所属研究所、院系:
人工智能与无人系统研究所

职称:
副教授

E-mail:
wangchd3@mail.sysu.edu.cn

办公地点:
A222




教师简介:
王昌栋，中山大学数据科学与计算机学院副教授，博士生导师。2004年9月至2008年7月在中山大学攻读数学与应用数学专业，并同时攻读计算机科学与技术专业，2008年获得中山大学理学学士学位。2008年9月至2013年7月在中山大学硕博连读，攻读计算机应用技术专业，2013年获得中山大学工学博士学位。2011年曾获首届广州市菁英计划公派留学项目资助，作为联合培养博士生，于2011年12月至2012年11月在美国伊利诺大学-芝加哥校区留学，师从IEEE Fellow Philip S. Yu。
他的研究方向包括数据聚类、社交网络、推荐系统。他以第一作者身份或者指导学生发表了120余篇学术论文，包括IEEE TPAMI、IEEE TKDE、IEEE?TCYB、IEEE TNNLS等国际顶级刊物和KDD、AAAI、IJCAI、CVPR等国际顶级会议。主持了包括广东省自然科学基金-****基金、广东特支计划“科技创新青年拔尖人才”、国家重点研发计划项目-子课题、国家自然科学基金-面上项目、国家自然科学基金-青年基金、CCF-腾讯犀牛鸟科研基金等13个项目。在教学方面，他分别获得2013／2015年IBM公司产学合作专业综合改革项目资助建设大数据平台/云计算课程，是全国20门受资助课程之一。
他是人工智能权威期刊Journal of Artificial Intelligence Research（JAIR，CCF B类）的副编辑（AE），担任第16届数据挖掘与应用国际学术会议（16th International Conference on Advanced Data Mining and Applications，ADMA 2020）的程序委员会共同主席（PC?Co-chair），是十几个国际刊物如IEEE TPAMI、JMLR、IEEE TKDE、IEEE TNNLS、IEEE TCYB、PR等的审稿人，是KDD（2019，2020）、IJCAI（2019，2020）、AAAI（2017、2018、2019、2020）、CIKM (2019)、IEEE ICDM （2014、2015、2016、2018、2019）的程序委员，是中国模式识别与计算机视觉学术会议PRCV 2018的网站主席。他曾参加ICDM2010（澳大利亚悉尼）、ICDM2011（加拿大温哥华）、SDM2013（美国奥斯汀）、ICMLA2014（美国底特律）、IEEE Bigdata2016（美国华盛顿）、DASFAA2018（澳大利亚黄金海岸）、ICDM2018（新加坡）、BIBM2018（西班牙马德里）、IJCAI2019（中国澳门）等国际会议，与学术界同行交流，并16次做ORAL报告。他的ICDM2010论文荣获最佳论文提名奖；他曾获2012年微软亚洲研究院****奖提名，2014年中国计算机学会优秀博士学位论文提名奖，2015年中国人工智能学会优秀博士学位论文奖，2017年广东特支计划“科技创新青年拔尖人才”，2018年度广东省自然科学奖一等奖、2020年度广东省自然科学奖二等奖。他是中国人工智能学会-模式识别专业委员会委员，中国计算机学会-数据库专业委员会委员，中国计算机学会-计算机视觉专业委员会委员，CCF-YOCSEF广州副主席（2018-2020），CCF广州分部副主席（2019.3-2021.3），CCF-YOCSEF广州主席（2020-2021），CCF广州分部学生分会指导主任（2021.4-2023.4），CCF学生分会工作组组长（2021.4-2022.4）。

研究领域:
数据挖掘、人工智能：
1、网络分析（社交网络）
2、数据聚类
3、医学数据处理
4、推荐算法
5、精准教育
欢迎大二及以上本科生进团队进行科研训练；欢迎保研学生进团队攻读博士、硕士研究生；本团队长期招聘科研博士后、特聘（副）研究员。

教育背景:
1. Visiting student at University of Illinois at Chicago, Jan. 2012-Nov.2012. Advisor: Prof. Philip S. Yu.
2. Combined master’s/PhD program: Ph.D. student in Computer Science, Sun Yat-sen University. Sept. 2010-June 2013. Advisor: Prof. Jian-Huang Lai.
3. Combined master’s/PhD program: M.Sc. in Computer Science, Sun Yat-sen University. Sept. 2008-June 2010. Advisor: Prof. Jian-Huang Lai.
4. B.S. in Applied Mathematics, Sun Yat-sen University. Sept. 2004--June 2008.

