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Faculty

Zenglin Xu

Founding director of Statistical Machine Intelligence and LEarning (SMILE) Lab Director of Institute of Big Data Mining and Inference, Big Data Research Center University of Electronic Science and Technology of China

徐增林
徐增林老师
Contact
Phone: (+86)186-0800-6385
Email: zenglin@gmail.com, zlxu@uestc.edu.cn
Page: http://bigdatalab.weebly.com
Aliation: School of Computer Science & Engineering, Big Data Research Center University of Electronic Science & Technology of China
Address: 2006 Xiyuan Avenue, Gaoxin-West District, Chengdu, Sichuan, 611731 China

Research Interests
The goal of my research is to address critical statistical and computational challenges involved in modern big data analysis, and to help the study of complex systems in the areas of information retrieval, computer vision, social computing, cyber security, bioinformatics, and biomedical applications. To this end, I study sparse, relational, and dynamic models driven by various applications, and develop ecient, e ective, and scalable algorithms for those models.
 My research areas includes:
   Multiple kernel learning/Multiview learning/Multitask learning/Multiway data analysis
   Nonparametric Bayesian approaches
   Network modeling/Dynamic models
   Online/Parallel learning
 Current projects:
   Distributed algorithms for network and multiway data modelling
   Computational models for Alzheimer's Disease analysis

Education
2005-2009, Ph.D.
Program: Computer Science and Engineering
The Chinese University of Hong Kong, Hong Kong
Supervisor: Irwin King and Michael R. Lyu
Dissertation: Learning with unlabeled data

2007/2008 Summers, Academic Visiting
Program: Computer Science and Engineering
Michigan State University, U.S.
Supervisor: Rong Jin

2002 -2005, M.S.
Program: Computer Software and Theory
Xi'an Jiaotong University, China
Adviser: Junyi Shen
Graduate School Entrance Score (2002): top 0.4% (of 500)

1998 -2002, B.S.
Program: Computer Science and Technology
Xi'an Polytechnic University, China
University Entrance Score (1998): top 0.2% (over 160,000) in Shandong Province,China

Experience 
  • 2014-now National 1000-youth Talent Professor and PhD Advisor
             University of Electronic Science & Technology of China (UESTC), Chengdu, Sichuan, China
  •  2010-2014 Research Associate
              Purdue University, West Lafayette, IN, USA
             Collaborators: Dr. Yuan (Alan) Qi, Dr. Ninghui Li
  •  2009-2010 Researcher
              Cluster of Excellence: MMCI, Saarland University and Max Planck Institute for Informatics, Saarbruecken, Germany Collaborator: Dr. Matthias Seeger

Awards 
2013: National Recruitment Program of Global Young Experts(1000-youth plan)
 2009: NIPS travel award, IJCAI travel award
 2007: IJCNN travel award
 2005: Outstanding Student of Xi'an Jiaotong University, China
 2002: Outstanding Graduate of Shaanxi Province, China
 2001: Honorable Mention, Mathematical Contest of Modeling (MCM), U.S.

