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CS: Xu Zenglin

published: 2016-12-20 15:24:37       hits: 

name : Xu Zenglin Sex: Male phone: 028-61830885
email: zlxu@uestc.edu.cn office-address: Main Building B1-201, Qingshuihe Campus, UESTC
PH.D  Supervisor: YES Master Supervisor: YES
major: Computer Science and Technology
research interst: Theory of Computation;  artificial intelligence and Pattern recognition; Computer graphics and image processing; Cloud computing and big data processing; Digital Media Technology.
Biography: Professor Xu is a member of China 1000-Talents (Youth) Program, and a full professor and PhD Advisor, University of Electronic Science & Technology of China (UESTC). He obtained his PhD in Computer Science and Engineering from the Chinese University of Hong Kong. His research interest includes machine learning and its applications on social network analysis, health informatics, and cyber security analytics.  Furthermore, he had published more than 50 papers in the top conferences and journals of machine learning, including NIPS, ICML, IJCAI, AAAI, IEEE TPAMI and IEEE TNN. Together with Professor Alan Qi, he taught a tutorial in AAAI 2012, Toronto, Canada. He is the recipient of the best student paper honorable mention of AAAI 2015, and the best student paper award runner up of ACML 2016. He is also the recipient of Young Researcher Award of APNNS. Dr. Xu has been a PC member or reviewer to a number of top conferences such as NIPS, ICML, AAAI, IJCAI, etc. He regularly servers as a reviewer to IEEE TPAMI, JMLR, Pattern Recognition, IEEE TNNLS, IEEE TKDD, ACM TKDD, etc. He is also a reviewer to National Science Foundation China, Hong Kong Research Grant Council, and the  Ministry of Science and Technology of China.
Education experience: Education:
1998.09-2002.06 B.S.  Xi'an Polytechnic University  Computer Science and Technology
2002.09-2005.06 M.S   Xi'an Jiaotong University      Computer Software and Theory
2005.09-2009.06 Ph.D. The Chinese University of Hong Kong Computer Science and Engineering
 
Experience:
2009.07-2010.07  Researcher Saarland University and Max Planck Institute for Informatics;
2010.08-2014.05  Research Associate  Purdue University;
2014.05-now      National 1000-youth Talent Professor and PhD Advisor University of Electronic Science & Technology of China (UESTC).
Selected Publications: [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 Unified View on Learning with Labeled and Unlabeled Data. LAP LAMBERT Academic Publishing, 2010.
[4]. Zenglin Xu, Mingzhen Mo, and Irwin King. Computational intelligence. In Alexandru Floares, editor, Semi-supervised Learning, pages 1-16. Nova Science Publishers, 2012.
[5]. Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, and Zhangbin Zhou. A novel discriminative naive bayesian network for classification. In A. Mittal and A. Kassim, editors, Bayesian Network Technologies: Applications and Graphical Models, pages 1-12. IDEA Group Inc., New York, 2007.
[6]. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Bayesian nonparametric models for multiway data analysis. IEEE Transactions on Pattern Recognition and Machine Intelligence(T-PAMI), 2015.
[7]. Haiqin Yang, Zenglin Xu, Jieping Ye, Irwin King, and Michael R. Lyu. E
fficient sparse generalized multiple kernel learning. IEEE Transactions on Neural Networks, 22(3):433-446, 2011.
[8]. 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.
[9]. Zenglin Xu, Kaizhu Huang, Jianke Zhu, Irwin King, and Michael R. Lyu. A novel kernel-based maximum a posteriori classification method. Neural Networks,22(7):977-987, 2009.
[10]. Zenglin Xu, Irwin King, and Michael R. Lyu. Feature selection based on minimum error minimax probability machine. International Journal of Pattern Recognition and Artificial Intelligence, 21(8):1-14, 2007.
[11].Bin Shen, Zenglin Xu and Jan P. Allebach. Kernel Tapering: a Simple and Effective Approach to Sparse Kernels for Image Processing. International Conference on Image Processing, 2014.
[12]Shandian Zhe, Zenglin Xu and Yuan (Alan) Qi. Joint association discovery and diagnosis of Alzheimer's disease by supervised heterogeneous multiview learning. Pacific Symposium on Biocomputing, 2014.
[13]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%).
[14]Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. In nite tucker decomposition: Non-parametric 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%).
[15]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 Artificial Intelligence, pages 745-752. AUAI Press, 2011. (AR: 96/285=33.6%).
[16]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%).
[17]Zenglin Xu, Rong Jin, Shenghuo Zhu, Michael R. Lyu, and Irwin King. Smooth optimization for effective multiple kernel learning. In AAAI '10: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. AAAI Press,2010. (AR: 264/982=26.9%).
[18]Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, and Michael R. Lyu. Simpleand e
fficient 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%).
[19]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%).
[20]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 Artificial Intelligence, pages 244-251. AUAI Press,2010. (AR: 88/260=33.8%).
[21]Zenglin Xu, Rong Jin, Michael R. Lyu, and Irwin King. Discriminative semisupervised feature selection via manifold regularization. In IJCAI '09: Proceedings of the 21th International Joint Conference on Arti cial Intelligence, pages 1303-1308, 2009.
[22]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. Lafferty, 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%).
[23]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%).
[24]Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu, and Irwin King. Non-monotonic 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%).
[25]Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, and Colin Campbell. Supervised self-taught learning: Actively transferring knowledge from unlabeleddata. In IJCNN '09: International Joint Conference on Neural Networks, pages 1272-1277. IEEE, 2009.
[26]Zenglin Xu, Rong Jin, Irwin King, and Michael Lyu. An extended level method for efficient 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%).
[27]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%).
[28]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%).
[29]Jianke Zhu, Steven C. Hoi, Zenglin Xu, and Michael R. Lyu. An effective 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.
[30]Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, and Michael R. Lyu. Efficient convex relaxation for transductive support vector machine. In J.C. Platt,D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 1641-1648. MIT Press, Cambridge, MA, 2007.(217/975 = 22%).
[31]Zenglin Xu, Jianke Zhu, Irwin King, and Michael Lyu. Kernel maximum aposteriori classification with error bound analysis. In ICONIP '07: Proceedings of the International Conference on Neural Information Processing, pages 841-850,2007.
[32]Zenglin Xu, Jianke Zhu, Michael R. Lyu, and Irwin King. Maximum margin based semi-supervised spectral kernel learning. In IJCNN '07: Proceedings of 20th International Joint Conference on Neural Network, pages 418-423, 2007.
[33]Zenglin Xu, Irwin King, and Michael R. Lyu. Web page classification 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. 
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