基本信息
何清  男  博导  中国科学院计算技术研究所
电子邮件: heqing@ict.ac.cn
通信地址: 北京市海淀区科学院南路六号中科院计算技术研究所智能信息处理重点实验室
邮政编码: 100190

研究领域

机器学习、大数据挖掘等人工智能领域

招生信息

   
招生专业
081202-计算机软件与理论
081203-计算机应用技术
083500-软件工程
招生方向
机器学习、数据挖掘、人工智能
计算机技术
软件工程

教育背景

1997-09--2000-07   北京师范大学   博士
学历

1997年8月-2000年7月 北京师范大学 模糊数学与人工智能专业 博士毕业,获博士学位

学位
北京师范大学 19970801--20000730 博士 
郑州大学           19850801--19870730硕士
河北师范大学   19810801--19850730学士
出国学习工作
2001年11月俄罗斯圣彼得堡信息与自动化研究所合作交流,执行中俄政府间科技合作项目
2003年10月澳大利亚UniSA高级访问学者,执行中澳国际特别基金合作项目
2004年10月澳大利亚UTS, 中国科学院高级访问学者计划.

工作经历

       

2000年8月北京师范大学博士毕业后,进入中国科学院计算技术研究所做博士后工作,出站后留所工作,现任中国

科学院计算技术研究所研究员、博士生导师,中国科学院大学岗位教授,中国科学院智能信息处理重点实验室机器

学习与数据挖掘课题组负责人。兼任中国人工智能学会副秘书长,常务理事,机器学习专业委员会常务理事, 分布

智能与知识工程专业委员会副主任委员,《International Journal of Machine Learning and Cybernetics》

编委。

主要研究领域:机器学习与数据挖掘,基于云计算的大数据挖掘。

主要学术贡献:提出了基于超曲面的覆盖学习算法;提出极小样本集抽样方法与相关理论;提出了基于进化规划的

基于摄动的模糊聚类改进算法,解决了模糊聚类失真问题;证明了模糊集扩展原理在范畴论意义下的合理性;提出

概念语义空间用于知识管理;提出基于极限学习机的分类、聚类、回归、异常发现算法。

在机器学习与数据挖掘相关领域发表三百余篇论文,其中发表Nature子刊1篇,CCF A类47篇,CCF B

类43篇。SCI收录论文48篇,Google Scholar个人引用6105次,单篇最高引用871次。著作3部,其中学会技术发展

报告两部,教材一部。获得专利7项,软件著作权16项。

论文获奖情况:论文《Extreme Support Vector Machine Classifier》获数据挖掘顶级会议PAKDD 2018最有影响

力论文奖。论文《Exploiting Associations between Word Clusters and Document Classes for

Cross-domain Text Categorization》获数据挖掘顶级会议SDM 2010最佳论文提名。

论文《Collaborative Dual-PLSA: Mining Distinction and Commonality across Multiple Domains for 

Text Classification》获数据挖掘顶级会议CIKM 2010最佳论文提名。

论文《Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed 

Feedback》获数据挖掘顶级会议SIGIR 2021最佳短文提名。

2008年底,开发完成了我国最早的基于云计算的并行大数据挖掘平台用于TB级实际数据的挖掘,实现了高性能、

低成本的数据挖掘,通过这次创新,使我国获得了自主知识产权的基于云计算的数据挖掘技术。受大会邀请在

第二、三、六届中国云计算大会上作了技术报告。

何清先后主持完成多个有关数据挖掘的国家自然科学基金项目和863项目,承担完成或参加完成的多项国家自然科学

基金项目被评为优或特优。承担完成了两项863项目获得好评。提出了一系列有效的数据挖掘算法和多个并行机器学习

算法。组织开发实现了四十多个并行机器学习算法,所开发的多个数据挖掘软件获得了软件著作权,并实际应用到

电信、电力、信息安全、环保、保险行业的数十家企业,为企业带来了可观的经济效益和社会效益。获得授权专利

10余项,曾获PAKDD2018国际会议最有影响力论文奖,2015年吴文俊人工智能科学技术创新奖二等奖,2006年北京市

科学技术奖。


工作简历
2008-10~现在, 中国科学院计算技术研究所, 研究员
2007-06~现在, 中国科学院计算技术研究所, 博士生导师
2006-05~现在, 中国科学院研究生院, 教授
2002-10~2007-09,中国科学院计算技术研究所, 硕士生导师
2002-08~2008-09,中国科学院计算技术研究所, 副研究员
2000-08~2002-07,中国科学院计算技术研究所, 博士后
社会兼职
2018-05-12-今,中国人工智能学会知识工程与分布智能专业委员会, 副主任委员
2014-04-25-今,中国电子学会大数据专家委员会, 委员
2012-05-31-今,中国通信学会大数据专家委员会, 委员
2009-06-01-今,中国电子学会云计算专家委员会, 委员
2003-08-01-今,中国人工智能学会, 副秘书长