 
Publications
a) Books or Edited Special Issues
1. Zenglin Xu and Irwin King. Introduction to Semi-supervised Learning. CRC Press, 2015 (expected).
2. Yi Fang, Zenglin Xu, Jiang Bian, and Ziad Al Bawab. International Journal of Web Science, Special Issue on Social Web Search and Mining. Inderscience, 2013.
3. Zenglin Xu, Irwin King, and Michael R. Lyu. More Than Semi-supervised Learning: A Uni ed View on Learning with Labeled and Unlabeled Data. LAP LAMBERT Academic Publishing, 2010.
b) Invited Book Chapters
1. Zenglin Xu, Mingzhen Mo, and Irwin King. Computational intelligence. In Alexandru Floares, editor, Semi-supervised Learning, pages 1-16. Nova Science Publishers, 2012.
2. Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, and Zhangbin Zhou.A novel discriminative naive bayesian network for classi cation. In A. Mittal and A. Kassim, editors, Bayesian Network Technologies: Applications and Graphical Models, pages 1-12. IDEA Group Inc., New York, 2007.
c) Refereed Journal Articles
1. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Bayesian nonparametric models for multiway data analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.37, no.2, pp.475{487, 2015.
2. Haiqin Yang, Zenglin Xu, Jieping Ye, Irwin King, and Michael R. Lyu. E-cient sparse generalized multiple kernel learning. IEEE Transactions on Neural Networks, 22(3):433{446, 2011.
3. Zenglin Xu, Irwin King, Michael R. Lyu, and Rong Jin. Discriminative semi-supervised feature selection via manifold regularization. IEEE Transactions on Neural Networks, 21(7):1033{1047, 2010.
4. Zenglin Xu, Kaizhu Huang, Jianke Zhu, Irwin King, and Michael R. Lyu. A novel kernel-based maximum a posteriori classi cation method. Neural Networks,22(7):977{987, 2009.
5. Zenglin Xu, Irwin King, and Michael R. Lyu. Feature selection based on mini-mum error minimax probability machine. International Journal of Pattern Recognition and Arti cial Intelligence, 21(8):1{14, 2007.
d) Refereed International Conference Articles
1. Shandian Zhe, Zenglin Xu, Xinqi Chu, Yuan Qi and Yongja Park Scalable Nonparametric Multiway Data Analysis. In AISTATS'15: Proceedings of the 18th Proceedings of International Conference on Arti cial Intelligence and Statistics.2015. (AR: 127/442= 28.7%)
2. Shandian Zhe, Zenglin Xu, and Yuan Qi. Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimers Disease. In AAAI'15: Proceedings of the 25th AAAI Conference on Arti cial Intelligence.Outstanding student paper honorable mention, 2015. (AR: 531/1991= 26.7%)
3. Zenglin Xu, Rong Jin, Bin Shen and Shenghuo Zhu. Nystrom Approximation for Sparse Kernel Methods: Theoretical Analysis and Empirical Evaluation In AAAI'15: Proceedings of the 25th AAAI Conference on Arti cial Intelligence.2015. (AR: 531/1991= 26.7%)
4. Christopher Gates, Ninghui Li, Zenglin Xu, Suresh N. Chari, Ian Molloy, and Youngja Park. Detecting Insider Information Theft Using Features from File Access Logs. European Symposium on Research in Computer Security (ESORICS),2014.
5. Bin Shen, Zenglin Xu and Jan P. Allebach. Kernel Tapering: a Simple and E ective Approach to Sparse Kernels for Image Processing. International Conference on Image Processing, 2014.
6. Shandian Zhe, Zenglin Xu and Yuan (Alan) Qi. Joint association discovery and diagnosis of Alzheimer's disease by supervised heterogeneous multiview learning.Paci c Symposium on Biocomputing, 2014.
7. Shouyuan Chen, Irwin King, Michael R. Lyu, and Zenglin Xu. Recovering pairwise interaction tensor. Neural Information Processing Systems (NIPS), 2013.(AR: 360/1420= 25.3%, Spotlight: 52/1420 = 3.7%)
8. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. In nite tucker decomposition: Nonparametric bayesian models for multiway data analysis. In ICML '12: Proceedings of the 29th International Conference on Machine Learning, pages 1023-1030, New York, NY, USA, 2012. Omnipress. (AR: 243/890 = 27.3%)
9. Feng Yan, Zenglin Xu, and Yuan (Alan) Qi. Sparse matrix-variate gaussian process blockmodels for network modeling. In UAI '11: Proceedings of the Twenty-Seventh Conference on Uncertainty in Arti cial Intelligence, pages 745-752. AUAI Press, 2011. (AR: 96/285=33.6%)
10. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Sparse matrix-variate t process blockmodels. In AAAI '11: Proceedings of the Twenty-Fifth AAAI Conference on Arti cial Intelligence. AAAI Press, 2011. (AR: 242/975=24.8%)
