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姓    名俞奎性    别  男


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最终学位   博士
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所属院系计算机与信息学院
所属科室(研究所)计算机软件与理论研究所职     称教 授
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E-mail   yukui@hfut.edu.cn
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邮  编
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博士,黄山学者特聘教授,博士生导师,CCF优秀博士论文奖获得者,科技部科技创新2030新一代人工智能重大项目课题负责人,加拿大PIMS博士后奖学金获得者。

2013年6月毕业于合肥工业大学计算机与信息学院,获工学博士学位。2011.08-2012.8作为国家公派联合培养博士生在美国University of Massachusetts Boston计算机系从事合作研究;2013.10-2015.09在加拿大Simon Fraser University 计算科学学院从事博士后研究;2015.10-2018.01作为研究员在澳大利亚University of South Australia信息技术与数学科学学院从事全职研究工作。

中国人工智能学会不确定性人工智能专委会委员,粒计算与知识发现专委会委员;担任国际人工智能领域顶级会议ECAI 2020的领域主席(SPC);担任多个国际著名的学术会议的程序委员会委员(如AAAI、KDD、IJCAI7、CIKM、ICDM等)和国际重要期刊的审稿人。

个人主页:https://sites.google.com/site/yukuiwebsite/

研究方向

研究方向: 因果推断,机器学习,自然语言处理,海洋大数据分析

课题组因果推断与机器学习(Causal Inference and Machine LearningCIML)

欢迎对本领域感兴趣的高年级本科生、研究生与博士生加入!


科研项目:

1.科技部-科技创新2030-“新一代人工智能”重大项目: 认知计算基础理论与方法研究,2020AAA0106100,2020/11-2024/10,在研,课题负责人

2.国家自然科学基金面上项目:面向多源高维数据的局部因果关系挖掘研究,61876206, 2019/01—2022/12,在研,主持

3.智能信息处理山西省重点实验室开放课题:高维数据中的因果推断问题研究,CICIP2020003,2020/10-2022/09,在研,主持

4.教育部创新团队:多源海量动态信息处理,IRT13059,2018/01—2020/12,在研,参加(骨干成员)

5.澳大利亚研究理事会(ARC)项目,Develop efficient data mining methods for evidence based decision making2017/01-2019/12,已结题,参加(主要完成人)

6.澳大利亚研究理事会(ARC)项目,Efficient causal discovery from observational data2017/01-2019/12,已结题,参加(主要完成人)

教学工作

教学情况:

1)人工智能;(2)数据挖掘与大数据分析前沿

学生情况

2020

博士生:李玉玲

硕士生:汪玉杰,曹逸文

2019

博士生:杨帅

硕士生:杨雅静,蔡明珠,胡闻涛

2018

硕士生:郭贤杰,刘超凡

Lab News:

2020-10: 祝贺郭贤杰同学(研三)荣获国家研究生奖学金(学院排名第三)。

2020-10祝贺郭贤杰(研三)和刘超凡(研三)同学荣获一等学业奖学金。

2020-07:因果特征选择综述文章Causality-based Feature Selection: Methods and Evalutions"被顶级期刊ACM Computing Surveys正式录用。

获奖情况

 

[1]2014年度中国计算机学会优秀博士学位论文奖 (全国共10

[2]2014年度加拿大亚太数学科学研究院(PIMS)博士后奖学金(全国共14名)

[3]2014年度ASE (Academy of Science and Engineering)大数据科学奉献奖

[4]2013年度西蒙菲沙大学博士后奖学金 (Ebco Eppich Fellowship Award)

Conference Services:

1. PC member: AAAI'21, PAKDD'21, KDD'20, CIKM'20, ICDM'20, ECAI'20 (SPC), PAKDD'20, KDD'19, CIKM'19, AAAI'19, PAKDD'19, KDD'18, PAKDD'18,CIKM'18, ICBK'18, IJCAI'17,CIKM'17, SIGKDD'16-17, IEEE ICDM'16, ICNC'15-16, IJCAI'15, IEEE ICTAI14-15, BigComp15, IMMM15, IEEE ICDM15 PhD. Forum, IEEE ICDM14. 

2. PC member: The 2016-2019 ACM SIGKDD Workshop on Causal Discovery (in conjunction with KDD'16, KDD17, KDD'18, KDD'19)

Software 

1. CausalFS: A Open-source C/C++ Toolbox for Causality-based Feature Selection.    https://github.com/kuiy/CausalFS 

2. LOFS: A Library of Online Streaming Feature Selection.

       https://github.com/kuiy/LOFS

3.  pyCausalFS: A Python Library of Causality-based Feature Selection for BN Structure       Learning and Classification.

       https://github.com/wt-hu/pyCausalFS

主要论著

*博士生,**硕士生

Journal Paper:

1.Zhaolong Lin*,Kui Yu,Hao Wang, Lei Li,and Xindong Wu. Using Feature  Selection for Local Causal Structure Learning. IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 10.1109/TETCI.2020.2978238, 2020.

2.Xinyu Wu**,Bingbing Jiang,Kui Yu,Chunyan Miao, and Huanhuan Chen. Accurate Markov Boundary Discovery for Causal Feature Selection.IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2019.2940509, 2019.

3.Kui Yu,Xianjie Guo, Lin Liu,Jiuyong Li,Hao Wang,Zhaolong Ling, Xindong Wu. Causality-based Feature Selection: Methods and Evaluations.ACM Computing Surveys, 53(5) (2020): 1-36.