教授课程

人工智能基础
认知计算
本科生毕业设计(计算机科学与技术)
云计算与大数据管理系列讲座
模糊数学及计算机应用

专利与软著

发明专利



( 1 ) 一种采用决策树的数据分类方法和系统, 发明, 2011, 第 2 作者, 专利号: 201110143821.7

( 2 ) 一种确定数据样本类别的方法及其系统 , 发明, 2010, 第 1 作者, 专利号: 200910077994.6

( 3 ) 一种关联规则挖掘方法及其系统 , 发明, 2010, 第 1 作者, 专利号: 200910077996.5

( 4 ) 一种数据挖掘系统中数据聚类的方法、系统及装置, 发明, 2011, 第 1 作者, 专利号: 201010102976.1

( 5 ) 数据关联规则挖掘实现方法与系统, 发明, 2011, 第 1 作者, 专利号: 200910091865.2

( 6 ) 聚类实现方法及系统 , 发明, 2011, 第 1 作者, 专利号: 200910091864.8

( 7 ) 聚类实现方法及系统, 发明, 2011, 第 1 作者, 专利号: 200910091866.7

( 8 ) 一种基于MapReduce的分布式垂直交叉网络爬虫系统, 发明, 2013, 第 2 作者, 专利号: 201310146080.7

( 9 ) 一种用于大数据的基于超曲面的分类方法及系统, 发明, 2013, 第 1 作者, 专利号: 201310926826.2,

( 10 ) 一种面向大数据的分布式主题发现方法及系统, 发明, 2013, 第 2 作者, 专利号: 201310526790.2

( 11 ) 描述型多维度复杂事件序列的并行频繁情节挖掘方法与系统, 发明, 2017, 第 5 作者, 专利号: 201610524750.8

( 12 ) 基于Spark的高效并行自动编码机及系统, 发明, 2016, 第 5 作者, 专利号: 2016101470075

( 13 ) 一种用于大数据的并行半定义分类方法与系统, 发明, 2016, 第 5 作者, 专利号: 201610570978.0

(14)一种多标记学习方法,发明,2018,第 5 作者,申请号:201810062864.4


软件著作权



1.Web挖掘云服务平台[简称WMCS]V1.0,中国2013SR027808 

2.基于云计算的Web 挖掘系统[简称CWMS]V1.0,中国2012SR119823 

3.数据挖掘云服务平台[简称COMS]V1.0,中国 2010SR060647 

4.并行分布式数据挖掘软件系统[简称PDMiner]V1.0,中国 2010SR005800 

5.迁移学习系统[简称TLS] V1.0,中国 2015SR195765

6.城市人口全生命周期数据挖掘系统[简称UWDMS]V1.0,中国 2015SR071535

7.基于几何超曲面的分类系统[简称HSC]V1.0,中国 2008SR02159

8.Web 智能信息处理软件[简称GHunt] V2.0,中国 2008SR35473

9.Web 智能信息处理软件[简称GHunt] V1.0,中国 2004SR07403

10.多策略数据挖掘平台[简称MSMiner] V1.0,中国 2003SR6886

11.潜在离网用户预测系统POSUPS V1.0,中国 2018SR045680




奖励信息
(1) 华为奖教金, 一等奖, 研究所(学校), 2020
(2) 本科优秀课程奖, 一等奖, 研究所(学校), 2020
(3) PAKDD2018国际会议最有影响论文奖, 一等奖, 其他, 2018
(4) 吴文俊人工智能科学技术创新奖——大数据挖掘算法与云服务, 二等奖, 省级, 2015
(5) 北京市科学技术奖——主体网格智能平台, 三等奖, 省级, 2006

合作情况

   
项目协作单位

美国Rutgers, the State University of New Jersey
俄罗斯圣彼得堡信息与自动化研究所
澳大利亚悉尼技术大学
中国移动通信有限公司研究院

指导学生

已指导学生

赵秀荣  硕士研究生  081202-计算机软件与理论  

谭庆  博士研究生  081202-计算机软件与理论  

庄福振  博士研究生  081202-计算机软件与理论  

赵卫中  博士研究生  081202-计算机软件与理论  

李金成  硕士研究生  081202-计算机软件与理论  

马旭东  硕士研究生  081202-计算机软件与理论  

李宁  博士研究生  081202-计算机软件与理论  

尚田丰  博士研究生  081202-计算机软件与理论  

罗文娟  博士研究生  081202-计算机软件与理论  

李婷婷  硕士研究生  081203-计算机应用技术  

王群  硕士研究生  081202-计算机软件与理论  

董智  硕士研究生  081202-计算机软件与理论  

马云龙  硕士研究生  430112-计算机技术  

韩硕  硕士研究生  081202-计算机软件与理论  

余文超  硕士研究生  081203-计算机应用技术  

杜长营  博士研究生  081202-计算机软件与理论  

金鑫  博士研究生  081202-计算机软件与理论  

王浩成  博士研究生  081202-计算机软件与理论  

敖翔  博士研究生  081202-计算机软件与理论  

周干斌  博士研究生  081202-计算机软件与理论  

吴新宇  硕士研究生  081202-计算机软件与理论  

程晓虎  硕士研究生  081202-计算机软件与理论  

左罗  硕士研究生  081202-计算机软件与理论  

何佳  博士研究生  081202-计算机软件与理论  

黄明  硕士研究生  081202-计算机软件与理论  

闫肃  硕士研究生  081202-计算机软件与理论  

罗丹  硕士研究生  085212-软件工程  

周英敏  硕士研究生  081202-计算机软件与理论  

张钊  博士研究生  081202-计算机软件与理论  

陈敬伍  硕士研究生  081202-计算机软件与理论  

潘斐阳  博士研究生  081202-计算机软件与理论  

奚冬博  硕士研究生  081202-计算机软件与理论  

罗玲  硕士研究生  081202-计算机软件与理论  

贾海  硕士研究生  025200-应用统计  

刘秋阁  硕士研究生  081202-计算机软件与理论  

现指导学生

柳阳  博士研究生  081202-计算机软件与理论  

李硕凯  博士研究生  081202-计算机软件与理论  

李昊明  硕士研究生  081202-计算机软件与理论  

王天鑫  硕士研究生  081202-计算机软件与理论  

于朔  博士研究生  081202-计算机软件与理论  

黄艨靼  硕士研究生  081202-计算机软件与理论  

汪润川  硕士研究生  081202-计算机软件与理论  

董临风  硕士研究生  081202-计算机软件与理论  

孙莹  博士研究生  081202-计算机软件与理论  

张函玉  博士研究生  081202-计算机软件与理论  

吴贻清  硕士研究生  081202-计算机软件与理论  

张富威  硕士研究生  085400-电子信息  

薛泓彦  博士研究生  081202-计算机软件与理论  

刘骐鸣  硕士研究生  081202-计算机软件与理论  

周子贤  硕士研究生  081202-计算机软件与理论  

科研活动

主持或参加完成的科研项目:

1.  国家自然科学基金面上项目:深度与宽度自适应的深度极端学习机模型研究, No.61573335 201601月至 2019 12月,负责人

2.  国家自然科学基金一年期滚动项目NO.91846113,项目名称:一年期滚动项目——证券管理决策大数据挖掘云服务平台研究,2019.1.1-2019.1.231

3.  国家自然科学基金大数据重大计划培育项目:证券管理决策大数据挖掘云服务平台研究” No. 9154612220161 月至201812月,负责人,圆满完成,被评为优。。     

4.  国家自然科学基金面上项目领域适应性问题相关学习算法与理论研究No. 61175052,2012.1-2015.12,负责人,圆满完成,顺利结题。

5.  国家自然科学基金重点项目“WEB 搜索与挖掘的新理论与方法No. 60933004,2010.1-2013.12, 合作方负责人, 结题被评为优。

6.  国家自然科学基金面上项目:分布式计算环境下的并行数据挖掘算法与理论研究,2010.12012.12,负责人,圆满完成,顺利结题。

7.  国家自然科学基金面上项目基于超曲面的覆盖分类算法与理论研究” No. 606750102007.1-2009.12 负责人,被评为优

8.  国家自然科学基金概念语义空间及其应用”No.60173017,负责人:何清,2001.1-2002.12,被评为优

9.  国家八六三高技术研究发展计划项目开放环境下海量web数据提取、集成、分析和管理系统平台与应用所属课题海量web数据内容管理、分析挖掘技 术与大型示范应用” No.2012AA011003, 2012.1-2014.12。子课题负责人,结题获得好评。

10. 国家八六三高技术研究发展计划基于感知机理的智能信息处理技术”No2006AA01Z128, 负责人,2006.9-2008.12,结题获得好评。

11. 国家八六三高技术研究发展计划自主计算的理论和技术研究”No2003AA115220, 负责人, 2003.7-2005.10,结题获得好评。

12. 973项目课题非结构化信息(图像)的内容理解与语义表征”No. 2007CB3110042007.7-2012.7,骨干,项目结题被评为优。

科研项目
( 1 ) 智能问答关键技术研究及其在体育领域的应用实现, 主持, 国家级, 2017-10--2021-09
( 2 ) 大数据分析核心算法及其理论分析-大数据分类算法, 主持, 国家级, 2019-01--2022-12
( 3 ) 国家重点研发计划项目:大数据分析的基础理论和技术方法(2018YFB1004300)课题:多源不确定数据挖掘方法与技术, 参与, 国家级, 2018-01--2021-04
( 4 ) 智能博弈算法研究, 主持, 国家级, 2019-08--2021-12
( 5 ) 开放环境下的基于智能芯片的迁移学习算法研究, 参与, 国家级, 2020-11--2021-11
发表和录用论文情况



一、会议论文

[1]. Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, Qing He: Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection. AAAI 2021,virtually February 2-9, 2021.

