姓名： 徐增林
任职： 国家青年特聘专家、计算机学院教授/博导
通讯地址： 深圳市南山区深圳大学城哈工大校区信息楼1722
邮编： 518055
电话：
电子邮件： xuzenglin@hit.edu.cn
研究方向：
致力于解决涉及现代大数据分析的关键建模和计算的挑战，实现复杂系统在自然语言处理，计算机视觉，社会计算，网络空间安全，生物信息学和生物医学应用等领域的研究。为此研究由各种应用驱动的稀疏、关系、动态、深度学习模型，并为这些模型开发精确、高效和可扩展的算法。
研究领域：
● 机器学习理论与方法
■ 多源异构数据学习：多核学习 / 多视图学习 / 多任务学习 / 张量数据分析 / 迁移学习
■ 贝叶斯深度学习
■ 分布式学习与联邦学习
■ 量子机器学习
● 机器学习应用
■ 医疗大数据分析
■ 网络数据分析
■ 序列数据分析
■ 知识图谱与自动问答
欢迎对上述课题感兴趣，愿意从事机器学习研究的同学积极报考，每年招收博士生2名，硕士生6名，博士后2名（博士后待遇优厚）。有兴趣者可邮件联系。
教育经历：
香港中文大学，中国 | 博士 2005 - 2009
l 项目：计算机科学与工程
● 导师：Irwin King 和 Michael R. Lyu
l 论文：未标记数据学习
密歇根州立大学，美国 | 访问学者 2007 - 2008
l 项目：计算机科学与工程.
● 导师：金荣（Rong Jin）
工作经历：
2019． | 教授，博导
哈尔滨工业大学（深圳）| 深圳西丽深圳大学城哈工大校区
2014 – 2019 |教授, 博导
电子科技大学 | 四川省成都市高新西区西源大道2006号
2010 – 2014 | 副研究员
普渡大学 | 西拉法叶，美国
2009 – 2010 | 副研究员
萨尔大学与马普信息所 | 萨尔布吕肯，德国
经费支持：
2018 – 2019|自然语言理解技术研究
● Nuance 联合研究基金(主持)
2017 - 2019 |基于知识图谱的社会计算系统研究
● 中央高校重点培育项目(主持)
2016 - 2019 |大规模贝叶斯张量分析技术研究
● 国家自然科学基金项目(主持)
2014 – 2018 |大数据分析和可扩展的机器学习的研究
● 启动经费(主持)
2011 – 2015 | 用于多源和多方面的数据可扩展的贝叶斯学习
● 美国国家科学基金会(主研)
2009 – 2013 |关系学习和推理网络模型
● 美国国家科学基金会(主研)
2007 – 2009|关于半监督学习下的低密度和流形假设的探索
● 香港研究资助局(主研)
2005 – 2017 |高斯假设下的基于核的最大后验（MAP）分类器的理论研究
● 香港研究资助局(主研)
专业活动：
研讨会组织
● 2018，第一届电子科大人工智能前沿论坛
● 2014, IEEE可扩展机器学习研讨会，美国华盛顿特区
● 2013，IEEE可扩展机器学习研讨会，美国加州圣克拉拉
● 2010，NIPS 2 社会计算的机器学习研讨会，加拿大不列颠哥伦比亚省温哥华
期刊审稿
● 机器学习期刊，IEEE神经网络汇刊，IEEE知识与数据工程汇刊，ACM智能系统和技术汇刊，ACM数据挖掘汇刊，神经计算，神经计算和应用，模式识别。
程序委员会成员及会议审稿
● NeuriPS， ICML， AAAI， IJCAI，CVPR，ACL，ICCV，ECCV， ICDM， ACM CIKM，IJCNN
● AAAI 2019 /IJCAI 2020 Senior PC
期刊编辑
● Elsevier《Neural Networks》（JCR-1区）Action Editor
● 《Neurocomputing》（JCR-2区）Associate Editor
邀请报告和教学报告：
特邀报告
● 张量网络与神经网络，2017年5月，CAAI第八届智能技术与应用技术大会类脑智能论坛，成都
● 张量网络与神经网络，2017年5月，ACM图灵大会，上海
● 高效的多核学习，2009年11月，马克斯 - 普朗克智能技术研究所，德国图宾根
● 可扩展多核学习研究，2008年10月，（NEC）美国实验室，库比蒂诺，加州，美国
教学报告
● 张量网络和神经网络，ICONIP2017, 中国广州
● 概率矩阵和张量块模型用于网络建模，AAAI教学讲座2012（与Alan Qi），加拿大多伦多
● 半监督学习的基础知识和进展，ICONIP 教学讲座2011（与Irwin King），中国上海
● 半监督学习的基础知识和进展，IJCNN教学讲座2010 （与Irwin King）, 西班牙巴塞罗那
奖励：
● 2016：亚太神经网络协会（APNNS）青年学者奖
● 2016: ACML最佳学生论文奖亚军
● 2015：AAAI最佳学生论文奖提名
● 2009：NIPS旅行奖，IJCAI旅行奖
● 2007：IJCNN旅行奖
论著：
专著与编著
1. Zenglin Xu and Irwin King. Introduction to Semi-supervised Learning. CRC Press, 2019 (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.
书籍章节
