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====Email: A@B, A=kaizhu.huang, B=xjtlu.edu.cn====

Education Background:

· Ph.D., Computer Science & Engineering, The Chinese University of Hong Kong (CUHK);
· M.S., Pattern Recognition & Intelligent Systems, Institute of Automation, Chinese Academy of Science (CAS);
· B.Eng., Automation, xi'an Jiaotong University (XJTU);

Experiences:

· Professor, Department of Electrical & Electronic Engineering, Xi'an Jiaotong-Liverpool University, (2017 -);
·Head, Department of Electrical & Electronic Engineering, Xi'an Jiaotong-Liverpool University, (2016.9 -2019.8);
· Founding Director, Suzhou Municipal Key Lab of Cognitive Computing and Applied Technology, (2016 - Now);
· Visiting/Honorary Professor, University of Stirling, UK (2016 - 2018)
· Concurrent Full Professor, University of Electronic Science and Technology of China, (2015 - 2017);
· Associate Professor, Department of Electrical & Electronic Engineering, Xi'an Jiaotong-Liverpool University, (2012 - 2016);
· Associate Professor, National Laboratory of Pattern Recognition,Chinese Academy of Sciences, (2009 - 2012);
· Researcher, Bristol University, (2008 - 2009);
· Research Fellow, The Chinese University of Hong Kong, (2007 - 2008);
· Research Scientist, Fujitsu R&D Center, (2004 - 2007).


Awards & Honours

​· 2019: Best Paper Award, 2019 International Conference on Brain Inspired Cognitive Systems (BICS 2019)​
· 2019: Best Student Paper Award, 2019 International Conference on Brain Inspired Cognitive Systems (BICS 2019)
​· 2018: Best Student Paper Award, 2018 International Conference on Brain Inspired Cognitive Systems (BICS 2018)
· 2017: Best Paper Runner-up Award, 2017 International Conference on Neural Information Processing (ICONIP 2017)
· 2014: Highly Demanded Talent Award of Universities in Suzhou, Suzhou
· 2011: APNNA Young Researcher Award, Asia Pacific Neural Network Society
​· 2008: Best Book Award in "三个一百"
· 2007: Postdoctoral Fellowship, Chinese University of Hong Kong
​· 2006: Q-finity Award, Fujitsu Laboratories
​· 2006: President Award, Fujitsu Laboratories
​· 2005: Excellent Research Award, Fujitsu R&D Centre
​· 2004: Special Research Award, Fujitsu R&D Centre
​· 2001: Postgraduate Scholarship, Chinese University of Hong Kong


Professional Service Activities

Board Member: Advisory Board
​· 2012~: Advisory Board in Springer Series in Bio-Neuroinformatics,
· 2015~: Section Editor (Senior Associate Editor) in Springer Journal: BMC Big Data Analytics,
· 2015~: Associate Editor in Cognitive Computation (ISI Impact Factor 3.441),
· 2016~: Associate Editor in Neurocomputing (ISI Impact Factor 3.339),
· 2016~: Advisory Board in Springer Book Series in Cognitive Computation Trend,