[2]. Feiyang Pan, Haoming Li, Xiang Ao, Wei Wang, Yanrong Kang, Ao Tan, Qing He.  GuideBoot: Guided Bootstrap for Deep Contextual Bandits in Online Advertising, WWW21,April 12-16, 2021,Ljubljana,Slovenia

[3]. Guoxin Yu, Xiang Ao, Ling Luo, Min Yang, Xiaofei Sun, Jiwei Li and Qing He. Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis. ACL 2021, findings, virtually August 1-6, 2021, Bangkok, Thailand.

[4]. Haoming Li, Feiyang Pan, Xiang Ao, Zhao Yang, Min Lu, Junwei Pan, Dapeng Liu, Lei Xiao, Qing He. Follow the Prophet: Accurate Online Conversion Rate Prediction In the Face of Delayed Feedback,SIGIR’21,Online,July 11-15, 2021 

[5]. Qingping Yang, Yixuan Cao, Hongwei Li, Ping Luo. Numerical formula recognition from tables. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021.

[6]. Rongyu Cao, Hongwei Li, Ganbin Zhou, Ping Luo. Towards Document Panoptic Segmentation with Pinpoint Accuracy: Method and Evaluation, in J. Llad´os et al. (Eds.): ICDAR 2021, LNCS 12822, pp. 3–18, 2021.

[7]. Rongyu Cao, Ping Luo. 2021. Extracting Zero-shot Structured Information from Form-like Documents: Pretraining with Keys and Triggers. AAAI 2021,virtually February 2-9, 2021.

[8]. Runchuan Wang, Zhao Zhang, Fuzhen Zhuang, Dehong Gao, Yi Wei, Qing He, Adversarial Domain Adaptation for Cross-lingual Information Retrieval with Multilingual BERT, CIKM’21, November 1–5, 2021, Virtual Event, Australia.

[9]. Tianxin Wang,Fuzhen Zhuang,zhiqiang Zhang,Daixin Wang, Jun Zhou,Qing He. Low-dimensional Alignment for Cross-Domain Recommendation, CIKM’21, November 1–5, 2021, Virtual Event, Australia.

[10]. Xiang Ao, Xiting Wang,Ling Luo, Ying Qiao, Qing He and Xing Xie. PENS: A Dataset and Generic Framework for Personalized News Headline Generation , ACL-IJCNLP 2021 main conference,82-92

[11]. Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang and Qing He. Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection, WWW21,April 12-16, 2021,Ljubljana,Slovenia

[12]. Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Qing He and Hui Xiong. Cost-Effective and Interpretable Job Skill Recommendation with Deep Reinforcement Learning, WWW21,April 12-16, 2021,Ljubljana, Slovenia

[13]. Ying Sun, Hengshu Zhu;_, Chuan Qin, Fuzhen Zhuang, Qing He, Hui Xiong. Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation accepted for a poster presentation at NeurIPS 2021.

[14]. Yixuan Cao, Feng Hong, Hongwei Li, Ping Luo: A Bottom-Up DAG Structure Extraction Model for Math Word Problems. AAAI 2021,virtually February 2-9, 2021. 

[15]. Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang, Ruobing Xie, Dongbo Xi, Xu Zhang, Leyu Lin and Qing He. Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users,SIGIR’21,Online,July 11-15, 2021

[16]. Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang, Kaikai Ge, Ying Sun, Xu Zhang, Leyu Lin and Juan Cao. Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks, SIGIR ’21, July 11–15, 2021, Virtual Event, Canada

[17]. Yongchun Zhu, Yudan Liu, Ruobing Xie, Fuzhen Zhuang, Xiaobo Hao, Kaikai Ge, Xu Zhang, Leyu Lin and Juan Cao. Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising,KDD’21,Online,August 14-18, 2021

[18]. Zhengqi Xu, Yixuan Cao, Rongyu Cao, Guoxiang Li. Xuangqiang Liu, Yan Pang, Yangbin Wang, Tianfer Zhang, Allie Cheung, Matten Tan, Lukas Petrikas, Ping Luo. Jura Towards Automatic Compliance Assessment, CIKM’21, November 1–5, 2021, Virtual Event, Australia.

[19]. Dongbo Xi, Fuzhen Zhuang, Bowen Song, Yongchun Zhu, Shuai Chen, Dan Hong, Tao Chen, Xi Gu, Qing He. Neural Hierarchical Factorization Machines for User's Event Sequence Analysis. SIGIR20 short paper, July 25-30, 2020, Xi'an, China. 

[20]. Dongbo Xi, Fuzhen Zhuang, Ganbin Zhou, Xiaohu Cheng, Fen Lin, Qing He. Domain Adaptation with Category Attention Network for Deep Sentiment Analysis. WWW’20, April 20–24, 2020, Taipei,pp.: 3133-3139.

[21]. Feiyang Pan, Xiang Ao, Pingzhong Tang, Min Lu, Dapeng Liu, Lei Xiao and Qing He. Field-aware calibration: a simple and empirically strong method for reliable probabilistic predictions. WWW’20, April 20–24, 2020, Taipei

[22]. Qiwei Zhong,Yang Liu,Xiang Ao,Binbin Hu,Jinghua Feng,Jiayu Tang,Qing He. Financial Defaulter Detection on Online Credit Payment via Multi-view Attributed Heterogeneous Information Network,WWW’20, April 20–24, 2020, Taipei

[23]. Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Gu Xi, and Qing He. Modeling Users’ Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection. Proceedings of the Web Conference 2020. pp.: 928-938. WWW’20, April 20–24, 2020, Taipei

[24]. Zhao Zhang, Fuzhen Zhuang*, Hengshu Zhu, Zhiping Shi, Hui Xiong, Qing He, Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion. AAAI 2020, Feb.7-12,Newyork USA.

[25]. Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, Jingjing Gu, Hui Xiong, Qing He. Modeling Users’ Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection, WWW’20, April 20–24, 2020, Taipei

[26]. Dongbo Xi, Fuzhen Zhuang*, Yanchi Liu, Jingjing Gu, Hui Xiong, Qing He: Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification. AAAI 2019.

[27]. Feiyang Pan, Qingpeng Cai, An-Xiang Zeng, Chun-Xiang Pan, Qing Da, Hualin He, Qing He, Pingzhong Tang. Policy Optimization with Model-based Explorations. AAAI 2019.

[28]. Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, Qing He. Warm Up Cold-start Advertisements : Improving  CTR Predictions via Learning to Learn ID Embeddings. To appear in the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019). 

