陈欢欢

电　话：

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，IEEE Fellow、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，现为中国科学技术大学计算机学院教授。在国外重要学术期刊IEEE Transactions on Neural Networks and Learning Systems，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、AAAI等发表论文150余篇。获2024年度“王宽诚育才奖”、2022年安徽省教学成果一等奖与中国科学院“优秀导师奖”。在科研成果中，荣获教育部自然科学二等奖，国际神经网络协会的青年科学家奖，ACM中国新星提名奖。发表的文章荣获IEEE Transactions on Neural Networks 年度最佳论文奖（全年发表论文仅此1篇论文获奖），博士论文荣获IEEE计算智能学会优秀博士论文奖与英国计算机学会全英优秀博士论文奖，IEEE Transactions on Neural Networks and Learning Systems优秀副编奖，TETCI优秀副编奖等。


作为项目负责人主持了科技创新2030-“新一代人工智能”重大项目“跨媒体因果推理与决策关键技术研究”、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项目（已获滚动支持）、国家基金委重点项目、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、安徽省重大专项等。


国际学术服务：

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

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

  • Knowledge-Based Systems，Associate Editor 副编（2024-）

  • IEEE Computational Intelligence Society Student Activities Committee Chair, 2015-

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


获奖情况：

  1. 2024年度中国科大王宽诚育才奖

  2. 2021年IEEE Transactions on Neural Networks and Learning Systems 优秀副编奖

  3. 2022年安徽省教学成果一等奖

  4. 2019年中国科学院优秀导师奖

  5. 2018年教育部自然科学二等奖

  6. 2015年国际神经网络学会青年科学家奖（International Neural Network Society (INNS) Young Investigator Award）

  7. 2009年度IEEE Transactions on Neural Networks Outstanding最佳论文奖 （2012年颁发）

  8. 2011年IEEE计算智能学会杰出博士论文奖 （Outstanding PhD Dissertation Award）

  9. 英国计算机学会杰出博士论文奖


十篇代表性论著：

  1. Taiyu Ban, Lyuzhou Chen, Xiangyu Wang, Xin Wang, Derui Lyu, Huanhuan Chen. Differentiable Structure Learning with Partial Orders. The 38th Conference on Neural Information Processing Systems (NeurIPS'24), 2024.

  2. Xin Wang, Shengfei Lyu, Lishan Yang, Yibing Zhan, Huanhuan Chen. A Dual-module Framework for Counterfactual Estimation over Time. The 41st International Conference on Machine Learning (ICML'24), July 21-27, 2024.

  3. Xiren Zhou, Shikang Liu, Ao Chen, Huanhuan Chen. Learning in CubeRes Model Space for Anomaly Detection in 3D GPR Data. The 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), August 3-9, 2024.

  4. Liangwei Chen, Xiren Zhou, Huanhuan Chen. Audio Scanning Network: Bridging Time and Frequency Domains for Audio Classification. In Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), 2024.

  5. Ao Chen, Xiren Zhou, Yizhan Fan, Huanhuan Chen. Underground Diagnosis Based on GPR and Learning in the Model Space. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, pp. 3832-3844, 2023.

  6. Xingyu Wu, Bingbing Jiang, Yan Zhong and Huanhuan Chen. Multi-target Markov Boundary Discovery: Theory, Algorithm, and Application IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, pp. 4964-4980, 2022.

  7. Xin Wang, Shengfei Lyu, Xingyu Wu, Tianhao Wu, Huanhuan Chen. Generalization Bounds for Estimating Causal Effects of Continuous Treatments. In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS'22), 2022.

  8. Yijun Bian and Huanhuan Chen. When does Diversity Help Generalization in Classification Ensembles?. IEEE Transactions on Cybernetics, 2021.

  9. Bingbing Jiang, Chang Li, Maarten de Rijke, Xin Yao, Huanhuan Chen. Probabilistic Feature Selection and Classification Vector Machine. ACM Transactions on Knowledge Discovery from Data, ACM Transactions on Knowledge Discovery from Data (TKDD), 13(2), 1-27.2019.

  10. Yaqiang Yao, Jie Cao, and Huanhuan Chen. Robust Task Grouping with Representative Tasks for Clustered Multi-Task Learning. In Proceedings of the ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD'19), US, 2019.