Liang Du[中文简历] [Bio]

Liang Du is an assistant researcher, worked with the data mining group lead by Prof. Yi-Dong Shen at the State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China.

Email: csliangdu [at] gmail.com, duliang [at] ios.ac.cn
Address: 3rd Floor, Building 5, Software Park, No.4, South 4th Street, Zhongguancun, Haidian District, Beijing, China, 100190


Interests | Education | Work Experiences | Publications | Academic Services | Fundings | Awards


What's New


Research Interests

I have broader interests in Data Mining, Machine Learning, Big Data Analysis. I am particularly interested in the following topics:

Education


Work Experience


Publications

Research Highlights: KDD(1), IJCAI(4), AAAI(1), ICDM(6), TKDE(1).

[Google Scholar] [DBLP]

Journal and Conference Papers (* = corresponding author)

    2015

  1. Hanmo Wang, Liang Du*, Peng Zhou, Lei Shi, YuHua Qian and Yi-Dong Shen. Localized Multiple Kernel Experimental Design. in Proceedings of the Fifteenth IEEE International Conference on Data Mining (ICDM), pages xxx-xxx, Atlantic City, NJ, USA, November 14–17, 2015, To appear. (Regular paper, acceptance rate 68/807 = 8.4%).

  2. NanNan Gu, MingYu Fan, Di Wang, LiHao Jia and Liang Du. Semi-supervised classification based on affine subspace sparse representation. Science in China-Series F: Information Sciences, 2015, To appear. [pdf]

  3. Liang Du and Yi-Dong Shen. Unsupervised Feature Selection with Adaptive Structure Learning. in Proceedings of the 21th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 209-218, Sydney, Australia, August 10–13, 2015. (Oral paper, Acceptance rate 159/819 = 19.41%). [pdf][codes][slide][poster]

  4. Nannan Gu, Mingyu Fan, Liang Du, Dongchun Ren. Efficient Sequential Feature Selection Based on Adaptive Eigenspace Model. Neurocomputing, Volume 161, Pages 199–209, August 2015. [pdf]

  5. Liang Du, Peng Zhou, Lei Shi, Hanmo Wang, Mingyu Fan, Wenjian Wang, Yi-Dong Shen. Robust Multiple Kernel K-means Clustering using L21-norm. in Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), pages 3476-3482, Buenos Aires, Argentinean, July 25-31, 2015. (Oral paper, acceptance rate 575/1996 = 28.8%). [pdf][codes]

  6. Peng Zhou, Liang Du*, Lei Shi, Hanmo Wang, Yi-Dong Shen. Recovery of Corrupted Multiple Kernels for Clustering. in Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), pages 4105-4111, Buenos Aires, Argentinean, July 25-31, 2015. (Oral paper, acceptance rate 575/1996 = 28.8%). [pdf]

  7. Peng Zhou, Liang Du*, Hanmo Wang, Lei Shi, Yi-Dong Shen. Learning a Robust Consensus Matrix for Clustering Ensemble via Kullback-Leibler Divergence Minimization. in Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), pages 4112-4118, Buenos Aires, Argentinean, July 25-31, 2015. (Oral paper, acceptance rate 575/1996 = 28.8%). [pdf]

  8. Peng Zhou, Liang Du, Mingyu Fan, and Yi-Dong Shen. An LLE based Heterogeneous Metric Learning for Cross-media Retrieval. In Proceedings of the Eleventh SIAM International Conference on Data Mining (SDM), pages 64-72, Vancouver, British Columbia, Canda, April 30-May 2, 2015. (Oral paper, acceptance rate 72/491 = 14.66%). [pdf]

  9. Hanmo Wang, Liang Du and Yi-Dong Shen. Convex Batch Mode Active Sampling via alpha-relative Pearson Divergence. The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), pages 3045-3051, Austin Texas, USA, January 25-29, 2015. (Acceptance rate 531/1991 = 26.67%). [pdf]
  10. 2014