[29]. Pan feiyang, Cai, Qi,Tang, Pingzhong, Zhuang, Fuzhen., He, Qing. Policy gradients for contextual recommendations, WWW2019

[30]. Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Xin Song, Qing He, Hui Xiong. A Structure-Aware Convolutional Neural Network Approach, KDD2019

[31]. Ling Luo, Xiang Ao,Yan Song,Jinyao Li,Xiaopeng Yang,Qing He,Dong Yu, Unsupervised Neural Aspect Extraction with Sememes,IJCAI2019

[32]. Ying Sun, Hengshu Zhu, Fuzhen Zhuang, Jingjing Gu and Qing He Exploring the Urban Region-of-Interest through the Analysis of Online Map Search Queries,KDD2018

[33]. Ganbin Zhou, Ping Luo, Rongyu Cao, Yijun Xiao, Fen Lin, Bo Chen, Qing He. Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation,The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) ,February 2–7, 2018,New Orleans, Lousiana, USA

[34]. Ganbin Zhou, Ping Luo, Yijun Xiao, Fen Lin, Bo Chen, Qing He. Elastic Responding Machine for Dialog Generation with Mechanism Dynamically Selecting,The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) ,February 2–7, 2018,New Orleans, Lousiana, USA

[35]. Jingwu Chen, Fuzhen Zhuang, Xin Hong, Xiang Ao, Xing Xie and Qing He: Attention-driven Factor Model for Explainable Personalized Recommendation. SIGIR 2018

[36]. Xiang Ao, Yang Liu, Zhen Huang, Luo Zuo, Qing He. Free-rider Episode Screening via Dual Partition Model. The 23rd International Conference on Database Systems for Advanced Applications (DASFAA), 2018.

[37]. Ling Luo, Xiang Ao, Feiyang Pan, Tong Zhao, Ningzi Yu, Qing He. Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention. The 27th International Joint Conference on Artificial Intelligence (IJCAI2018).

[38]. Jia He, Changying Du, Changde Du, Fuzhen Zhuang, Qing He, Guoping Long.Nonlinear Maximum Margin Multi-view Learning with Adaptive Kernel, IJCAI17

[39]. Ganbin Zhou, Ping Luo, Rongyu Cao, Fen Lin, Bo Chen, Qing He. Mechanism-Aware Neural Machine for Dialogue Response Generation,AAAI2017

[40]. Xiang Ao, Ping Luo, Jin Wang, Fuzhen Zhuang, Qing He. Mining Precise-positioning Episode Rules from Event Sequences,ICDE2017

[41]. Fuzhen Zhuang, Jing Zheng, Chuan Shi and Qing He.Transfer Collaborative Filtering from Multiple Sources via Consensus Regularization,WSDM2017

[42]. Qing He, Yunlong Ma, Qun Wang, Fuzheng Zhuang, Zhongzhi Shi. Parallel Outlier Detection Using KD-Tree Based on MapReduce, IEEE CloudCom 2011,Washington, DC, USA, 4-9 July, 2011

[43]. Qing He, Zhongzhi Shi, Lian Ren.The Classification Method Based on Hyper Surface,2002 International Joint Conference on Neural Networks,2002.5:1499-1503, Honolulu, Hawaii,USA, May 12-17, 2002

[44]. Qing He, Xiurong Zhao, Sulan Zhang. Multi-modal services for web information collection based on multi-agent techniques, Lecture Notes in Computer Science, v 4088 LNAI, Agent Computing and Multi-Agent Systems: 9th Pacific Rim International Workshop on Multi-Agents, PRIMA 2006, p 129-137, Guilin, China, in August 2006

[45]. Jia He, Changying Du, Fuzhen Zhuang,Yin Xin, Qing He*, Guoping Long. Online Bayesian Max-margin Subspace Multi-view Learning, IJCAI-16,July 9–15, 2016, New York

[46]. Ping Luo, Ganbin Zhou, Qing He*. Browsing Regularities in Hedonic Content Systems: the More the Merrier? IJCAI-16,July 9–15, 2016, New York

[47]. Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He*. Online Frequent Episode Mining, ICDE 2015 : International Conference on Data Engineering (ICDE15), Seoul, Korea, April 13-17, 2015

[48]. Changying Du, Shandian Zhe, Fuzhen Zhuang, Alan Qi, Qing He*, Zhongzhi Shi. Bayesian Maximum Margin Principal Component Analysis, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15),Austin, Texas, USA, January 25–30, 2015,

[49]. Xinyu Wu, Ping Luo, Qing He*, Tianshu Feng. Festival, Date and Limit Line: Predicting Vehicle Accident Rate in Beijing, SDM15, British Columbia, Canada, April 30-May 2

[50]. Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He*, Zhongzhi Shi.Discovering and learning sensational episodes of news events. The 23rd international conference on World Wide Web, WWW2014, Seoul, Korea, April 7-11,

[51]. Wenjuan Luo, Fuzhen Zhuang, Xiaohu Cheng, Qing He*, Zhongzhi Shi. Ratable Aspects over Sentiments: Predicting Ratings for Unrated Reviews, IEEE International Conference on Data Mining (ICDM 2014), Shenzhen, China,December 14-17, 2014

[52]. Xin Jin, Fuzhen Zhuang, Hui Xiong, Changying Du, Ping Luo and Qing He*. Multi-task Multi-view Learning for Heterogeneous Tasks, CIKM’14, November 03–07, 2014, Shanghai, China

[53]. Fuzhen Zhuang, Xiaohu Cheng, Sinno Jialin Pan, Wenchao Yu, Qing He*, Zhongzhi Shi. Transfer Learning with Multiple Sources via Consensus Regularized Autoencoders, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML14/PKDD14), Nancy, France, September 15th to 19th, 2014.

[54]. Changying Du, Jia He, Fuzhen Zhuang, Yuan Qi, Qing He*. Nonparametric Bayesian Multi-Task Large-margin Classification, 21st European Conference on Artificial intelligence (ECAI14), Prague, Czech, 18-22 Aug. 2014.

[55]. Shuo Han, Fuzhen Zhuang, Qing He*, Zhongzhi Shi. Balanced Seed Selection for Budgeted Influence Maximization in Social Networks, PAKDD 2014: Pacific-Asia Conference on Knowledge Discovery and Data Mining , 2014-05-13, Tainan, Taiwan, China

[56]. Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He*, Zhongzhi Shi. Discovering and learning sensational episodes of news events. The 23rd international conference on World Wide Web, WWW2014, Seoul, Korea,April 7-11

[57]. Wenjuan Luo, Fuzhen Zhuang, Xiaohu Cheng, Qing He*, Zhongzhi Shi. Ratable Aspects over Sentiments: Predicting Ratings for Unrated Reviews, IEEE International Conference on Data Mining (ICDM 2014), Shenzhen, China / December 14-17, 2014

[58]. Xin Jin, Fuzhen Zhuang, Hui Xiong, Changying Du, Ping Luo and Qing He*. Multi-task Multi-view Learning for Heterogeneous Tasks, CIKM’14, Shanghai, China, November 03–07, 2014

[59]. Shuo Han, Fuzhen Zhuang, Qing He*, Zhongzhi Shi. Balanced Seed Selection for Budgeted Influence Maximization in Social Networks, PAKDD 2014 : Pacific-Asia Conference on Knowledge Discovery and Data Mining, Tainan, Taiwan, China,2014-05-13

[60]. Fuzhen Zhuang, Ping Luo, Changying Du, Qing He*, Zhongzhi Shi. Triplex Transfer Learning: Exploiting both Shared and Distinct Concepts for Text Classification, WSDM’13, Rome, Italy, February 4–8, 2013

[61]. Fuzhen Zhuang, Ping Luo, Peifeng Yin, Qing He*, Zhongzhi Shi. Concept Learning for Cross-domain Text Classification: a General Probabilistic Framework, 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013). Beijing, China, August 3-9, 2013

[62]. Tianfeng Shang, Qing He*, Fuzhen Zhuang and Zhongzhi Shi. A New Similarity Measure Based on Preference Sequence for Collaborative Filtering. Web Technologies and Applications. 15th Asia-Pacific Web Conference, APWeb 2013,Sydney, NSW, Australia, 4-6 April 2013

[63]. Tianfeng Shang, Qing He*, Fuzhen Zhuang, Zhongzhi Shi. Extreme Learning Machine Combining Matrix Factorization for Collaborative Filtering. IEEE The 2013 International Joint Conference on Neural Networks, IJCNN 2013, Dallas, TX, USA, August 4-9, 2013.

