Kaizhu Huang

Profile

Dr. Kaizhu Huang is currently Professor at Department of Intelligent Science and Associate Dean of Research at School of Advanced Technology, Xi’an Jiaotong-Liverpool University. He was an Affiliated Full Professor at University of Electronic Science and Technology of China. He acted as Head of Department from 2016.9-2019. 8. Prof. Huang received the B.Sc. degree in Engineering in 1997, the M.Sc degree from Institute of Automation, Chinese Academy of Sciences in July 2000 and the Ph.D. degree from The Chinese Univ. of Hong Kong (CUHK) in 2004. He worked as a researcher in Fujitsu R&D Centre, CUHK, and University of Bristol from 2004 to 2009. He worked as an Associate Professor at National Laboratory of Pattern Recognition (NLPR), Chinese Academy of Sciences from 2009 to 2012. Dr. Huang is the recipient of 2011 Asian Pacific Neural Network Society (APNNS) Younger Researcher Award. He also received Best Book Award in National “Three 100” Competition 2009. He has published 9 books in Springer and over 200 international research papers (about 70+ SCI-indexed international journals and 90+ EI conference papers) e.g., in journals (JMLR, Neural Computation, IEEE T-PAMI, IEEE T-NNLS, IEEE T-BME, IEEE T-SMC, NN) and conferences (NIPS, IJCAI, SIGIR, UAI,CIKM, ICDM, ICML,ECML, CVPR). He serves as Associate Editor or Senior Associate Editor in four international journals: Neural Networks (JCR Tier1 Journal), BMC Big Data Analytics, Cognitive Computation (JCR Tier1 Journal) , Neurocomputing (JCR Tier 1 Journal). He also serves as Advisory Board Member in many Springer Book Series. He served as (senior) program committees in many international conferences such as NIPS, ICLR, AAAI, IJCAI, WSDM, ACM-WI, ICONIP, IJCNN, WCCI, EANN, and KDIR. Especially, he serves as chairs in several major conferences or workshops, e.g., CMVIT 2021 (General co-Chair), WCCI 2020 (Conflict co-Chair), ACSS 2017 (General co-Chair), ACML 2015 (Publication co-Chair), SDA 2015 (Organizing co-Chair), ICONIP 2014 (Program co-Chair), DMC 2012-2017 (Organizing co-Chair), ICDAR 2011 (Publication Chair), ACPR 2011 (Publicity Chair), ICONIP2006, 2009-2011 (Session Chair).
  • Qualifications

    • PhD in Computer Science and Engineering , The Chinese University of Hong Kong (CUHK), 2004
    • MEng in Pattern Recognition and Intelligent Systems, Institute of Automation, Chinese Academy of Sciences, 2000
    • BEng in Automation, Xi'an Jiaotong University, - 1997
  • Experience

    • Associate Dean of Research, School of Advanced Technology, XJTLU 2020-
    • Head of EEE Department, Xi'an Jiaotong-Liverpool University - 2016 to 2019
    • Associate Professor, Professor, Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University (XJTLU) - 2013 to Present
    • Associate Professor, National Laborotory of Pattern Recognition, Chinese Academy of Sciences - 2009 to 2012
    • Researcher, Bristol University - 2008 to 2009
    • Research Fellow, Chinese University of Hong Kong - 2007 to 2008
    • Research Scientist, Fujitsu R&D Centre - 2004 to 2007
  • Research interests

    • Machine Learning Pattern Recognition Information Retrieval Image Processing
  • Articles