3.Kui Yu,Lin Liu,Jiuyong Li, Wei Ding,and Thuc Le. Multi-Source Causal Feature Selection.IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 42(9): 2240-2256, 2020.

4.Kui Yu, Lin Liu, and Jiuyong Li. Learning Markov Blankets from Multiple Interventional Datasets. IEEE Transactions on Neural Networks and Learning Systems (TNNLS),31(6), 2005-2019, 2020. 

5.Zhaolong Lin*, Kui Yu, Hao Wang, Lin Liu, Wei Ding, and Xindong Wu. BAMB: A Balanced Markov Blanket Discovery Approach to Feature Selection. ACM Transactions on Intelligent Systems and Technology (TIST),10(5): 52:1-52:25 (2019). 

6.Kui Yu and Huanhuan Chen.Markov boundary-based outlier Mining.IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 30(4): 1259-1264 (2019) 

7.Kui Yu, Lin Liu, Jiuyong Li, and Huanhuan Chen. Mining Markov Blanket without Causal Sufficiency. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 29(12): 6333-6347 (2018). 

8.Kui Yu,Xindong Wu, Wei Ding, Yang Mu, and Hao Wang. Markov Blanket Feature Selection using Representative Sets. IEEE Transactions on Neural Networks and Learning Systems (TNNLS).28(11): 2775-2788, 2017. 

9.Kui Yu, Xindong Wu, Wei Ding, and Jian Pei. Scalable and Accurate Online Feature Selection for Big Data. ACM Transactions on Knowledge Discovery from Data (TKDD),11(2),1:39,2016. 

10.Kui Yu, Wei Ding, Dan A. Simovici, Hao Wang, Jian Pei, and Xindong Wu. Classification with Streaming Features: An Emerging Pattern Mining Approach. ACM Transactions on Knowledge Discovery from Data (TKDD), 9(4): 30:1-30:31 (2015).  

11.Kui Yu, Wei Ding, Hao Wang, and Xindong Wu. (2013) Bridging Causal Relevance and Pattern Discriminability: Mining Emerging Patterns from High-Dimensional Data. IEEE Transactions on Knowledge and Data Engineering (TKDE)25(12): 2721-2739.

12.Xindong Wu, Kui Yu, Wei Ding, Hao Wang, and Xingquan Zhu. (2013) Online Feature Selection with Streaming Features. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 35(5): 1178-1192. 

Conference Paper 

1.Debo Cheng*, Jiuyong Li, Lin Liu, Jixue Liu, Kui Yu and Thuc Duy Le. Causal query in observational data with hidden variables. The 24th European Conference on Artificial Intelligence (ECAI'20), Santiago de Compostela, Spain, June 8-12, 2020. 

2.Xinyu Wu**, Bingbing Jiang, Kui Yu, Huanhuan Chen, and Chunyan Miao. Multi-label Causal Feature Selection. The 34th AAAI Conference on Artificial Intelligence (AAAI'20), February 7-12, 6430-6437, New York, USA.

3.Bingbing Jiang*, Xinyu Wu**, Kui Yu, and Huanhuan Chen. Joint Semi-supervised Feature Selection and Classification through Bayesian Approach.The 33th AAAI Conference on Artificial Intelligence (AAAI'19), 3983-3990, January 27- February 1, 2019 , Honolulu, Hawaii, USA. 

4.Kui Yu, Dawei Wang, Wei Ding, David L. Small, Shafiqul Islam, Jian Pei, and Xindong Wu. Tornado Forecasting with Multiple Markov Boundaries.Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD 2015), 10-13 August, Sydney, Australia.

5.Kui Yu, Xindong Wu, Wei Ding, and Pei Jian. Towards Scalable and Accurate Online Feature Selection for Big Data. Proceedings of the 14th IEEE International Conference on Data Mining (ICDM 2014), Shenzhen,China, December 14-17, 2014. 

6.Kui Yu,Xindong Wu, Zan Zhang, Yang Mu, Hao Wang, and Wei Ding. Markov Blanket Feature Selection with Non-Faithful Data Distributions. Proceedings of the 13th IEEE International Conference on Data Mining (ICDM 2013), Dallas, Texas, December 7-10, 2013857-866.

7.Dawei Wang, Wei Ding, Kui Yu, Xindong Wu, Ping Chen, David L. Small, and Shafiqul Islam. Towards long-lead forecasting of extreme flood events: a data mining framework for precipitation cluster precursors identification. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2013), Chicago, IL, USA, August 11-14, 2013, 1285-1293.

8.Kui Yu, Wei Ding, Dan A. Simovici, and Xindong Wu. Mining Emerging Patterns by Streaming Feature Selection. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), Beijing, China, August 12-16, 2012, 60-68.

9.Kui Yu, Xindong Wu, Wei Ding, and Hao Wang. Causal Associative Classification. Proceedings of the 11th IEEE International Conference on Data Mining (ICDM 2011), Vancouver, Canada, December 11-14, 2011, 914-923.

10.Xindong Wu, Kui Yu, Hao Wang, and Wei Ding. Online Streaming Feature Selection. Proceedings of the 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, June 21-24, 2010, 1159-1166.

11.Kui Yu, Xindong Wu, Hao Wang, and Wei Ding. Causal Discovery from Streaming Features. Proceedings of the 10th IEEE International Conference on Data Mining (ICDM 2010), Sydney Australia, December 14-17, 2010, 1163-1168.

12.Kui Yu, Hao Wang, and Xindong Wu. A Parallel Algorithm for Learning Bayesian Networks. Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007) , Nanjing, China, May 22-25, 2007, 1055-1063.