Recent Conference Chairs/PC (Part List)
· 2020: International Conference on Neural Information Processing (ICONIP 20) (Workshop co-Chair),
· 2020: World Congress on Computational Intelligence (WCCI 20) (Conflict co-Chair),
· 2020: AAAI Conference on Artificial Intelligence (AAAI 20) (Senior PC),
· 2020: International Conference on Learning Representation (ICLR 20) (PC),
· 2019: International Conference on Bio-Inspired Computing Systems (BICS 2019) (Program co-Chair),
· 2019: IJCAI19-International Workshop on Artificial Intelligence for Business Security (Program co-Chair),
· 2019: AAAI Conference on Artificial Intelligence (AAAI 19) (Senior PC),
· 2019: Neural Information Processing Systems (NeurIPS 19) (PC),
· 2019: International Conference on Learning Representation (ICLR19) (PC),
· 2019: International Conference on Joint Artificial Intelligence (IJCAI19) (PC),
· 2019: International Conference on Machine Learning (ICML 2019) (PC),
· 2019: International Joint Conference on Neural Networks (IJCNN 2019) (PC),
· 2018: International Workshop on Artificial Intelligence and Cybersecurity (AIC 2018) (Organizing co-Chair)
· 2018: AAAI Conference on Artificial Intelligence (AAAI 18) (Senior PC),
· 2018: International Joint Conference on Artificial Intelligence (IJCAI 18) (PC),
· 2018: Neural Information Processing Systems (NeurIPS 18) (PC),
· 2018: Artificial Intelligence x Cybernetic Summit (AICS) (Program co-chair),
· 2017: AAAI Conference on Artificial Intelligence (AAAI 17) (Senior PC),
· 2017: International Joint Conference on Artificial Intelligence (IJCAI 17) (PC),
· 2017: International Conference on Neural Information Processing (ICONIP 2017) (Tutorial and Workshop co-Chair),
· 2017: 1st Artificial Intelligence x Cybernetic Summit (AICS) (General co-chair),
· 2017: International Workshop on Artificial Intelligence and Cybersecurity (AIC 2017) (Lead Organizing Chair)
. 2017: International Conference on Smart Grid Technology and Data Processing (Steering Committee Chair)
· 2016: International Workshop on Data Mining and Cybersecurity (AIC 2016) (Organizing Chair)
· 2016: AAAI Conference on Artificial Intelligence (AAAI 16) (Senior PC),
​· 2015: Asian Conference on Machine Learning (ACML 2015) (Publication Chair),
​· 2015: International Workshop on Scalable Data Analytics (SDA 2015) (Organizing Chair),
​· 2014: International Conference on Neural Information Processing (ICONIP 2014) (Program co-Chair),
​· 2014: International Workshop on Artificial Intelligence and Cybersecurity (AIC 2014) (Leading Organizing Chair),
​· 2013: International Workshop on Artificial Intelligence and Cybersecurity (AIC 2013) (Organizing co-Chair),
​· 2012: International Workshop on Artificial Intelligence and Cybersecurity (AIC 2012) (Organizing co-Chair),
​· 2011: International Conference on Document Analysis and Recognition (ICDAR 2011) (Publication Chair),
​· 2011: First Asian Conference on Patter Recognition (ACPR 2011) (Publicity Chair),
External Grant Reviewer
​· 2017~: Singapore AI Programme Funding,
​· 2012~: Hong Kong RGC Funding,
​· 2010~: NSFC,
Member: Committee/Task Force
​· 2012-~: CCF National Committee member of Artificial Intelligence and Pattern Recognition,
· 2014-~: National Artificial Intelligence Society: Committee member of Pattern Recognition


Selected Journal Publications (from 60+)

