周志华 - 南京大学 - 人工智能学院

个人简介

Professor, Computer Science and Artificial Intelligence, Nanjing University, China Fellow of the ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, CCF, CAAI Correspondence Mail:Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Xianlin Campus Mailbox 603 163 Xianlin Avenue, Qixia District Nanjing 210023, China 周志华著.机器学习,北京:清华大学出版社,2016. (ISBN 978-7-302-42328-7) English version 周志华, 王魏, 高尉, 张利军著.机器学习理论导引,北京:机械工业出版社,2020. (ISBN 978-7-111-65424-7) Z.-H. Zhou. Ensemble Methods: Foundations and Algorithms, Boca Raton, FL: Chapman & Hall/CRC, 2012. (ISBN 978-1-439-830031), with Japanese version; 中文版 Z.-H. Zhou, Y. Yu, C. Qian. Evolutionary Learning: Advances in Theories and Algorithms, Berlin: Springer, 2019. (ISBN 978-981-13-5955-2); 中文版 Award & Honor (international only) Award (incomplete): Nation Science Review 2019 Best Paper Award IEEE Computer Society Edward J. McCluskey Technical Achievement Award (2019) ACML Distinguished Contributions Award (2019) Academy of Europe Foreign Member (2017) IEEE Computational Intelligence Magazine 2017 Outstanding Paper Award (with Nitesh V. Chawla, Yaochu Jin, and Graham Williams) ACM Fellow (2016) AAAI Fellow (2016) AAAS Fellow (2016) IEEE ICDM Outstanding Service Award (2016) PAKDD Distinguished Contributions Award (2016) ACM Distinguished Scientist (2013) IEEE Computational Intelligence Society Outstanding Early Career Award (2013) IEEE Fellow (2013) IAPR Fellow (2012) PAKDD 2012 Data Mining Competition (Open Category) Grand Prize Winner Team Leader (Members: Nan Li, Chao Qian, Shao-Yuan Li, Yue Zhu, Qing Da) Pattern Recognition Journal 2006-2010 Most Cited Article (with Xiaoyang Tan, Songcan Chen, and Fuyan Zhang) IET/IEE Fellow (2010) ISIBM Outstanding Achievement Award (2009) Keynote/Plenary Speaker (recent incomplete): CVPR'21 (IEEE Conference on Computer Vision and Pattern Recognition), Jun. 2021, virtual WSDM'20 (13th ACM International Conference on Web Search and Data Mining), Feb. 2020, Houston, TX, USA IJCAI'19 (28th International Joint Conference on Artificial Intelligence, Aug. 2019, Macao, China) AISTATS'19 (22nd International Conference on Artificial Intelligence and Statistics, Apr. 2019, Naha, Japan) ICPR'18 (24th International Conference on Pattern Recognition, Aug. 2018, Beijing, China) KSEM'17 (10th International Conference on Knowledge Science, Engineering and Management, Aug. 2017, Melbourne, Australia) AusAI'16 (29th Australasian Joint Conference on Artificial Intelligence, Dec. 2016, Hobart, Austrlia) ISNN'16 (13th International Symposium on Neural Networks, Jul. 2016, Saint Petersburgh, Russia) ICMLA'15 (14th IEEE International Conference on Machine Learning and Applications, Dec. 2015, Miami, FL, USA) ANNPR'14 (6th IAPR International Workshop on Artificial Neural Networks in Pattern Recognition, Oct. 2014, Montreal, Canada) ECDA'14 (European Conference on Data Analysis, Jul. 2014, Bremen, Germany) Course Introduction to Machine Learning For undergraduate students Advanced Machine Learning For graduate students Introduction to Data Mining For undergraduate students [go top]

研究领域

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I have wide research interests, mainly including artificial intelligence, machine learning, data mining, pattern recognition, evolutionary computation and multimedia retrieval, among which machine learning and data mining are my core research areas. I am particularly interested in the problem of how to enable computing machines to handle "ambiguity".Currently I am interested in the following ML/DM topics: Multi-label learning Multi-instance learning Mult-view learning Semi-supervised and active learning Cost-sensitive and class-imbalance learning Metric learning, dimensionality reduction and feature selection Ensemble learning Structure learning and clustering Crowdsourcing learning Logic learning For applications, I am mainly interested in the following areas: Image retrieval Web search and mining Face recognition Computer-aided medical diagnosis Bioinformatics Software Mining I am also interested in: Theoretical aspects of evolutionary computation Improving comprehensibility (of complicated learning systems) Research is for fun. If I am interested in some other things, the above list will grow; if something in the list does not attract me any more, the above list will shrink. In short, these are just my current interests.