecture Notes in Computer Sciences (LNCS), Nanjing, China, 2013.5.15-2013.5.17

[12] Yun Li , Suyan Gao, and Songcan Chen: Ensemble feature weighting basedon local learning and diversity, Twenty-Sixth AAAI Conference on ArtificialIntelligence (AAAI), Toronto, Canada, 2012.7.22-2012.7.26

[13] Yun Li, and Lili Feng: Integrating feature selection and min-max modular SVM for powerful ensemble, International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, 2012.6.10-2012.6.15

[14] Yun Li, and Suyan Gao: Energy-based feature selection and its ensembleversion, International Conference on Neural Information Processing (ICONIP), Lecture Notes in Computer Science(LNCS), Shanghai, China, 2011.11.13-2011.11.18

[15] Xiaomin Xie, and Yun Li: Bisecting data partitioning methods formin-max modular support vector machine, IEEE International Conference on FuzzySystems and Knowledge Discovery (FSKD), Shanghai, China, 2011.7.26-2011.7.28

[16] Yun Li, Sujun Hu, Wenjie Yang, Guozi Sun, Fangwu Yao, and Geng Yang: Similarity-Based Feature Selection for Learning from Examples withContinuous Values, 13th Pacific-Asia Conference on Knowledge and Data Mining(PAKDD), Bangkok, Thailand, 2009.4.27-2009.4.30

[17] Yun Li, Baoliang Lu, and Zhongfu Wu: A hybrid method of unsupervisedfeature selection based on ranking, International Conference on PatternRecognition (ICPR), Hong Kong, China, 2006.9.3-2006.9.7

其他待添加