11. Zenglin Xu, Rong Jin, Shenghuo Zhu, Michael R. Lyu, and Irwin King. Smooth optimization for e ective multiple kernel learning. In AAAI '10: Proceedings of the Twenty-Fourth AAAI Conference on Arti cial Intelligence. AAAI Press,2010. (AR: 264/982=26.9%)
12. Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, and Michael R. Lyu. Simple and ecient multiple kernel learning by group lasso. In ICML '10: Proceedings of the 27th International Conference on Machine Learning, pages 1175-1182.Omnipress, 2010. (AR: 152/594=25.6%)
13. Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Online learning for group lasso. In ICML '10: Proceedings of the 27th International Conference on Machine Learning, pages 1191{1198. Omnipress, 2010. (AR: 152/594=25.6%)
14. Kaizhu Huang, Rong Jin, Zenglin Xu, and Cheng-Lin Liu. Robust metric learning by smooth optimization. In UAI '10: Proceedings of the Twenty-Sixth Conference on Uncertainty in Arti cial Intelligence, pages 244{251. AUAI Press,2010. (AR: 88/260=33.8%)
15. Zenglin Xu, Rong Jin, Michael R. Lyu, and Irwin King. Discriminative semi-supervised feature selection via manifold regularization. In IJCAI '09: Proceedings of the 21th International Joint Conference on Arti cial Intelligence, pages 1303-1308, 2009.
16. Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu, and Zhirong Yang.Adaptive regularization for transductive support vector machine. In Y. Bengio,L. Bottou, J. La erty, and C. Williams, editors, Advances in Neural Information Processing Systems 22 (NIPS), pages 2125{2133. 2009. (AR: 263/1105= 23.8%,Spotlight: 87/1105 = 7.8%)
17. Zhirong Yang, Irwin King, Zenglin Xu, and Errki Oja. Heavy-tailed symmetric stochastic neighbor embedding. In Y. Bengio, L. Bottou, J. La erty,and C. Williams, editors, Advances in Neural Information Processing Systems 22(NIPS), pages 2169{2177. 2009. (AR: 263/1105= 23.8%, Spotlight: 87/1105 =
7.8%)
18. Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu, and Irwin King. Nonmonotonic feature selection. In ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning, pages 1145{1152, New York, NY,USA, 2009. ACM. (160/640 = 25%)
19. Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, and Colin Campbell.Supervised self-taught learning: Actively transferring knowledge from unlabeled data. In IJCNN '09: International Joint Conference on Neural Networks, pages 1272-1277. IEEE, 2009.
20. Zenglin Xu, Rong Jin, Irwin King, and Michael Lyu. An extended level method for ecient multiple kernel learning. In D. Koller, D. Schuurmans, Y. Bengio,and L. Bottou, editors, Advances in Neural Information Processing Systems 21 (NIPS), pages 1825{1832. 2008. (AR: 250/1022 = 24%)
21. Zenglin Xu, Rong Jin, Kaizhu Huang, Irwin King, and Michael R. Lyu. Semi-supervised text categorization by active search. In CIKM '08: Proceedings of the thirteenth ACM international conference on Information and knowledge management, pages 1517{1518, New York, NY, USA, 2008. ACM Press. (AR: 256/772 = 33%)
22. Kaizhu Huang, Zenglin Xu, Irwin King, and Michael R. Lyu. Semi-supervised learning from general unlabeled data. In ICDM '08: Proceedings of IEEE International Conference on Data Mining, pages 273-282, Los Alamitos, CA, USA,2008. IEEE Computer Society. (AR: 70/724 = 9%)
23. Jianke Zhu, Steven C. Hoi, Zenglin Xu, and Michael R. Lyu. An e ective approach to 3d deformable surface tracking. In ECCV '08: Proceedings of the 10th European Conference on Computer Vision, pages 766{779, Berlin, Heidelberg,2008. Springer-Verlag.
24. Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, and Michael R. Lyu. Ecient convex relaxation for transductive support vector machine. In J.C. Platt,D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural InformationProcessing Systems 20, pages 1641{1648. MIT Press, Cambridge, MA, 2007.(217/975 = 22%)
25. Zenglin Xu, Irwin King, and Michael R. Lyu. Web page classi cation with heterogeneous data fusion. In WWW '07: Proceedings of the 16th International Conference on World Wide Web, pages 1171{1172, New York, NY, USA, 2007.ACM Press.
26. Yu Liu, Zheng Qin, Zenglin Xu, and Xingshi He. Feature selection with particle swarms. In Computational and Information Science, LNCS, volume 3314, pages 425-430, 2004.