Publication by DBLP Publication by Google Scholar Detailed Publication
. Xiaobo Jin, Xu-Yao Zhang, Kaizhu Huang, Guanggang Geng, Stochastic Conjugate Gradient Algorithm with Variance Reduction, IEEE Transactions on Neural Networks and Learning Systems, accepted, 2018.
. Yanchun Xie, Jimin Xiao, Kaizhu Huang, Jeyarajan Thiyagalingam, Yao Zhao, Correlation Filter Selection for Visual Tracking Using Reinforcement Learning, IEEE Transactions on Circuits and Systems for Video Technology, accepted, 2018.
. Jieming Ma, Haochuan Jiang, Ziqiang Bi, Kaizhu Huang et al. , Maximum Power Point Estimation for Photovoltaic Strings Subjected to Partial Shading Scenarios, IEEE Transactions on Industry Applications, accepted,2018.
. Fanzhou Xiong, Biao Sun, Xu Yang,Kaizhu Huang, Hong Qiao, Amir Hussain, Zhi-Yong Liu,Guided Policy Search for Sequential Multi-Task Learning, IEEE Transactions on Systems Man and Cybernetics-Systems, accepted, 2018.
. Xi Yang, Kaizhu Huang, Rui Zhang, Amir Hussain, Learning Latent Features with Infinite Non-negative Binary Matrix Tri-factorization, IEEE Transactions on Emerging Topics in Computational Intelligence, accepted, 2018.
. Changzhi Luo, Meng Wang, Kaizhu Huang, Jiashi Feng, Zero-Shot Learning via Attribute Regression and Class Prototype Rectification, IEEE Transactions on Image Processing, 27(2):637-648, 2018. (JCR Q1)
. Xiao-Bo Jin, Guang-Gang Geng, Guo-Sen Xie, Kaizhu Huang, Pair-wise Loss for Optimizing NDCG Approximately, Information Sciences, Volume 453, Pages 50-65, 2018. (JCR Q1)
. Jianyu Sun, Guoqiang Zhong, Kaizhu Huang, Junyu Dong, Banzhaf Random Forests: Cooperative Game Theory Based Random Forests with Consistency, Neural Networks , 106: 20-29, 2018. (JCR Q1)
. Kaizhu Huang, Haochuan Jiang, Xu-Yao Zhang, Rui Zhang, Field Support Vector Machines, IEEE Transactions on Emerging Topics in Computational Intelligence, 1(6), 454-463, 2017.
. Jieming Ma, Haochuan Jiang, Kaizhu Huang, Ziqiang Bi, Kalok Man, Novel Field-Support Vector Regression-Based Soft Sensor for Accurate Estimation of Solar Irradiance, IEEE Transactions on Circuits and Systems I, 64(12): 3183-3191, 2017.
. Summrina Kanwal Wajid, Amir Hussain, Kaizhu Huang, Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH)-Based Feature Extraction‒ A Novel Technique, Expert Systems With Applications, accepted, 2017.
. Yao Lu, Kaizhu Huang, Cheng-Lin Liu, A fast projected fixed-point algorithm for large graph matching, Pattern Recognition, 60: 971-982, 2016 . (2014 ISI Impact Factor 3.096)
· Haiqin Yang, Kaizhu Huang, Irwin King, Michael R. Lyu, Maximum Margin Semi-supervised Learning with Irrelevant Data, Neural Networks, 70: 90-102, 2015. (2014 ISI Impact Factor 2.076)