  11. Liang Wu, Liang Du, Bo Liu, Guandong Xu, Yong Ge, Yanjie Fu, Yuanchun Zhou, Jianhui Li and Hui Xiong. Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval. The 14th IEEE International Conference on Data Mining (ICDM), pages 610-619, Shenzhen, China, December 14-17, 2014. (Regular paper, acceptance rate 71/727 = 9.7%). [pdf]

  12. Lei Shi, Liang Du and Yi-Dong Shen. Robust Spectral Learning for Unsupervised Feature Selection. The 14th IEEE International Conference on Data Mining (ICDM), pages 977-982, Shenzhen, China, December 14-17, 2014. [pdf][codes]
  13. 2013

  14. Liang Du, Zhiyong Shen, Xuan Li, Peng Zhou and Yi-Dong Shen. Local and Global Discriminative Learning for Unsupervised Feature Selection. The 13th IEEE International Conference on Data Mining (ICDM), pages 131-140, Dallas, TX, USA, December 7-10, 2013. (Regular paper, acceptance rate 94/809 = 11.6%). [pdf]

  15. Jun Deng, Liang Du and Yi-Dong Shen. Heterogeneous Metric Learning for Cross-Modal Multimedia Retrieval, The 14th International Conference on Web Information System Engineering (WISE), pages 43-56, Nanjing, China, October 13-15, 2013. [pdf]

  16. Liang Du and Yi-Dong Shen. Towards robust co-clustering. The 23rd International Joint Conference on Artificial Intelligence (IJCAI, pages 1317-1322, Beijing, China, August 3-9, 2013. (Oral paper, acceptance rate 195/1473=13.2%). [pdf]

  17. Liang Du and Yi-Dong Shen. Joint clustering and feature selection. The 14th International Conference on Web-Age Information Management (WAIM), pages 253-264, Beidaihe, China, June 14-16, 2013. [pdf]

  18. Liang Du and Yi-Dong Shen, Zhiyong Shen, Jianying Wang and Zhiwu Xu. A self-supervised framework for clustering ensemble. The 14th International Conference on Web-Age Information Management (WAIM), pages 253-264, Beidaihe, China, June 14-16, 2013. [pdf]

  19. Xuan Li, Liang Du and Yi-Dong Shen. Update summarization via graph-based sentence ranking. IEEE Transactions on Knowledge and Data Engineering (TKDE), May 2013, vol.25, no.5, pages 1162-1174. [pdf]

  20. Liang Wu, Alvin Chin, Guandong Xu, Liang Du, Xia Wang, Kangjian Meng, Yonggang Guo and Yuanchun Zhou. Who Will Follow Your Shop? Exploiting Multiple Information Sources in Finding Followers. Database Systems for Advanced Applications (DASFAA), pages 401-415, Wuhan, China, April 22-25, 2013. [pdf]
  21. 2012

  22. Liang Du Xuan Li and Yi-Dong Shen. Robust nonnegative matrix factorization via half-quadratic minimization. In Proceedings of IEEE 12th International Conference on Data Mining (ICDM), pages 201-210, Brussels, Belgium, December 10-13, 2012. (Regular paper, acceptance rate 81/756=10.7%). [pdf]
  23. 2011

  24. Liang Du, Xuan Li, and Yi-Dong Shen. User Graph Regularized Pairwise Matrix Factorization for Item Recommendation, in Proceedings of the 7th International Conference on Advanced Data Mining and Applications (ADMA), pages 372-385, Beijing, China, December 18-20, 2011. [pdf]

  25. Liang Du, Xuan Li, and Yi-Dong Shen. Cluster Ensembles via Weighted Graph Regularized Nonnegative Matrix Factorization, in Proceedings of the 7th International Conference on Advanced Data Mining and Applications (ADMA), pages 215-228, Beijing, China, December 18-20, 2011. [pdf]

  26. Xuan Li, Liang Du, and Yi-Dong Shen. Graph-Based Marginal Ranking for Update Summarization, in Proceedings of the 11th SIAM International Conference on Data Mining (SDM), pages 486-497, Arizona, USA, April 28-30, 2011. [pdf]
  27. 2010