工作经历:
1. Assistant Professor:?School of Mobile Information Engineering, Sun Yat-sen University, July 2013-Dec. 2015.
2. Assistant Professor: School of Data and Computer Science, Sun Yat-sen University, Dec. 2015-July. 2016.
3. Associate?Professor: School of Data and Computer Science, Sun Yat-sen University, July. 2016-Now.

海外经历:
1. Visiting student at University of Illinois at Chicago, Jan. 2012-Nov. 2012.

获奖及荣誉:
2020年度广东省自然科学奖二等奖；小样本视觉表征与识别方法；全部完成人：郑伟诗,王昌栋,赖剑煌, 谢晓华,胡建芳；完成单位：中山大学.
2018年度广东省自然科学奖一等奖；视觉鲁棒特征提取与非线性分析；全部完成人：赖剑煌,郑伟诗,谢晓华,阮邦志,王昌栋,朱俊勇,马锦华,黄剑；完成单位：中山大学,香港浸会大学.
2016年“广东特支计划”科技创新青年拔尖人才.
2016年广东省自然科学基金-****科学基金获得者.
2015年中国人工智能学会优秀博士学位论文.
2014年中国计算机学会优秀博士学位论文提名奖.
SIAM SDM 2013 Student Travel Award.
2012 Microsoft Research Asia (MSRA) Fellowship Nomination Award.
IEEE ICDM 2011 Student Travel Award.
?IEEE ICDM 2010 Honorable Mention Award for the Best Research Paper.
?IEEE ICDM 2010 Student Travel Award.

科研项目:
1)??? 2019年度中山大学高校基本科研业务费-新兴学科交叉学科资助计划项目，基于脑电数据分析的人工耳蜗术后耳聋患者大脑功能康复系统建立及其临床示范应用，No. 19lgjc10，2019 .01-2020.12，主持。
2)??? 2019年国家自然科学基金-面上项目，基于相似度学习的异构数据聚类算法研究及其应用，No. **，2019.01-2022.12。
3)??? 2019年国家重点研发计划项目“社区风险监测与防范关键技术研究”课题5 “‘数据-计算’深度交互的社区风险情景计算与预测技术”，No. 2018YFC**，2018.07-2021.06，课题5中山大学负责人。
4)??? 2019年“广州市高校创新创业教育项目” 广州市大学生创新创业项目综合信息服务平台建设， No. 2019PT204，2019.01-2020.12，参与方主持。
5)??? 2016年“广东特支计划”科技创新青年拔尖人才，No. 2016TQ03X542，2017.04-2020.04，主持。
6)??? 2016年国家重点研发计划项目“面向大范围场景透彻感知的视觉大数据智能分析关键技术与验证系统”课题3“群体视觉大数据的透彻感知关键技术”，No. 2016YFB**，2016.07 -2020.06，课题3项目骨干。
7)??? 2016年广东省自然科学基金-****科学基金，大数据非线性聚类方法及其应用，No. 2016A，2016.06.01-2020.06.01，主持。
8)??? 2016年度中山大学高校基本科研业务费青年教师科研资助计划项目-重点培育项目，基于社交网络的大数据推荐算法及其应用，No. 67，2016.01-2017.12，主持。
9)??? 2015年度广东省前沿与关键技术创新专项资金-重大科技专项，基于自主分布式数据库的大数据内存计算技术研发及应用，No. 2015B，2013.08-2016.05，高校方主持。
10) 2016年国家自然科学基金-青年科学基金，具有耦合性结构的多视图社交网络社区发现算法研究及其应用，No. **，2016.01-2018.12，主持。
11) 2015年广东省自然科学基金-博士启动项目，多视图聚类新方法及其应用，No. 2014A，2015.01-2018.01，主持。
12) 2014年CCF-腾讯犀牛鸟科研基金，异构社交网络动态社区检测，No. CCF-TencentRAGR**，2014.09.20-2015.10.01，主持。
13）2013年度中山大学高校基本科研业务费青年教师科研资助计划项目-培育项目，基于计算机视觉技术的商业零售移动大数据采集与分析，No. 46，2014.01.01 -2015.12.31，主持。

