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陈欢欢


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陈欢欢

 

 

电 话:0551-

E- Mail:hchen@ustc.edu.cn

个人主页:http://staff.ustc.edu.cn/~hchen/  

 

主要研究方向:机器学习、数据挖掘、计算智能、地下管网探测与数据融合、演化计算等。

     

陈欢欢,1982年1月生,博士,教授,博导。2004年获中国科技大学学士学位,2008年获英国伯明翰大学博士学位。2011入选中组部第二 批“青年千人”,现为中国科学技术大学计算机学院教授。获2011年IEEE计算智能协会优秀博士论文奖、全英杰出博士论文奖。在国外重要学术期 刊IEEE Transactions on Neural Networks,IEEE Transactions on Knowledge and Data Engineering,IEEE  Transactions on Evolutionary Computation和人工智能领域重要国际学术会议 IJCAI、KDD、ECAI等发表论文30余篇。其中,在神经网络的国际权威期刊IEEE Transactions on Neural Networks上的论文获2012年度最佳论文奖。由于在神经网络与学习系统等方面的贡献,申请人获得2015年度国际神经网络学会(International Neural Network Society (INNS))青年科学家奖(Young Investigator Award)。主持承担了首批国家重点研发计划“大数据知识工程基础理论及其应用研究”五课题之一“知识导航中的交互机理”、国家基金委重大研究计划培育项目、国家基金委面上项目、国家基金委与英国皇家学会合作交流项目、国家基金委青年项目等。

 

国际学术服务:

01. IEEE Transactions on Neural Networks and Learning Systems (TNNLS),Associate Editor 副编(2016-)

02. IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), Associate Editor 副编(2016-)

03. IEEE Computational Intelligence Society Social Media Committee Chair, 2015-

04. IEEE World Congress on Computational Intelligence (IEEE WCCI) Publications Integrity Chair, 2016

     

获 奖 情 况

   

2015年国际神经网络学会青年科学家奖(International Neural Network Society (INNS) Young Investigator Award)

2009年度IEEE Transactions on Neural Networks Outstanding最佳论文奖 (2012年颁发)

2011年IEEE计算智能学会杰出博士论文奖 (Outstanding PhD Dissertation Award)

英国计算机学会杰出博士论文奖

 

主 要 论 著

   

 

[01]  Huanhuan Chen, Peter Tino, and Xin Yao. Efficient Probabilistic Classification Vector Machine with Incremental Basis Function Selection. IEEE Transactions on Neural Networks and Learning Systems. Early access online. DOI: 10.1109/TNNLS.2013.2275077
[02] Huanhuan Chen, Peter Tino, Ali Rodan and Xin Yao. Learning in the Model Space for Cognitive Fault Diagnosis. IEEE Transactions on Neural Networks and Learning Systems. Early access online. DOI: 10.1109/TNNLS.2013.2256797
[03] Huanhuan Chen, Fengzhen Tang, Peter Tino and Xin Yao. Model-based Kernel for Efficient Time Series Analysis. In Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'13), pages 392-400, Chicago, USA, August 11-14, 2013. (Oral presentation).
[04] Rodrigo G. F. Soares, Huanhuan Chen and Xin Yao. Semi-supervised Classification with Cluster Regularisation. IEEE Transactions on Neural Networks and Learning Systems. vol.23, no.11, pp.1779-1792, November 2012.
[05] Huanhuan Chen and Anthony G Cohn. Buried Utility Pipeline Mapping Based on Multiple Spatial Data Sources: A Bayesian Data Fusion Approach. In Proceedings of the 22nd International Joint Conferences on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011.
[06] Huanhuan Chen, Peter Tino and Xin Yao. Probabilistic Classification Vector Machines. IEEE Transactions on Neural Networks. vol.20, no.6, pp.901-914, June 2009. IEEE Transactions on Neural Networks Outstanding 2009 Paper Award (bestowed in 2012)
[07] Huanhuan Chen and Xin Yao. Multi-objective Neural Network Ensembles based on Regularized Negative Correlation Learning. IEEE Transactions on Knowledge and Data Engineering. vol. 22, no. 12, pp. 1738-1751, December 2010.
[08] Huanhuan Chen and Xin Yao. Regularized Negative Correlation Learning for Neural Network Ensembles. IEEE Transactions on Neural Networks. vol.20, no.12, pp.1962-1979, December 2009.
[09] Huanhuan Chen, Peter Tino and Xin Yao. Predictive Ensemble Pruning by Expectation Propagation. IEEE Transactions on Knowledge and Data Engineering. vol.21, no.7, pp.999-1013, July 2009.
[10] Lean Yu, Huanhuan Chen, Shouyang Wang and K. K. Lai. Evolving Least Squares Support Vector Machines for Stock Market Trend Mining. IEEE Transactions on Evolutionary Computation. vol 13, No. 1, Feb 2009.
[11] Huanhuan Chen, Anthony Cohn and Xin Yao. Ensemble Learning by Negative Correlation Learning. In Ensemble Learning: Theory and Application, Zhang Cha and Yunqian Ma (Ed.). Springer-Verlag, 2012.
[12] Arjun Chandra, Huanhuan Chen and Xin Yao Trade-off between Diversity and Accuracy in Ensemble Generation. In Multi-objective Machine Learning, Yaochu Jin (Ed.), pp.429-464, Springer-Verlag, 2006. (ISBN: 3-540-30676-5)
[13] Huanhuan Chen and Anthony G Cohn. Buried Utility Pipeline Mapping based on Street Survey and Ground Penetrating Radar. In Proceedings of the European Conference on Artificial Intelligence (ECAI'10), Lisbon, Portugal, 2010.
[14] Huanhuan Chen and Anthony G Cohn. Probabilistic Conic Mixture Model and its Applications to Mining Spatial Ground Penetrating Radar Data. In Workshop in SIAM Conference on Data Mining (WSDM'10), Columbus, 2010.
[15] Huanhuan Chen and Anthony G Cohn. Probabilistic Robust Hyperbola Mixture Model for Interpreting Ground Penetrating Radar Data. In Proceedings of the 2010 IEEE World Congress on Computational intelligence (WCCI'10), Barcelona, 2010.
[16] Shan He, Huanhuan Chen, Xiaoli Li and Xin Yao. Profiling of mass spectrometry data for ovarian cancer detection using negative correlation learning. In Proceedings of the 19th International Conference on Artificial Neural Networks (ICANN'09), Cyprus, 2009.
[17] Huanhuan Chen, Peter Tino and Xin Yao. A Probabilistic Ensemble Pruning Algorithm. In Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (WICDM'06), Hong Kong, 2006.

 

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