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 classification. In A. Mittal and A. Kassim, editors, Bayesian Network Technologies: Appli- cations and Graphical Models, pages 1–12. IDEA Group Inc., New York, 2007.
期刊文章
1. Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven CH Hoi, Zenglin Xu. Semi-supervised deep embedded clustering, Neurocomputing, 325, 121-130, 2019.
2. Zhao Kang, Liangjian Wen, Wenyu Chen and Zenglin Xu. Low-rank kernel learning for graph-based clustering, Knowledge-Based Systems, 2019.
3. Shudong Huang, Peng Zhao, Yazhou Ren, Tianrui Li, Zenglin Xu. Self-paced and soft-weighted nonnegative matrix factorization for data representation, Knowledge-Based Systems. 2018.
4. Shudong Huang, Zhao Kang, Ivor W Tsang, Zenglin Xu. Auto-weighted Multi-view Clustering via Kernelized Graph Learning, Pattern Recognition, 2018.
5. Liyang Hao, Siqi Liang, Jinmian Ye and Zenglin Xu. TensorD: A tensor decomposition library in TensorFlow, Neurocomputing, 318, 196-200, 2018.
6. Zenglin Xu, Bin Liu, Shandian Zhe, Haoli Bai, Zihan Wang and Jennifer Neville. Variational Random Function Model for Network Modeling, IEEE transactions on neural networks and learning systems, 99, 1-7, 2018.
7. Shudong Huang, Yazhou Ren, and Zenglin Xu. Robust Multi-View Data Clustering with Multi-view Capped-Norm K-means, Neurocomputing , 2018.
8. Shudong Huang, Zhao Kang, and Zenglin Xu. Self-weighted Multi-View Clustering with Soft Capped Norm, Knowledge-Based Systems (2018).
9. Bin Liu, Yingming Li, Zenglin Xu, Manifold regularized matrix completion for multi-label learning with ADMM. Neural Networks, https://doi.org/10.1016/j.neunet.2018.01.011，2018
10. Shudong Huang, Zenglin Xu, jiancheng Lv, Adaptive Local Structure Learning for Document Co-clustering, Knowledge-Based Systems, https://doi.org/10.1016/j.knosys.2018.02.020, 2018
11. Shudong Huang, Hongjun Wang, Tao Li, Tianrui Li, and Zenglin Xu.
Robust Graph Regularized Nonnegative Matrix Factorization for Clustering, Data Mining and Knowledge Discovery, 2018
12. Zenglin Xu, Shandian Zhe,Yuan(Alan) Qi and Peng Yu. Association Discovery and Diagnosis of Alzheimer’s Disease with Bayesian Multiview Learning. Journal of Artificial Intelligence Research, 56 (2016).
13. Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Budget constrained non-monotonic feature selection. Neural Networks, 71, 214- 224, 2015
14. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Bayesian nonparametric mod- els for multiway data analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.37, no.2, pp.475–487, 2015.
15. Haiqin Yang, Zenglin Xu, Jieping Ye, Irwin King, and Michael R. Lyu. Efficient sparse generalized multiple kernel learning. IEEE Transactions on Neural Networks, 22(3):433–446, 2011.
16. Zenglin Xu, Irwin King, Michael R. Lyu, and Rong Jin. Discriminative semi-supervised feature selection via manifold regularization. IEEE Trans- actions on Neural Networks, 21(7):1033–1047, 2010.
17. 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.
国际会议文章
1. Yu Pan, Jing Xu, Maolin Wang, Jinmian Ye, Fei Wang, Kun Bai, Zenglin Xu. Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition. AAAI, 2019.
2. Haoli Bai, Zhuangbin Chen, Michael R Lyu, Irwin King, Zenglin Xu. Neural Relational Topic Models for Scientific Article Analysis. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018.
3. Hao Liu, Haoli Bai, Lirong He, Zenglin Xu. Structured Inference for Recurrent Hidden Semi-markov Model. IJCAI, 2018
4. Zhao Kang, Xiao Lu, J Yi, Zenglin Xu. Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification. IJCAI, 2018 .
5. Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu: Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition, CVPR, 2018
6. Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska：SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks. PPoPP, 2018
7. Zhao Kang, Chong Peng, Qiang Cheng, Zenglin Xu: Unified Spectral Clustering with Optimal Graph. AAAI, 2018
8. Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang: Learning with Incomplete Labels. AAAI, 2018
9. Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang: Learning with Feature Network and Label Network Simultaneously. AAAI 2017: 1410-1416
10. Yazhou Ren, Peng Zhao, Yongpan Sheng, Dezhong Yao, Zenglin Xu: Robust Softmax Regression for Multi-class Classification with Self-Paced Learning. IJCAI 2017: 2641-2647
11. Guangxi Li, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, Michael R. Lyu: Simple and efficient parallelization for probabilistic temporal tensor factorization. IJCNN 2017: 1-8
12. Shudong Huang, Zenglin Xu, Fei Wang: Nonnegative matrix factorization with adaptive neighbors. IJCNN 2017: 486-493
13. Liqiang Wang, Yafang Wang, Bin Liu, Lirong He, Shijun Liu, Gerard de Melo, Zenglin Xu: Link prediction by exploiting network formation games in exchangeable graphs. IJCNN 2017: 619-626
14. Yazhou Ren, Peng Zhao, Zenglin Xu, Dezhong Yao: Balanced self-paced learning with feature corruption.
IJCNN 2017: 2064-2071
15. Bin Liu, Zenglin Xu, Bo Dai, Haoli Bai, Xianghong Fang, Yazhou Ren, Shandian Zhe: Learning from semantically dependent multi-tasks. IJCNN 2017: 3498-3505
16. Yiyang Zhao, Linnan Wang, Wei Wu, George Bosilca, Richard W. Vuduc, Jinmian Ye, Wenqi Tang,Zenglin Xu: Efficient Communications in Train- ing Large Scale Neural Networks. ACM Multimedia (Thematic Workshops) 2017: 110-116
17. Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang: Learning with Feature Network and Label Network Simultaneously. In AAAI’17: Proceedings of the 26th AAAI Conference on Artificial Intelligence. 1410- 1416
18. Yingming Li, Ming Yang, Zenglin Xu, and Zhongfei (Mark) Zhang Multi- view Learning with Limited and Noisy Tagging. In IJCAI’16: Proceedings of the 25th International Joint Conference on Artificial Intelligence.