  28. Zhiyong Shen, Liang Du, Xukun Shen, and Yi-Dong Shen. Interval-valued Matrix Factorization with Applications, in Proceedings of the 10th IEEE International Conference on Data Mining (ICDM), pages 1037-1042, Sydney, Australia, December 14-17, 2010. [pdf]

  29. Xuan Li, Yi-Dong Shen, Liang Du, and Chen-Yan Xiong. Exploiting novelty, coverage and balance for topic-focused multi-document summarization, in Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM), pages 1765-1768, Toronto, Canada, October 26-30, 2010. [pdf]

PhD Thesis


Research Fundings


Selected Awards & Honors


Academic Services

Journal Reviewer

  • IEEE Transactions on Knowledge and Data Engineering (TKDE).
  • Data Mining and Knowledge Discovery (DMKD).
  • Information Sciences.
  • Neurocomputing.
  • International Journal of Pattern Recognition and Artificial Intelligence.
  • Conference PC Member

  • The 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016.
  • The workshop, The DAta mining meets Visual Analytics at big data era (DAVA 2015), on The IEEE International Conference on Data Mining (ICDM) 2015.
  • The IEEE International Conference on Advanced and Trusted Computing (ATC) 2015.
  • External Reviewer

  • The ACM International Conference on Knowledge Discovery and Data Mining (KDD) 2010, 2013, 2015.
  • The International Joint Conference on Artificial Intelligence (IJCAI) 2011, 2015.
  • The AAAI Conference on Artificial Intelligence (AAAI) 2012, 2013, 2015, 2016.
  • The ACM Conference on Information and Knowledge Management (CIKM) 2011.
  • The International Semantic Web Conference (ISWC) 2010, 2011.
  • The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2011, 2012, 2013.
  • The Pacific Rim International Conferences on Artificial Intelligence (PRICAI) 2010, 2012, 2014.
  • The International Conference on Software Engineering and Knowledge Engineering (SEKE) 2010, 2011.

  • Short Bios

    Liang Du is currently a Lecturer in Shanxi University. Prior to that, he was an assitant researcher in the State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences. During July 2013 and July 2014, he was a Software Engineer at Alibaba Group working on the optimization of CPS internet ads. He received the B.E. degree in Software Engineering from Wuhan University in 2007, and Ph.D degree in Computer Science from Institute of Software at University of Chinese Academy of Sciences in 2013. He has broader interests in Data Mining, Machine Learning, and Big Data Analysis. He is particularly interested in the following topics: clustering with noise and heterogeneous data, ranking for feature selection, active learning and document summarization. He has published more than 20 papers in top conferences and journals, including KDD(1), IJCAI(4), AAAI(1), ICDM(6), TKDE(1), SDM(2), CIKM(1). He is the recipient of Presidential Scholarship of the Chinese Academy of Sciences in 2013. He is now leading/participating a few national projects such as NSFC and the 973 program.

    杜亮,博士,山西大学计算机与信息技术学院讲师。主要研究领域:数据挖掘、机器学习。2007年武汉大学获软件工程学士学位,2013年中国科学院软件研究所计算机科学国家重点实验室获博士学位。2013年7月至2014年7月在阿里巴巴集团担任软件工程师从事计算广告、大数据分析等方面的开发和研究。2014年7月至2015年7月担任中科院软件研究所助理研究员。近年来发表学术论文20多篇,其中多篇论文发表于国际顶级会议和期刊,如:ACM KDD(1)、 IJCAI(4)、 AAAI(1)、 IEEE ICDM(6)、 IEEE TKDE(1)、SIAM SDM(2)、ACM CIKM (1)等。同时为IEEE TKDE、ACM TIST、DMKD、 Neurocomputing、 IJPRAI等国际期刊以及多次为KDD、IJCAI、AAAI、ISWC、PAKDD、PRICAI等国际会议担任审稿人。曾获中国科学院院长奖、北京市优秀毕业生等奖励。目前,作为核心人员参加973和面上课题各一项,主持NSFC青年基金和中科院软件所开放课题各一项。