[64]. Xin Jin, Fuzhen Zhuang, Shuhui Wang, Qing He*, and Zhongzhi Shi. Shared Structure Learning for Multiple Tasks with Multiple Views, ECML/PKDD13, Prague, September 23-27, 2013

[65]. Wenchao Yu, Guangxiang Zeng, Ping Luo, Fuzhen Zhuang,Qing He*, and Zhongzhi Shi. Embedding with Autoencoder Regularization, ECML/PKDD13, Prague,September 23-27, 2013

[66]. Changying Du, Fuzhen Zhuang, Qing He* and Zhongzhi Shi. Multi-Task Semi-Supervised Semantic Feature Learning for Classification, ICDM2012,pp. 191-200, Brussels, Belgium, 2012 (12/10-12/13)

[67]. Wenjuan Luo Fuzhen Zhuang, Qing He*, and Zhongzhi Shi. Quad-tuple PLSA: Incorporating Entity and Its Rating in Aspect Identification, The 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), PAKDD 2012, pp. 392–404, Kuala Lumpur, Malaysia, 29 May - 1 June,2012

[68]. Xudong Ma, Ping Luo, FuzhenZhuang, Qing He*, Zhongzhi Shi and ZhiyongShen. Combining Supervised and Unsupervised Models via Unconstrained Probabilistic Embedding, Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI 11,pp.1396-1401C,Barcelona in July 2011

[69]. Fuzhen Zhuang, Ping Luo, Hui Xiong, Qing He*. Yuhong Xiong. Exploiting Associations between Word Clusters and Document Classes for Cross-domain Text Categorization, 2010 SIAM International Conference on Data Mining (SDM'2010), pp.13-24, Columbus, Ohio, April 19, 2010(EI,被大会推荐的十二篇最佳论文提名之一)

[70]. Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He*, Yuhong Xiong, and Zhongzhi Shi. D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-Defined Classification, accepted as a regular paper at the IEEE International Conference on Data Mining (ICDM 2010) to be held in Sydney Australia, December 14-17,2010, pp.709-718, (EI )

[71]. Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He*, Yuhong Xiong, Zhongzhi Shi1, Hui Xiong. Collaborative Dual-PLSA: Mining Distinction and Commonality across Multiple Domains for Classification, The 19th ACM International Conference on Information and Knowledge Management( CIKM’10), October 26-30, 2010, Toronto, Canada. (获得八篇最佳论文提名之一, 并获得Student Travel Awards)

[72]. Qing Tan, Qing He*, Zhongzhi Shi. Nonparametric Curve Extraction Based on Ant Colony System, Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10), pp.599-604, Atlanta, USA, July 10-15, 2010

[73]. Weizhong Zhao, Huifang Ma, Qing He.Parallel k-means clustering based on mapreduce, Cloud Computing, 2009

[74]. Ping Luo, Fuzhen Zhuang, Hui Xiong, Yuhong Xiong, Qing He*. Transfer Learning from Multiple Source Domains via Consensus Regularization, full paper in CIKM 2008 ,  Napa Valley, California October 26-30, 2008 (EI)

[75]. Qiuge Liu, Qing He*, Zhongzhi Shi. Extreme Support Vector Machine Classify, Lecture Notes in Computer Science, v 5012 LNAI, Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Proceedings, 2008, p 222-233,Osaka,Japan,May 20-23,2008(EI)

[76]. Luo, Ping; Lu, Kevin; He, Qing*; Shi, Zhongzhi. A heterogeneous computing system for data mining workflows, Lecture Notes in Computer Science, v 4042 LNCS, Flexible and Efficient Information Handling - 23rd British National Conference on Databases, BNCOD 23, Proceedings, 2006, p 177-189, Belfast, Northern Ireland, UK, July 18-20, 2006

[77]. Zheng, Zheng; He, Qing*; Shi, Zhongzhi. Granule sets based bilevel decision model, Lecture Notes in Computer Science, v 4062, Rough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings, 2006, p 530-537, Chongqing, China, July 24-26, 2006 

[78]. Zhao, Xiu-Rong; He, Qing*; Shi, Zhong-Zhi. HyperSurface Classifiers ensemble for high dimensional data sets, Lecture Notes in Computer Science, v 3971, Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, p 1299-1304, Chengdu, China, May 28 - June 1, 2006

[79]. Ping Luo, Qing He*, Rui Huang, Fen Lin, Zhongzhi Shi. Execution Engine of Meta-learning System for KDD in Multi-agent Environment. Lecture Notes in Computer Science. Springer-Verlag, Volume 3505 / 2005, 149-160. AIS-ADM 2005, St. Petersburg, Russia, June 6-8, 2005

[80]. Ping Luo, Su Yan, Zhiqiang Liu, Zhiyong Shen, Shengwen Yang, Qing He. From Online Behaviors to Offline Retailing, the ACM KDD 2016 Conference as a full presentation(CCF A)

二、期刊论文

[81]. Feiyang Pan,Shuokai Li,Xiang Ao,Qing He. Relation Reconstructive Binarization of Word Embeddings, Frontiers of Computer Science, 2022, 16(2): 162307Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchuan Zhu, Hengshu Zhu, Hui Xiong, Qing He, A Comprehensive Survey on Transfer Learning,  PROCEEDINGS OF THE IEEE, Vol. 109, No. 1, January 2021, pp.43-76

[82]. Jingwu Chen, Fuzhen Zhuang, Tianxin Wang, Leyu Lin, Feng Xia, Lihuan Du, Qing He. Follow the Title then Read the Article: Click-guide Network for Dwell Time Prediction, IEEE Transactions on Knowledge and Data Engineering, VOL. 33, NO. 7, JULY 2021, pp.2903-2913

[83]. Shuokai Li, Xiang Ao, Feiyang Pan, Qing He. Learning Policy Scheduling for Text Augmentation, Neural Networks,145(2022):121-127

[84]. Xiang Ao, Yang Liu, Zidi Qin, Yi Sun and Qing He. Temporal High-order Proximity Aware Behavior Analysis on Ethereum. World Wide Web ,(2021) 24:1565–1585.

[85]. Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Qi Zhang, Qing He, and Hui Xiong. Market-oriented Job Skill Valuation with Cooperative Composition Neural Network,NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-22215-y.