    • More publications can be seen at http://www.premilab.com/KaizhuHUANG.ashx
    • Dong, Hang; Wang, Wei; Huang, Kaizhu; Coenen, Frans Automated Social Text Annotation with Joint Multi-Label Attention Networks, IEEE Transactions on Neural Networks and Learning Systems, accepted, 2020.
    • Yanchun Xie, Jimin Xiao, Kaizhu Huang, et al.: Correlation Filter Selection for Visual Tracking Using Reinforcement Learning. IEEE Trans. Circuits Syst. Video Techn. 30(1): 192-204 (2020)
    • Xiaobo Jin, Xu-Yao Zhang, Kaizhu Huang, Guanggang Geng, Stochastic Conjugate Gradient Algorithm with Variance Reduction, IEEE Transactions on Neural Networks and Learning Systems, 30(5): 1360-1369, 2019.
    • Fanzhou Xiong, Biao Sun, Xu Yang,Kaizhu Huang et al.,Guided Policy Search for Sequential Multi-Task Learning, IEEE Transactions on Systems Man and Cybernetics-Systems, 49(1): 216-226, 2019.
    • 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, 1890 - 1902, Volume: 55 , Issue: 2, 2019
    • Xi Yang, Kaizhu Huang et al. Learning Latent Features with Infinite Non-negative Binary Matrix Tri-factorization, IEEE Transactions on Emerging Topics in Computational Intelligence, 2(6): 450-463, 2018.
    • Fanzhou Xiong, Biao Sun, Xu Yang, Kaizhu Huang et al. Guided Policy Search for Sequential Multi-Task Learning, IEEE Transactions on Systems Man and Cybernetics-Systems, Pages 1-11, issues 99, 2018.
    • Kaizhu Huang, Haochuan Jiang, Xu-Yao Zhang, Field Support Vector Machines, IEEE Transactions on Emerging Topics in Computational Intelligence, 1(6), 454-463, 2017.
    • Ma, Jieming, Jiang, Haochuan, Huang, Kaizhu et al. 2017 'Novel Field-Support Vector Regression-Based Soft Sensor for Accurate Estimation of Solar Irradiance', IEEE Transactions on Circuits and Systems I: Regular Papers, 64(12): 3183-3191, 2017. (JCR Tier 2 journal, 2016 ISI impact factor 2.407)
    • Luo, Changzhi, Wang, Meng, Huang, Kaizhu & Feng, Jiashi 2017 'Zero-Shot Learning via Attribute Regression and Class Prototype Rectification', IEEE Transactions on Image Processing, 27(2):637-648, 2018. (JCR Tier 1, 2016 ISI impact factor 4.828)
    • Zhang, Yan-ming, Huang, Kaizhu & Liu, Cheng-Lin 2015 'MTC: A Fast and Robust Graph-based Transductive Learning Algorithm', IEEE Transactions on Neural Networks, vol. 26, no. 9, p. 1979-1991
    • Yin, Xu-Cheng, Yin, Xuwang, Huang, Kaizhu & Hao, Hong-Wei 2014 'Robust Text Detection in Natural Scene Images', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 5, p. 970-983
    • Zhang, Yan-Ming, Huang, Kaizhu, Hou, Xinwen & Liu, Cheng-Lin 2014 'Learning Locality Preserving Graph from Data', IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 44, no. 11, p. 2088-2098
    • Zhang, Xu-Yao, Yang, Pei-pei, Zhang, Yan-Ming, Huang, Kaizhu & Liu, Cheng-Lin 2014 'Combination of Classification and Clustering Results with Label Propagation', IEEE Signal Processing Letters, vol. 21, no. 5, p. 610-614
    • Huang, Kaizhu, Yang, Haiqin, King, Irwin & Lyu, Michael 2008 'M4: Learning Large Margin Machines Locally and Globally', IEEE Transactions on Neural Networks, vol. 19, no. 2, p. 260-272
    • Huang, Kaizhu, Yang, Haiqin, King, Irwin & Lyu, Michael 2006 'Maximizing Sensitivity in Medical Diagnosis Using Biased Minimax Probability Machine', IEEE Transactions on Cybernetics , vol. 36, no. 4, p. 821-831
    • Huang, Kaizhu, Yang, Haiqin, King, Irwin & Lyu, Michael 2006 'Maximizing sensitivity in medical diagnosis using biased minimax probability Machine', IEEE Transactions on Biomedical Engineering, vol. 53, no. 5, p. 821-831
    • Huang, Kaizhu, Yang, Haiqin, King, Irwin, Lyu, Michael & Chan, Laiwan 2004 'The Minimum Error Minimax Probability Machine', Journal of Machine Learning Research, vol. 5, no. , p. 1253-128
  • Proceedings