主要学术兼职:
1)??? Associate Editor
-?????? Journal of Artificial Intelligence Research (JAIR, CCF B, Since Aug. 2019).
2)??? Conference Co-Chairs:
-?????? 16th International Conference on Advanced Data Mining and Applications (ADMA 2020), PC Co-chair.
-?????? PRCV 2018, Website Co-chair.
?
3)??? Program Committee Members:
-?????? IEEE ICDM 2014, 2015, 2016, 2018, 2019.
-?????? AAAI 2017, 2018, 2019, 2020.
-?????? KDD 2019, 2020.
-?????? IJCAI 2019, 2020.
-?????? CIKM 2019.
-?????? IJCAI Demo Track 2018, 2019, 2020.
-?????? The 8th IEEE International Conference on Big Knowledge (IEEE ICBK) 2017.
-?????? The 4th IEEE International Congress of Big Data Congress 2015.
4)??? Reviewers:
-?????? IEEE TPAMI, IEEE TCYB, IEEE TKDE, IEEE TNNLS, JMLR, IEEE TII.
-?????? Pattern Recognition, Neural Networks, Neurocomputing, Knowledge-Based Systems, Information Sciences, KAIS.
-?????? Many other good journals

教授课程:
1)??? 2013 IBM Big Data Platform Course (One of the 20 courses supported by IBM in China).
2)??? 2013 Fall: Linear Algebra (required course, 300 students, 100 students per class, 4 hours per class each week).
3)??? 2014 Spring: Numerical Analysis (selective course, 300 students, 150 students per class, 3 hours per class each week).
4)??? 2014 Fall: Cloud Application Development (required course, 300 students, 150 students per class, 4 hours per class each week).
5)??? 2015 Spring: Data Mining (selective course, 80 students, 2 hours each week).
6)??? 2015 Spring: Numerical Analysis (selective course, 500 students, 150 students per class, 3 hours per class each week).
7)??? 2015 Fall: Cloud Application Development (required course, 450 students, 150 students per class, 4 hours per class each week).
8)??? 2016 Cloud Computing Course (One of the 20 courses supported by IBM in China).
9)??? 2016 Spring: Data Mining (selective course, 25 students, 2 hours each week).
10) 2016 Spring: Numerical Analysis (selective course, 500 students, 150 students per class, 3 hours per class each week).
11) 2017 Spring: Data Mining (selective course, 120 students, 2 hours each week).
12) 2017 Fall: Cloud Application Development (required course, 300 students, 150 students per class, 4 hours per class each week).
13) 2018 Spring: Data Mining (selective course, 240 students, 120 students per class, 2 hours per class each week).
14) 2018 Fall: Graduate Student English (selective course for master students, 10 students, 10 students per class, 2 hours per class each week).
15) 2019 Spring: Data Mining and Machine Learning (required course, 60 students, 60 students per class, 3 hours per class each week).
16) 2019 Fall: Data Mining (required course, 30 students, one class, 8 hours per class each week, 4 weeks).
17) 2019 Fall: Graduate Student English (selective course for master students, 39 students, 39 students per class, 2 hours per class each week, co-work with other two teachers).
18) 2019 Fall: Machine Learning and Artificial Intelligence (selective course for master students, 40 students, 40 students per class, 3 hours per class each week, co-work with the other six teachers)