[86]. Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, Qing He. Deep Subdomain Adaptation Network for Image Classification. IEEE Transactions on Neural Networks and Learning Systems, VOL. 32, NO. 4, APRIL 2021,pp. 1713-1722

[87]. Zhao Zhang, Fuzhen Zhuang, Meng Qu, Zheng-Yu Niu, Hui Xiong, Qing He, Knowledge graph embedding with shared latent semantic units, Neural Networks 139 (2021) 140–148

[88]. Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, Hengshu Zhu, Pengpeng Zhao, Chang Tan, Qing He: Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification,Neural Networks 132 (2020) 75–83.

[89]. Fuzhen Zhuang, Yingmin Zhou, Haochao Ying, Fuzheng Zhang, Xiang Ao, Xing Xie, Qing He, Hui Xiong: Sequential Recommendation via Cross-domain Novelty Seeking Trait Mining. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY ,Vol.35, ‏No.‏ 2, SI: ‏ 305-319, MAR 2020

[90]. Jia He, Changying Du,Fuzhen Zhuang, Xin Yin, Qing He. Guoping Long. Online Bayesian Max-margin Subspace Learning for Multi-view Classification and Regression, Machine Learning, (2020) 109:219–249.

[91]. Jia He, Fuzhen Zhuang, Yanchi Liu, Qing He, Fen Lin:

Bayesian dual neural networks for recommendation. Frontiers Comput. Sci. 13(6): 1255-1265 (2019)

[92]. Ming Huang, Fuzhen Zhuang, Xiao Zhang, Xiang Ao, Zhengyu Niu, Min-Ling Zhang, Qing He. Supervised representation learning for multi-label classification. Machine Learning 108(5): 747-763 (2019)

[93]. Thapana Boonchooa, Xiang Ao, Yang Liu, Weizhong Zhao, Fuzhen Zhuang, Qing He. Grid-based DBSCAN: Indexing and Inference. Pattern Recognition (PR), 90 : 271-284, 2019

[94]. Thapana Boonchoo, Xiang Ao , Qing He: Multi-Aspect Embedding for Attribute-Aware Trajectories. Symmetry 11(9): 1149 (2019)

[95]. Xiang Ao, Haoran Shi, Jin Wang, Luo Zuo, Hongwei Li, Qing He:Large-Scale Frequent Episode Mining from Complex Event Sequences with Hierarchies. ACM TIST 10(4): 36:1-36:26 (2019) 

[96]. Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Jingwu Chen, Zhiping Shi, Wenjuan Wu, Qing He: Multi-Representation Adaptation Network for Cross-domain Image Classification. Neural Networks 119:214-221 (2019).

[97]. Zhao Zhang, Fuzhen Zhuang, Xuebing Li, Zhengyu Niu, Jia He, Qing He, Hui Xiong: Knowledge Triple Mining via Multi-Task Learning. Information Systems, Information Systems 80 (2019): 64–75

[98]. Fuzhen Zhuang, Jing Zheng, Jingwu Chen, Xiangliang Zhang, Chuan Shi, Qing He. Transfer collaborative filtering from multiple sources via consensus regularization. Neural Networks 108: 287-295 (2018)

[99]. Fuzhen Zhuang, Xuebing Li, Xin Jin, Dapeng Zhang, Lirong Qiu, Qing He. Semantic Feature Learning for Heterogeneous Multitask Classification via Non-Negative Matrix Factorization. IEEE Trans. Cybernetics 48(8): 2284-2293 (2018)

[100]. X. Ao, P. Luo, J. Wang, F. Zhuang and Q. He, "Mining Precise-Positioning Episode Rules from Event Sequences," in IEEE Transactions on Knowledge & Data Engineering, vol. 30, no. 3, pp. 530-543, 2018.

[101]. Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He. Discovering and learning sensational episodes of news events[J]. Information Systems,Volume 78, November 2018, Pages 68-80

[102]. Zhou Ganbin Luo Ping He Qing.Predicting Compositional Time Series via Autoregressive Dirichlet Estimation[J]. Science China Information Sciences, 2018, 61(9):098-106.

[103]. Fuzhen Zhuang,Zhiqiang Zhang,Mingda Qiana,Chuan Shi,Xing Xie,Qing He. Representation learning via Dual-Autoencoder for recommendation,Neural Networks,Volume 90, June 2017, Pages 83-89

[104]. Fuzhen Zhuang, Xiaohu Cheng, Ping Luo, Sinno Jialin Pan, Qing He: Supervised Representation Learning with Double Encoding-layer Autoencoder for Transfer Learning. ACM Transactions on Intelligent Systems and Technology (TIST)     ISSN:2157-6904  Volume:9  Issue:2  Page:1-17  .

[105]. Haocheng Wang, Fuzhen Zhuang, Xin Jin, Xiang Ao, and Qing He. Bag of little bootstraps on features for enhancing classification performance. Intelligent Data Analysis 20 (2016) 1085–1099. 

[106]. Qing He, Haocheng Wang, Fuzhen Zhuang, Tianfeng Shang, Zhongzhi Shi. Parallel sampling from big data with uncertainty distribution, Fuzzy Sets and Systems 258 (2015) 117–133

[107]. Jie Lu, Zheng Zheng, Guangquan Zhang, Qing He and Zhongzhi Shi. A new solution algorithm for solving rule-sets based bilevel decision problems, CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE. ,2015,Vol: 27, No: 4,pages: 830-54

[108]. Wenjuan Luo, Fuzhen Zhuang, Weizhong Zhao, Qing He, Zhongzhi Shi. QPLSA: Utilizing quad-tuples for aspect identification and rating,  Information Processing and Management 51 (2015) 25–41

[109]. Wenchao Yu, Fuzhen Zhuang, Qing He and Zhongzhi Shi. Learning Deep Representations via Extreme Learning Machine, Neurocomputing, Volume 149, Part A, 3 February 2015, Pages 308-315

[110]. Qing He, Xin Jin, Changying Du, Fuzhen Zhuang and Zhongzhi Shi. Clustering in extreme learning machine feature space. Neurocomputing 128 : 88-95 (2014). (SCI).

[111]. Qing He, Tianfeng Shang, Fuzhen Zhuang and Zhongzhi Shi. Parallel Extreme Learning Machine for Regression based on MapReduce, Neurocomputing 102(2013)52–58 (SCI\EI)

[112]. He, Qing; Zhao, Weizhong; Shi, Zhongzhi. CHSMST: A clustering algorithm based on hyper surface and minimum spanning tree, Soft Computing, v 15, n 6, p 1097-1103, June 2011(SCI\EI)

[113]. Qing He, Changying Du, Qun Wang, FuzhenZhuang, Zhongzhi Shi. A Parallel Incremental Extreme SVM Classifier, Neurocomputing,74 (2011) 2532–2540 (SCI\EI )

[114]. Qing He, Xiurong Zhao, Zhongzhi Shi.Minimal consistent subset for Hyper Surface Classification method. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE Volume: 22 Issue: 1 Pages: 95-108, FEB 2008.(SCI\EI)

[115]. Qing He, Xiurong Zhao, Zhongzhi Shi. Classification based on dimension transposition for high dimension data,International Journal Soft Computing 11(4),2007, pp: 329 - 334(SCI)

[116]. Xiang Ao; Ping Luo; Xudong Ma; Fuzhen Zhuang; Qing He*; Zhongzhi Shi; Zhiyong Shen. Combining Supervised and Unsupervised Models via Unconstrained Probabilistic Embedding, Information Sciences, 257 (2014) 101–114. (SCI impact factor (2012): 3.643)

[117]. Fuzhen Zhuang, Ping Luo, Changying Du, Qing He*, Zhongzhi Shi, Hui Xiong: Triplex transfer learning: exploiting both shared and distinct concepts for text classification, IEEE TRANSACTIONS ON CYBERNETICS, VOL. 44, NO. 7, 1191-1203, JULY 2014 (impact factor (2012): 3.236) (SCI\EI)

[118]. Shuo Han, Fuzhen Zhuang, Qing He*, Zhongzhi Shi, & Xiang Ao. Energy model for rumor propagation on social networks. Physica A: Statistical Mechanics and its Applications,394 (2014) 99–109.