    • More publications can be seen at http://www.premilab.com/KaizhuHUANG.ashx
    • Guanyu Yang, Kaizhu Huang, Rui Zhang, John Goulermas and Amir Hussain, Inductive Generalized Zero-shot Learning with Adversarial Relation Network, European Conference on Machine Learning (ECML), 2020.(acceptance rate 19%).
    • Yanchun Xie, Jimin XIAO, Mingjie Sun, Chao Yao, Kaizhu Huang, Matching Representations Matters: End-to-End Learning for Neural Texture Transfer, European Conference on Computer Vision (ECCV), 2020.(spotlight paper, acceptance rate 5%).
    • Jiezhu Cheng,Kaizhu Huang, Zibin Zheng, Towards Better Forecasting by Fusing Near andDistant Future Visions, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI),2020.
    • Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Mingjie Sun,Kaizhu Huang, Reliability DoesMatter: An End-to-End Weakly Supervised Semantic Segmentation Approach, Thirty-FourthAAAI 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 Com-puter Vision Workshop (ICCV-W), 2019.
    • Shufei Zhang*,Kaizhu Huang, Rui Zhang, and Amir Hussain, Generalized Adversarial Trainingin Riemannian Space , In IEEE Fifteen Conference on Data Mining (ICDM2019) , 2019,(acceptance rate 9.1%)
    • Xi Yang*, Yuyao Yan*,Kaizhu Huang, and Rui Zhang, VSB-DVM: An End-to-end BayesianNonparametric Generalization of Deep Variational Mixture Model, In IEEE Fifteen Conferenceon Data Mining (ICDM2019) , 2019, (acceptance rate 9.1%)
    • Hang Dong*, Wei Wang,Kaizhu Huang, and Frans Coenen, Joint Multi-Label Attention Net-works for Social Text Annotation, In Annual Conference of the North American Chapter of theAssociation for Computational Linguistics: Human Language Technologies (NAACL-HLT2019) , 2019, (acceptance rate 22.6%)
    • (2015-2016) A Unified Gradient Regularization Family for Adversarial Examples In: Proceedings of IEEE Fifteen Conference on Data Mining (ICDM 2015), pp.
    • (2014-2015) Scalable Data Analytics: Theory and Applications. In: Eighth ACM International Conference on Web Search and Data Mining (WSDM 2015), pp. 425-426
    • (2013-2014) Accurate and Robust Text Detection: A Step-In for Text Retrieval in Natural Scene Images In: ACM Special Interest Group on Information Retrieval (SIGIR2013), pp.
    • (2012-2013) Fast kNN Graph Construction with Locality Sensitive Hashing In: European conference on Machine Learning (ECML 2013), pp. 660-674
    • (2012-2013) Feature Transformation with Class Conditional Decorrelation In: IEEE Thirteen Conference on Data Mining (ICDM 2013), pp. 674
    • (2011-2012) Geometry Preserving Multi-task Metric Learning In: European conference on Machine Learning (ECML 2012), pp. 648-664
    • (2011-2012) Low Rank Metric Learning with Manifold Regularization In: IEEE Eleventh conference on Data Mining (ICDM 2011), pp.
    • (2010-2011) Pattern Field Classification with Style Normalized Transformation In: International Joint Conference on Artificial Intelligence (IJCAI 2011), pp. 1621-1626
    • (2010-2011) Fast and Robust Graph-based Transductive Learning via Minimum Tree Cut In: IEEE Eleventh conference on Data Mining (ICDM 2011), pp. 952-961
    • (2010-2011) Robust Metric Learning with Smooth Optimization In: The 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), pp.
    • (2009-2010) Sparse Metric Learning via Smooth Optimization In: Advances in Neural Information Processing System 22 (NIPS 2009), pp.
    • (2009-2010) GSML: A Unified Framework for Sparse Metric Learning In: IEEE 9th Conference on Data Mining (ICDM 2009), pp.
    • (2008-2009) Direct Zero-norm Optimization for Feature Selection In: IEEE 8th International Conference on Data Mining (ICDM 2008), pp.
    • (2008-2009) Semi-supervised Learning from General Unlabeled Data In: IEEE 8th International Conference on Data Mining (ICDM 2008), pp.
  • Books, monographs, compilations and manuals

    • Huang, Kaizhu, King, Irwin & Sichtig (ed.), H. 2014 Part C: Machine Learning Methods: Handbook of Bio- and Neuroinformatics,, Springer,
    • Brown, C, Sichtig, H, King, Irwin, Huang, Kaizhu & Masulli (ed.), F. 2014 Part D: Modeling Regulatory Networks: The Systems Biology Approach: Handbook of Bio- and Neuroinformatics,, Springer,
    • Loo, Chu Kiong, Yap, Keem Siah, Wong, Kok Wai, Teoh, Andrew & Huang, Kaizhu 2014 Neural Information Processing - 21st International Conference Proceedings, Part I, Springer,
    • Loo, Chu Kiong, Yap, Keem Siah, Wong, Kok Wai, Teoh, Andrew & Huang, Kaizhu 2014 Neural Information Processing - 21st International Conference Proceedings, Part II, Springer,
    • Loo, Chu Kiong, Yap, Keem Siah, Wong, Kok Wai, Teoh, Andrew & Huang, Kaizhu 2014 Neural Information Processing - 21st International Conference Proceedings, Part III, Springer,
    • Huang, Kaizhu, Yang, Haiqin, King, Irwin & Lyu, Michael 2008 Machine Learning: Modeling Data Locally and Globally,, Springer Verlag,
  • Grants

