个人信息

姓　　名: 李云 性　　別: 男 导师类型: 博士生导师
技术职称: 教授 电子邮箱: liyun@njupt.edu.cn
博士招生学科: (0810Z2)信息网络
学术型硕士招生学科: (081200)计算机科学与技术
专业型硕士招生类别(领域): (085400)电子信息

个人简介: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年7月到2007年9月，在上海交通大学计算机科学与技术流动站从事博士后研究，主要在上海交通大学仿脑计算与机器智能研究中心进行机器学习和模式识别的研究。2007年10月来南京邮电大学计算机学院工作至今。在此期间于2012年8月到2013年7月在美国亚利桑那州立大学访学。吴文俊人工智能科学技术奖获得者（2019），2016年江苏省“青蓝工程”中青年学术带头人，IEEE会员，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，江苏省人工智能学会数据挖掘与应用专委会秘书长，江苏省计算机学会大数据专委会副主任委员，江苏省人工智能学会机器学习专委会常委。国际智能系统与知识工程会议ISKE2017程序委员会共同主席、国际神经信息处理会议ICONIP2011的共同出版主席。多个国内外知名期刊的审稿人和多个国际主流会议的程序委员会委员，如IEEE Trans. Neural Networks and Learning Systems, AAAI2017、KDD2017等。主持 “云计算和大数据”方向的国家重点研发计划课题1项，国家自然科学基金面上项目2项，江苏省自然科学基金1项。发表学术论文60余篇，包括发表在本领域顶级期刊和会议，如IEEE Trans. Neural Networks and Learning Systems, Pattern Recognition,Knowledge and Information Systems, AAAI, IJCAI, ECML，ICASSP等。


研究领域:（1）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。并将研究成果应用到信息安全、信号处理和认知无线电等；（2）利用高性能计算或分布式计算解决大规模机器学习问题；（3）机器学习和生物信息学的交叉研究；（4）模式识别，主要研究基于生物特征的医疗健康监测技术。


代表性论文：
[1] Yun Li, Tao Li and Huan Liu, Recent advances in featureselection and its applications. Knowledgeand Information Systems,53(3), 551-577, 2017.