[119]. Wenjuan Luo, Fuzhen Zhuang, Qing He*, Zhongzhi Shi Exploiting relevance, coverage, and novelty for query-focused multi-document summarization,Knowledge-Based Systems. Volume 46, July 2013, Pages 33–42 . (SCI\EI)

[120]. Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He*, Yuhong Xiong, Zhongzhi Shi and Hui Xiong. Mining Distinction and Commonality across Multiple Domains using Generative Model for Text Classification, IEEE Transactions on Knowledge and Data Engineering, VOL. 24, NO. 11, NOVEMBER 2012,2025-2039(SCI\EI )

[121]. Zhiping Shi, Xi Liu, Qingyong Li, Qing He*, Zhongzhi Shi, Extracting Discriminative Features for CBIR, MULTIMEDIA TOOLS AND APPLICATIONS, Volume 61, Number 2 (2012), 263-279(SCI)

[122]. Fuzhen Zhuang, George Karypis, Xia Ning, Qing He*, Zhongzhi Shi. Multi-view learning via probabilistic latent semantic analysis, Information Sciences,199 (2012) 20–30(SCI\EI)

[123]. Weizhong Zhao, Qing He*, Huifang Ma, Zhongzhi Shi. Effective Semi-supervised Document Clustering via Active Learning with Instance-level Constraints, Knowledge and Information Systems (2012) 30:569–587 (SCI\EI)

[124]. Tan, Qing; He, Qing*; Zhao, Weizhong; Shi, Zhongzhi; Lee, E.S. An improved FCMBP fuzzy clustering method based on evolutionary programming, Computers and Mathematics with Applications, v 61, n 4, p 1129-1144, February 2011(SCI\EI)

[125]. Guang-Quan Zhang, ZhengZheng, Jie Lu, Qing He*. An Algorithm for Solving Rule-Sets Based Bilevel Decision Problems, COMPUTATIONAL INTELLIGENCE Vol.27 No.2 pp.235-259, 2011 (SCI\EI)

[126]. Fuzhen Zhuang, Ping Luo, Hui Xiong, Yuhong Xiong, Qing He*, and Zhongzhi Shi. Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, December 2010 (vol. 22 no. 12) ,pp. 1664-1678 (SCI\EI)

[127]. Zheng, Z., Lu, J, Zhang G, He Q*, Rule sets based bilevel decision model and algorithm, Expert Systems with Applications, 2009. Vol. 36, No. 1, 18-26(SCI)

[128]. Shifei Ding, Yongping Zhang, Xiaofeng Lei, Xinzheng Xu, Xin Wang, Li Wang, Qing He*. Research on a principal components decision algorithm based on information entropy, Journal of Information Science, Vol. 35, No. 1, 120-127 (2009) (SCI)

[129]. Zhuang F Z, Luo P, He Q, et al. Inductive transfer learning for unlabeled target-domain via hybrid regularization. Chinese Sci Bull, 2009, 54: 2470―2478 (SCI)

[130]. Zhiping Shi, Qing He*, Zhongzhi Shi. An Index and Retrieval Framework Integrating Perceptive Features and Semantics for Multimedia Database. Multimedia Tools and Application (2009) 42:207–231 Springer (SCI)

[131]. Zheng, Z., Lu, J, Zhang G, He Q*, Rule sets based bilevel decision model and algorithm, Expert Systems with Applications, 2009. Vol. 36, No. 1, 18-26(SCI)

[132]. Ping Luo, Guoxing Zhan, Qing He*, Zhongzhi Shi, and Kevin Lu, On Defining Partition Entropy by Inequalities. IEEE TRANSACTIONS ON INFORMATION THEORY, v53, n 9, SEPTEMBER 2007, p 3233-3239.(SCI)

[133]. Ping Luo; Lu, Kevin; Shi, Zhongzhi; He, Qing*. Distributed data mining in grid computing environments. Future Generation Computer Systems, v 23, n 1, Jan 1, 2007, p 84-91(SCI\EI)

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毕业学生与在研学生情况

      何清2020年获得华为奖教金、2007年获得所长奖教金,他所教授的课程《人工智能基础》获评国科大优秀课程,他所指导的学生多人获得科学院和计算所奖励,学生毕业后就业情况很好。

 

已毕业学生

赵秀荣是2004级硕士研究生,于2007年获得计算机软件与理论专业硕士学位。在学期间,她发表SCIEI收录论文5篇,获得中国科学院刘永龄奖学金(全院50)2007年毕业后在北京中央外汇业务中心工作。

刘秋阁是2005级硕士研究生,2008年获得计算机软件与理论专业硕士学位。在学期间,他在PAKDD08发表长文一篇(长文占录用文章的12%),并获得赴日本参会奖励(共10名),2008年毕业后到腾讯研究院在北京工作。

赵卫中是2007级博士研究生,2010年获得计算机软件与理论专业博士学位。在学期间,他发表国外SCI期刊和计算机学报等EI收录论文6篇,获得2009年北纬通讯奖学金,2010年优秀毕业生称号,2010年毕业后去湘潭大学工作,2012年去美国工作。

李金成是2007级硕士研究生,2010年获得计算机软件与理论方向硕士学位。他在学期间发表三篇论文被EI收录,获得2010年所长优秀奖,现在深圳证券所工作。

庄福振是2006级硕博研究生2011年获得计算机软件与理论专业博士学位。在IEEETKDEInformationScienceChinese Science Bulletin, CIKM2010SDM2010ICDM2010等期刊和会议发表论文,2008年获得度夏培肃奖,2011年获得中国科学院院长奖学金优秀奖,2013年获得中国人工智能学会优秀博士论文奖。在学期间赴香港科技大学学习2个月,并获得国家留学基金资助前往明尼苏达大学学习半年。20117月留在计算所工作,2013年被聘为副研究员。

马旭东是2008级硕士研究生, 2011年获得计算机软件与理论专业硕士学位。在学期间,他获得2010年腾迅优秀奖,在2011年在人工智能顶级国际会议IJCAI2011上发表论文一篇,毕业后前往Google工作,现在美国Google总部工作。