​· Yan-Ming Zhang, Kaizhu Huang, Cheng-Lin Liu, MTC: A Fast and Robust Graph-based Transductive Learning Algorithm, IEEE Transactions on Learning Systems and Neural Networks, 26(9): 1979-1991, 2015. (2014 ISI Impact Factor 4.370)
​· Xu-Yao Zhang, Peipei Yang, Yan-Ming Zhang, Kaizhu Huang, and Cheng-Lin Liu, Combination of Classification and Clustering Results with Label Propagation, IEEE Signal Processing Letters, 21(5): 610-614, 2014. (2012 ISI Impact Factor 1.674)
​· Yan-Ming Zhang, Kaizhu Huang, Xinwen Hou, and Cheng-Lin Liu, Learning Locality Preserving Graph from Data, IEEE Trans. Cybnetics, 44(11): 2088-2098, 2014. (2012 ISI Impact Factor 3.236)
​· Xu-Cheng Yin, Kuang Yin, Kaizhu Huang, Hong-Wei Hao, Robust Text Detection in Natural Images, IEEE Trans. on Pattern Analysis and Machine Intelligence, 36(5): 970-983, 2014. (2012 ISI Impact Factor4.795)
​· Xu-Cheng Yin, Kaizhu Huang, Hong-Wei Hao, Convex Ensemble Learning with Sparsity and Diversity, Information Fusion Journal, in press, 2014. (2012 ISI Impact Factor 2.262)
​· Peipei Yang, Kaizhu Huang, Cheng-lin Liu, Geometry Preserving Multi-task Metric Learning, Machine Learning, Volume 92(1), 133-175, 2013. (2012 ISI Impact Factor 1.467)
​· Bo Xu, Kaizhu Huang, Cheng-Lin Liu: Maxi-Min Discriminant Analysis via Online Learning. Neural Networks 34: 56-64, 2012. (2012 ISI Impact Factor 1.927)
​· Kaizhu Huang, Danian Zheng, Irwin King, Michael R. Lyu, Arbitrary Norm Support Vector Machines. Neural Computation, Vol. 21, No. 2: 560–582, 2009. (2012 ISI Impact Factor 1.760)
​· Zenglin Xu, Kaizhu Huang, Jianke Zhu, Irwin King, Michael R. Lyu: A novel kernel-based maximum a posteriori classification method. Neural Networks 22(7): 977-987, 2009. (2012 ISI Impact Factor 1.927)
​· Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu. M4: Learning Large Margin Machines Locally and Globally, IEEE Trans. Neural Networks, vol. 19, iss. 2, pp. 260-272, 2008. (2012 ISI Impact Factor 3.766)
​· Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Maximizing Sensitivity in Medical Diagnosis Using Biased Minimax Probability Machine. IEEE Trans. Biomedical Engineering, Vol 53, Issue 5, 821- 831, May 2006. (2012 ISI Impact Factor 2.348)
​· Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Imbalanced Learning With Biased Minimax Probability Machine. IEEE Trans. System Man, Cybnetics, Part B, Vol 36, No 4, 913 – 923, August, 2006. (2012 ISI Impact Factor 3.236)
​· Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, and Laiwan Chan, The Minimum Error Minimax Probability Machine. Journal of Machine Learning Research, Vol. 5, pp. 1253-1286, October 2004. (2012 ISI Impact Factor 3.420)