[2] Miaomiao Wu, YunLi. Adversarial mRMR against evasion attacks, International Joint Conference on Neural Networks (IJCNN 2018), Riode Janeiro, Brazil, 2018.7.8-7.13.
[]ZhongfengLiu, Yun Li and Wei Ji. Differential private ensemble feature selection, International Joint Conference on Neural Networks (IJCNN 2018), Rio de Janeiro, Brazil, 2018.7.8-7.13.
KejiHan, Yun Li, Jie Hang. Adversaryresistant deep neural networks via advanced feature nullification, Knowledge-Based Systems, 179:108-116,2019.
Ziyu Shen, Xusheng Zhang, Bin Xia,Zheng Liu, Yun Li. Multi-GranularityPower Prediction for Data Center Operations via Long Short-Term Memory Network.IEEE International Symposium on Paralleland Distributed Processing with Applications (IEEE ISPA 2019). Xiamen, China. 2019.12.16-2019.12.18
Jie Hang, Keji Han, Hui Chen, YunLi. Ensemble adversarial black-boxattacks against deep learning systems. Pattern Recognition,v 101, May 2020, 在线发表.
石鑫盛,李云.公平性机器学习中基于分类间隔的歧视样本发现和消除算法研究,中国科学: 信息科学, 2020, 在线发表[1] Yun Li, Jennie Si, Guojing Zhou, Shasha Huang and Songcan Chen. “FREL:A stable feature selection algorithm”. IEEE Trans. Neural Networks and Learning Systems, vol.26, pp.1388-1402, 2015. [2] Yun Li, Suyan Gao and Songcan Chen. “Ensemble feature weighting based on local learning and diversity”. Twenty-Sixth Conf. Artificial Intelligence (AAAI), vol.2, pp.1019-1025, Toronto, Canada, 2012.[3] Yun Li and Bao-Liang Lu. “Feature selection based on loss-margin of nearest neighbor classification”. Pattern Recognition, vol.42, pp.1914-1921, 2009. [4] Yun Li and Zhong-Fu Wu. “Fuzzy feature selection based on min-max learning rule and extension matrix”. Pattern Recognition, vol.41, pp. 217-226, 2008. [5] Yun Li, Jun Yang, Wei Ji. Local learning-based feature weighting with privacy preservation. Neurocomputing, vol. 174, pp. 1107-1115, 2016.[6] Wei Ji and Yun Li. “Stable dysphonia measures selection for Parkinson speech rehabilitation via diversity regularized ensemble”. Proc. 41st IEEE Int’l Conf. on Acoustics, Speech and Signal Processing( ICASSP), Shanghai, P. R. China, 2016.[7] Peng Yan and Yun Li. “Graph-Margin Based Multi-Label Feature Selection”. European Conference on Machine Learning, ECML, Riva del Garda, Italy, 2016.[8] Yingxiang Huang, Yun Li and Baohua Qiang. “Internet traffic classification based on min-max ensemble feature selection”. Proc. Int’l Joint Conf. on Neural Networks (IJCNN), pp. 3485-3492, Vancouver, Canada, 2016.[9] Wei Ji, Yun Li and Kejia Chen. “Local energy-based framework for feature ranking”. Journal of Intelligent & Fuzzy Systems, vol. 28, pp. 1565-1575, 2015. [10] Yanping Wu and Yun Li. “Semi-supervised Min-Max Modular SVM”. Int’l Joint Conf. Neural Networks (IJCNN), Killarney, Ireland, 2015.[11] Jun Yang and Yun Li, “Differentially private feature selection”, Proc. Int’l Joint Conf. on Neural Networks (IJCNN), pp.4182-4189, 2014.[12 Kai Chen, Wenqiang Wan and Yun Li. “Differentially private feature selection under MapReduce framework”, The Journal of China Universities of Posts and Telecommunications, vol. 20(5), pp.85-90, 2013.[13] Yun Li, Shasha Huang, Songcan Chen and Jennie Si. “Stable L2-regularized ensemble feature weighting”, Int’l Workshop on Multiple Classifier Systems (MCS), Lecture Notes in Computer Sciences (LNCS), vol.7872, pp.167-178, 2013.[14] Wei Ji and Yun Li. “Energy-based feature ranking for assessing the dysphonia measurements in parkinson detection”, IET Signal Processing, vol.6(4), pp. 300-305, 2012.[15] Yun Li and Li-Li Feng. “Integrating feature selection and min-max modular SVM for powerful ensemble”. Int’l Joint Conf. Neural Networks (IJCNN), vol.1, pp.1-8, Brisbane, Australia, 2012. [16] Yun Li and Su-Yan Gao. “Energy-based feature selection and its ensemble version”, Int’l Conf. Neural Information Processing (ICONIP), Lecture Notes in Computer Science (LNCS), vol.7063, pp.53-62, 2011. [17] Xiao-Min Xie and Yun Li. “Bisecting data partitioning methods for min-max modular support vector machine”, IEEE Int’l Conf. Fuzzy System and Knowledge Discovery (FSKD), pp.992-996, 2011. [18] Yun Li, Bao-Liang Lu and Teng-Fei Zhang. “Combining feature selection with extraction: unsupervised feature selection based on principal component analysis”. International Journal on Artificial Intelligence Tools, vol.18, pp.883-904, 2009. [19] Yun Li, Su-Jun Hu, Wen-Jie Yang, Guo-Zi Sun, Fang-Wu Yao and Geng Yang. “Similarity-based feature selection for learning from examples with continuous values”. Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD), Lecture Notes in Artificial Intelligence (LNAI), vol.5476, pp.957-964, Bangkok, Thailand, 2009. [20] Yun Li, Bao-Liang Lu and Zhong-Fu Wu, “Hierarchical fuzzy filter method for unsupervised feature selection”. Journal of Intelligent & Fuzzy Systems, vol.18, pp.157-169, 2007. [21] Yun Li, Bao-Liang Lu and Zhong-Fu Wu. “A hybrid method of unsupervised feature selection based on ranking”. Proc. of Int’l Conf. on Pattern Recognition (ICPR), pp.687-690, Hong Kong, P. R. China, 2006.