19. Shandian Zhe, Yuan Qi, Youngja Park, Zenglin Xu, Ian Molloy, and Suresh Chari DinTucker: Scaling up Gaussian Process Models on Large Multidi- mensional Arrays . In AAAI’16: Proceedings of the 26th AAAI Conference on Artificial Intelligence.
20. Yingming Li, Ming Yang, Zenglin Xu, and Zhongfei (Mark) Zhang Learning with Marginalized Corrupted Features and Labels Together. In AAAI’16: Proceedings of the 26th AAAI Conference on Artificial Intelligence.
21. 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 Artificial Intelligence and Statistics. 2015. (AR: 127/442= 28.7%)
22. Shandian Zhe, Zenglin Xu, and Yuan Qi. Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimer’s Disease. In AAAI’15: Proceedings of the 25th AAAI Conference on Artifi- cial Intelligence. Outstanding student paper honorable mention, 2015. (AR: 531/1991= 26.7%)
23. Zenglin Xu, Rong Jin, Bin Shen and Shenghuo Zhu. Nystrom Approx- imation for Sparse Kernel Methods: Theoretical Analysis and Empirical Evaluation In AAAI’15: Proceedings of the 25th AAAI Conference on Ar- tificial Intelligence. 2015. (AR: 531/1991= 26.7%)
24. 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.
25. 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.
26. 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.
27. 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%)
28. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Infinite tucker decomposi- tion: 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%)
29. Feng Yan, Zenglin Xu, and Yuan (Alan) Qi. Sparse matrix-variate gaus- sian 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%)
30. 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 Artificial Intelligence. AAAI Press, 2011. (AR: 242/975=24.8%)
31. 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 Intelli- gence. AAAI Press, 2010. (AR: 264/982=26.9%)
32. Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, and Michael R. Lyu. Simple and efficient 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%)
33. Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Online learn- ing 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%)
34. 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%)
35. 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 Artificial Intelligence, pages 1303–1308, 2009.
36. 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%)
37. Zhirong Yang, Irwin King, Zenglin Xu, and Errki Oja. Heavy-tailed sym- metric stochastic neighbor embedding. In Y. Bengio, L. Bottou, J. Lafferty, and C. Williams, editors, Advances in Neural Information Processing Sys- tems 22 (NIPS), pages 2169–2177. 2009. (AR: 263/1105= 23.8%, Spotlight: 87/1105 = 7.8%)
38. Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu, and Irwin King. Non- monotonic feature selection. In ICML ’09: Proceedings of the 26th An- nual International Conference on Machine Learning, pages 1145–1152, New York, NY, USA, 2009. ACM. (160/640 = 25%)
39. Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, and Colin Camp- bell. Supervised self-taught learning: Actively transferring knowledge from unlabeled data. In IJCNN ’09: International Joint Conference on Neural Networks, pages 1272–1277. IEEE, 2009.
40. 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 Process- ing Systems 21 (NIPS), pages 1825–1832. 2008. (AR: 250/1022 = 24%)
41. Zenglin Xu, Rong Jin, Kaizhu Huang, Irwin King, and Michael R. Lyu. Semi-supervised text categorization by active search. In CIKM ’08: Pro- ceedings 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%)
42. Kaizhu Huang, Zenglin Xu, Irwin King, and Michael R. Lyu. Semi- supervised learning from general unlabeled data. In ICDM ’08: Proceed- ings of IEEE International Conference on Data Mining, pages 273–282, Los Alamitos, CA, USA, 2008. IEEE Computer Society. (AR: 70/724 = 9%)
43. 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.
44. Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, and Michael R. Lyu. Ef- ficient convex relaxation for transductive support vector machine. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural In- formation Processing Systems 20, pages 1641–1648. MIT Press, Cambridge, MA, 2007. (217/975 = 22%)