    • Huang, Kaizhu, 2019-2022, Principal Investigator, Investigation of Classifier Design Based on Adversarial Learning, NSFC General Program
    • Huang, Kaizhu 2017-2020, Principal Investigator, Deep-learning Based Intelligent Scene Understanding, XJTLU KSF
    • Huang, Kaizhu 2017-2019, Principal Investigator, Collective Classification Based on Deep Neural Networks, Suzhou S&T Programme
    • Huang, Kaizhu 2016-2019, Principal Investigator, Suzhou Municipal Key Laboratory Construction Funding, Suzhou S&T Programme
    • Huang, Kaizhu 2015-2018, Principal Investigator, Cross-domain and Collective Pattern Classification Theory and Applications, NSFC General Program
    • Huang, Kaizhu 2013-2014, Principal Investigator, A Survey on Deep Neural Networks in Pattern Recognition, Collaborate with Fujitsu Research and Development Center co., LTD
    • Huang, Kaizhu 2013-2016, Co-PI, Theory and Key techniques for perturbation based character recognition, NSFC General Program
    • Huang, Kaizhu 2012-2016, Principal Investigator, Large-scale metric learning for public security, National 973 Sub-task
    • Huang, Kaizhu 2011-2013 , Principal Investigator, Sparse Metric Learning for complicated data, NSFC General Program
  • Professional service activities

    • 2017-present PC or Senior PC, IJCAI,AAAI, NIPS, ICLR, ICML, ECCV
    • 2019-Present, Action Editor, Neural Networks (JCR Tier 1 Journal)
    • 2016-Present, Associate Editor, Cognitive Computation (JCR Tier 1 Journal)
    • 2016-Present, Associate Editor, Neurocomputing (JCR Tier 1 Journal)
    • 2016-Present, Section Editor (Senior Associate Editor), Springer Nature BMC Big Data Analytics.
    • 2015 , Publication co-Chair, 2015 Asian Conference on Machine Learning.
    • 2015 , Lead Organizing Chair, International Workshop of Scalable Data Analytics: Theory & Applications (S-DATA).
    • 2014 -2017 Advisory Board Member, Springer Series in Bio-Neuroinformatics.
    • 2014- Present, NSFC Grant Reviewer.
    • 2014, Program co-Chair, International Conference on Neural Information Processing (ICONIP 2014).
    • 2014 CCF National Committee of Artificial Intelligence and Pattern Recognition.
    • 2014 International Workshop on Data Mining and Cybersecurity (DMC 2014).
    • 2014 Hong Kong RGC Funding.
    • 2013 International Workshop on Data Mining and Cybersecurity (DMC 2013).
    • 2012 International Workshop on Data Mining and Cybersecurity (DMC 2012).
    • 2011 First Asian Conference on Patter Recognition (ACPR 2011).
    • 2011 Eleventh International Conference on Document Analysis and Recognition (ICDAR 2011).
  • Awards and honours

    • 2020 Best Runner-up Paper Award, International Conference on Neural Information Processing
    • 2019 Best Paper Award, International Conference on Brain Inspired Cognitive Systems
    • 2019 Best Student Paper Award, International Conference on Brain Inspired Cognitive Systems
    • 2018 Best Student Paper Award, International Conference on Brain Inspired Cognitive Systems
    • 2017 Best Paper Finalist Award, International Conference on Neural Information Processing
    • 2011 APNNA Young Researcher Award, Asia Pacific Neural Network Assembly
    • 2007 Postdoctoral Fellowship, Chinese University of Hong Kong
    • 2006 President Award, Fujitsu Laboratories
    • 2006 Q-finity Award, Fujitsu Laboratories
    • 2005 Excellent Research Award, Fujitsu R&D Centre
    • 2004 Special Research Award, Fujitsu R&D Centre
    • 2001 Postgraduate Scholarshop, Chinese University of Hong Kong
  • Telephone

    +86-512-88161404
  • Email

    Kaizhu.Huang@xjtlu.edu.cn
  • Address

    EE510A
    Electrical and Electronic Engineering
    EE Building
    Xi'an Jiaotong-Liverpool University
    111 Ren'ai Road
    Suzhou Dushu Lake Science and Education Innovation District
    Suzhou Industrial Park
    Suzhou
    P. R. China
    215123
    Suzhou Dushu Lake Science and Education Innovation District
    Suzhou Industrial Park
    Suzhou
    P.R.China
    215123