李婷婷是2009级硕士研究生, 2011年获得计算机软件与理论专业硕士学位。在学期间,她发表两篇EI收录论文,2011年毕业后到中国银行在北京工作。

  庆是2008级博士研究生, 2012年获得计算机软件与理论专业博士学位。在学期间,他在AAAI10IJCMA等国际会议和期刊上发表论文4,获得2010年北纬通信博士生奖,20122月毕业后到阿里云在北京工作。

  群是2009级硕士研究生,2012年获得计算机软件与理论专业硕士学位,发表EI收录论文2篇,2010年获得北纬通信硕士生奖,2012年毕业到人民网工作,现在高德公司在北京工作。

罗文娟是2008级计算机软件与理论专业硕博研究生,她在SCI国际期刊KBSAIRS2010PAKDD2012等会议上发表论文4篇,2013年毕业后到人人网在北京工作。

  智是2010级计算机软件与理论专业硕博连读研究生,已发表EI收录文章2篇,2013年毕业后到新华网,在北京工作。

马云龙是计算机应用技术专业2010级硕士研究生,已发表EI收录文章1篇,2011年获得北纬通信硕士生奖,2013年毕业后到中国科学院信息工程研究所,在北京工作。

 

李宁是2009级计算机软件与理论专业博士研究生,已在IJNDCSNPD2012等期刊和会议发表EI收录的论文4篇,国内核心论文2篇,现在中科院信工所工作。

尚田丰是2010级计算机软件与理论专业博士研究生,已在SCI国际期刊NeuroComputing发表论文1篇,并已在APWeb13IJCNN13上发表论文,2012年获得北纬通信博士生奖。毕业后到新加坡管理大学做博士后。

韩硕是2011级计算机软件与理论专业硕士研究生,在Physica APAKDD14上发表论文两篇,2013年获得北纬通讯奖学金。毕业后到北京亚马逊公司工作。

余文超是2011级计算机软件与理论专业硕士研究生,在ECMLPKDD13NeuroComputing上发表录用论文3篇,2013年获得计算所所长优秀奖,毕业后到美国那卡罗莱纳大学读博士。

杜长营是2009级计算机软件与理论专业硕博连读研究生,2015年博士毕业。他已在NeuroComputing发表SCI收录论文,并在ICDM12上发表长文一篇,获得2010年所长优秀奖,毕业后到中国科学院软件所工作。

金鑫是2011级计算机软件与理论专业博士研究生,已在SCI国际期刊NeuroComputingAMC发表论文2篇,并在ECMLPKDD13发表论文(oral+poster),2013年获得所长优秀奖,2015年博士毕业,毕业后到华为公司北京工作。

敖翔是2010级硕博连读研究生,20129月转博,在Information SciencesWWW14等期刊和会议上发表论文3篇,申请专利1项,2013年获得腾讯奖学金特等奖,获得2014年国家奖学金,2015年博士毕业,现留所工作。

吴新宇是2012级计算机软件与理论硕士研究生,申请了专利两项,2013年获得了计算所技术创新大赛奖项,获得中国科学院计算技术研究所硕士所长奖学金,毕业后到IBM北京工作。

程晓虎是2012级计算机软件与理论专业硕士研究生,IJCAI15FSKD14发表论文一篇,获得中国科学院计算技术研究所斯伦贝谢硕士生奖学金,毕业后到腾讯北京工作。

王浩成是2012级计算机软件与理论专业博士研究生,在Fuzzy Sets and SystemsIDASNPD2014ELM2015上发表论文。 获得2015年度所长优秀奖博士生奖,毕业后到北京市公安局工作。

闫肃是2013级计算机软件与理论专业硕士研究生,在KDD16合作发表论文一篇,毕业后到腾讯工作。

罗丹是2013级计算机软件与理论专业硕士研究生,在ICDM2015合作发表论文一篇,毕业后到微软工作。

黄明是2014级计算机软件与理论专业硕士研究生,申请专利一项,在Machine Learning期刊发表论文一篇,毕业后到百度工作。

左罗是2014级计算机软件与理论专业硕士研究生,申请专利一项,在DASFA18合作发表论文一篇,毕业后到中国人民银行工作。

周干斌2013级计算机软件与理论专业直博研究生, 在IJCAI16, AAAI17AAAIISCIENCE CHINA发表论文五篇,毕业后到腾讯工作。

周英敏2014级计算机软件与理论专业 硕士研究生,在WWW17发表论文一篇,现在苏州微软研究院工作。

何佳 直博研究生 2014年入学,在IJCAI16,IJCAI17等会议和期刊上发表论文5篇,国家奖学金博士生奖,第四范式博士生奖,毕业后到华为北京研究所工作。

陈敬伍 硕士研究生 2016年入学,合作获得IJCAI17年数据挖掘大赛最具潜力奖。在IEEETKDESIGIR18发表论文2篇,获得国家奖学金硕士生奖,毕业后到头条工作。 

泰 潘 博士留学生 2016年入学,在PR等国际期刊上发表论文两篇,毕业后到泰国国立法政大学Thammasat University任教。

奚冬博  硕士研究生 2017年入学,在AAAI2019WWWW2020SIGIR20发表论文4篇,获得2019年获三好学生称号,2019年获易方达金融科技硕士生奖,2019年获学业奖学金一等奖,毕业后到美团工作。

张 钊博士研究生  2015年入学,申请专利一项.发表AAAI2020EMNLP2018CIKM2018Information System论文共四篇,获得所级企业冠名奖学金,毕业后留所工作。

潘斐阳 直博研究生 2016年入学,IJCAI-17数据挖掘大赛第一赛季第1名、特别奖、最具潜力奖,IJCAI-18数据挖掘大赛第一赛季第1名,Kaggle世界排名63/83522Kaggle Recuit challenge1名,Kaggle TalkingData fraud click detection2名。在WWW2020AAAI2019WWW2019SIGIR19NeuIPS发表论文6篇,2018年获得所级企业冠名奖,获得2021年度所长特别奖,毕业后入选华为天才计划进入华为工作。

罗 玲 硕士研究生 2018年入学,分别在IJCAI18IJCAI19ENMLP20发表长文3篇,毕业后到华为工作。

现指导学生

孙 莹 硕博研究生  2017年入学,在NATURE 子刊COMMUNICATIONS Scientific Reports, KDD18KDD19SIGIR21WWW21等发表论文

柳 阳 博士研究生 2017年入学,IEEETKDE录用论文1篇,WWW21WWWDASFACIKM2020发表论文5

李硕凯 直博研究生 2018年入学,在Neural Networks发表论文一篇 

于 朔 博士研究生 2019入学  

黄艨靼 硕士研究生 2019入学 

董临风 硕士研究生 2019入学     

贾 海 硕士研究生 2019入学,2021年毕业

王天鑫 硕士研究生 2019入学,在ECAI 2020CIKM2021发表论文二篇

汪润川 硕士研究生 2019入学,在CIKM2021发表论文一篇

张富威 硕士研究生 2020入学

张函玉 直博研究生  2020入学

吴贻清 硕士研究生 2020年入学,在ICDM 2020发表论文一篇

 薛泓彦  博士研究生 2020入学  

刘骐鸣  硕士研究生  2020入学  

周子贤  硕士研究生  2020入学