Selected Conference Publications (from 90+)

. Jiezhu Cheng, Kaizhu Huang, Zibin Zheng, Towards Better Forecasting by Fusing Near and Distant Future Visions, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
. Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Mingjie Sun, Kaizhu Huang, Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
. Xiao-Bo Jin, Guo-Sen Xie, Kaizhu Huang, Jianyu Miao, Qiufeng Wang, Beyond Attributes: High-order Attribute Features for Zero-shot Learning, In International Conference on Computer Vision Workshop (ICCV-W), 2019.
. Shufei Zhang, Kaizhu Huang, Rui Zhang, and Amir Hussain, Generalized Adversarial Training in Riemannian Space , In IEEE Fifteen Conference on Data Mining (ICDM’2019) , 2019, (acceptance rate 9.1%).
·Xi Yang, Yuyao Yan, Kaizhu Huang, and Rui Zhang, VSB-DVM: An End-to-end Bayesian Nonparametric Generalization of Deep Variational Mixture Model, In IEEE Fifteen Conference on Data Mining (ICDM’2019), 2019, (acceptance rate 9.1%).
· Hang Dong, Wei Wang, Kaizhu Huang and Frans Coenen, Joint Multi-Label Attention Networks for Social Text Annotation, In Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019)" , 2019, (acceptance rate 22.6%) .
· Chunchun Lv,
Kaizhu Huang, and Hai-Ning Liang, A Unified Gradient Regularization Family for Adversarial Examples, In IEEE Fifteen Conference on Data Mining (ICDM’2015) , 2015, (acceptance rate 8.4%).
· Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, Scalable Data Analytics: Theory and Applications, in Eighth ACM International Conference on Web Search and Data Mining (WSDM’2015), 2015.
· Xu-Yao Zhang, Kaizhu Huang, and Chenglin Liu, Feature Transformation with Class Conditional Decorrelation. In IEEE Thirteen Conference on Data Mining (ICDM’2013), pages 887-896, 2013. (Acceptance Rate = 94/809 =11.6%)
· Yan-Ming Zhang, Kaizhu Huang, Guanggang Geng, Cheng-Lin Liu, Fast kNN Graph Construction with Locality Sensitive Hashing, In European conference on Machine Learning (ECML’2013), (Acceptance Rate =25.0%)
· Xu-Cheng Yin, Xuwang Yin, Kaizhu Huang, Hong-Wei Hao, Accurate and Robust Text Detection: A Step-In for Text Retrieval in Natural Scene Images, ACM Special Interest Group on Information Retrieval (SIGIR’2013), pages 1091-1092, 2013.
· Peipei Yang, Kaizhu Huang, and Cheng-Lin Liu, Geometry Preserving Multi-task Metric Learning. In European conference on Machine Learning (ECML’2012), LNCS Vol.7523, pp.648-664, 2012 (Acceptance Rate =23.7%)
· Guoqiang Zhong, Kaizhu Huang, and Cheng-Lin Liu, Low Rank Metric Learning with Manifold Regularization. In IEEE Eleventh conference on Data Mining (ICDM’2011), 1266-1271, 2011. (Short paper, Acceptance Rate =18%)
· Yan-Ming Zhang, Kaizhu Huang, and Cheng-Lin Liu,Fast and Robust Graph-based Transductive Learning via Minimum Tree Cut, IEEE Eleventh conference on Data Mining (ICDM’2011), 952-961, 2011. (Full paper, Acceptance Rate =12%)
· Xu-Yao Zhang, Kaizhu Huang, and Cheng-Lin Liu, Pattern Field Classification with Style Normalized Transformation, In The International Joint Conference on Artificial Intelligence (IJCAI’2011), 1621-1626, 2011. (Acceptance Rate =227/1325=17%)
· Kaizhu Huang, Rong Jin, Zenglin Xu, and Cheng-Lin Liu Robust Metric Learning with Smooth Optimization , In The 26th Conference on Uncertainty in Artificial Intelligence (UAI’2010), 244-251,2010. (Acceptance Rate =30/260=11.3%)
· Kaizhu Huang, Yiming Ying, Colin Campbell, GSML: A Unified Framework for Sparse Metric Learning,IEEE Ninth conference on Data Mining (ICDM’2009), 189-198, 2009. (Acceptance Rate = 70/786 = 8.9%)
· Yiming Ying, Kaizhu Huang, Colin Campbell,, Sparse Metric Learning via Smooth Optimization, in Proc. Advances in Neural Information Processing System 22 (NIPS’2009), Cambridge, MA, 2009. (Acceptance Rate = 263/1105 = 23.8%)
· Zenglin Xu, Rong Jin, Kaizhu Huang, Irwin King, Michael R. Lyu, Semi-supervised Text Categorization by Active Search, In ACM 17th Conference on Information and Knowledge Management (CIKM’2008), 1517-1518, 2008. (Acceptance rate = 132+124)/772=16%)
· Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, Semi-supervised Learning from General Unlabeled Data, In IEEE Eighth conference on Data Mining (ICDM’2008), 273-282, 2008. (Acceptance Rate =70/724=9.6%)
· Kaizhu Huang, Irwin King, Michael R. Lyu, Direct Zero-norm Optimization for Feature Selection, In IEEE Eighth Conference on Data Mining (ICDM’2008), 845-850, 2008. Short paper (Acceptance Rate=(70+74)/724 =20%)
· Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, Learning Classifiers from Imbalanced Data Based on Biased Minimax Probability Machine. Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’2004), 558-563, Washington, DC, June 27 -July 2, 2004. (Acceptance Rate = 17%)
· Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, Learning Large Margin Machines Locally and Globally. Proceedings international Conference on Machine Learning (ICML’2004), Banff, Canada, pp. 401-408, 2004. (Acceptance Rate = 32%) 

Professional Memberships

​· IEEE
​· ACM
  名称 大小
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