代表性论著:
2021:
1) Ling Huang (Graduate student), Chang-Dong Wang* and Hong-Yang Chao. HM-Modularity: A Harmonic Motif Modularity Approach for Multi-layer Network Community Detection. IEEE Transactions on Knowledge and Data Engineering,?Vol. 33, No. 6, pp. 2520-2533, June 2021.
2) Bao-Yu Liu (Graduate student), Ling Huang (Graduate student), Chang-Dong Wang*, Suohai Fan and Philip S. Yu. Adaptively Weighted Multiview Proximity Learning for Clustering.? IEEE Transactions on Cybernetics,?Vol. 51, No. 3, pp. 1571-1585, March 2021.
3) Xunxun Wu (Graduate student), Chang-Dong Wang*?and Pengfei Jiao. Hybrid-order Stochastic Block Model. In Proc. of the 35th AAAI Conf. on Artificial Intelligence (AAAI'20, CCF A类, Acceptance rate 21%), Virtually, Feb. 2-9, 2021.
4) Man-Sheng Chen (Graduate student), Ling Huang, Chang-Dong Wang*, Dong Huang and Jian-Huang Lai. Relaxed Multi-view Clustering in Latent Embedding Space. Information Fusion, Vol. 68, pp. 8-21, 2021.
5) Dong Huang, Chang-Dong Wang*, Jian-Huang Lai and Chee-Keong Kwoh. Toward Multi-Diversified Ensemble Clustering of High-Dimensional Data: From Subspaces to Metrics and Beyond. IEEE Transactions on Cybernetics,?in press, 2020.
6) Guangqiang Xie, Runpeng Zhang (Undergraduate), Yang Li*, Ling Huang, Chang-Dong Wang, Hao Yang (Undergraduate), and Jiahao Liang (Undergraduate). AttractRank: District Attraction Ranking Analysis Based on Taxi Big Data. IEEE Transactions on Industrial Informatics,?Vol. 17, No. 3, pp. 1679-1688, March 2021.
7) Dong Huang*, Chang-Dong Wang, Hongxing Peng, Jianhuang Lai and Chee-Keong Kwoh. Enhanced Ensemble Clustering via Fast Propagation of Cluster-Wise Similarities. IEEE Transactions on Systems Man Cybernetics-Systems,?Vol. 51, No. 1, pp. 508-520, Jan. 2021.
8) Juan-Hui Li (Graduate student), Ling Huang, Chang-Dong Wang*, Dong Huang, Jian-Huang Lai and Pei Chen. Attributed Network Embedding with Micro-Meso Structure. ACM Transactions on Knowledge Discovery from Data,?Vol. 15, No. 4, Article 72, April 2021.
9) Guang-Yu Zhang (Graduate student), Xiaowei Chen (Graduate student), Yu-Ren Zhou*, Chang-Dong Wang?and Dong Huang. Consistency- and Inconsistency-aware Multi-view Subspace Clustering.?In Proc. of the 26th International Conference on Database Systems for Advanced Applications (DASFAA'21, CCF B类), Taipei, Taiwan, April 11-14, 2021.
2020:
1）Chang-Dong Wang*, Wei Shi (Graduate student), Ling Huang, Kun-Yu Lin, Dong Huang and Philip S. Yu. Node-pair Information Preserving Network Embedding Based on Adversarial Networks. IEEE Transactions on Cybernetics,?in press, 2020.
2) Chang-Dong Wang*, Man-Sheng Chen (Graduate student), Ling Huang, Jian-Huang Lai and Philip S. Yu. Smoothness Regularized Multi-view Subspace Clustering with Kernel Learning.?IEEE Transactions on Neural Networks and Learning Systems,?in press 2020.
3) Chang-Dong Wang*, Wu-Dong Xi (Graduate student), Ling Huang, Yin-Yu Zheng (Graduate student), Zi-Yuan Hu (Undergraduate) and Jian-Huang Lai. A BP Neural Network Based Recommender Framework with Attention Mechanism. IEEE Transactions on Knowledge and Data Engineering,?in press 2020.
4) Man-Sheng Chen (Graduate student),?Ling Huang, Chang-Dong Wang*, Dong Huang and Philip S. Yu. Multi-view Subspace Clustering with Grouping Effect.?IEEE Transactions on Cybernetics,?in press, 2020.
5)?Shi-Ting Zhong (Graduate student), Ling Huang, Chang-Dong Wang*, Jian-Huang Lai and Philip S. Yu. An Autoencoder Framework with Attention Mechanism for Cross-Domain Recommendation. IEEE Transactions on Cybernetics,?in press, 2020.
6) Bao-Yu Liu (Graduate student), Ling Huang, Chang-Dong Wang*, Jian-Huang Lai and Philip S. Yu. Multi-view Consensus Proximity Learning for Clustering. IEEE Transactions on Knowledge and Data Engineering,?in press 2020.
7) Ling Huang (Graduate student), Chang-Dong Wang*, Hong-Yang Chao and Philip S. Yu. MVStream: Multiview Data Stream Clustering. IEEE Transactions on Neural Networks and Learning Systems,?Vol. 31, No. 9, pp. 3482-3496, Sept. 2020. DOI: 10.1109/TNNLS.2019.**.
8) Dong Huang, Chang-Dong Wang*, Jian-Sheng Wu, Jian-Huang Lai, and Chee-Keong Kwoh. Ultra-Scalable Spectral Clustering and Ensemble Clustering. IEEE Transactions on Knowledge and Data Engineering,?Vol. 32, No. 6, pp. 1212-1226, June 2020. DOI: 10.1109/TKDE.2019.**.
9) Man-Sheng Chen (Graduate student), Ling Huang (Graduate student), Chang-Dong Wang* and Dong Huang. Multi-view Clustering in Latent Embedding Space. In Proc. of the 34th AAAI Conf. on Artificial Intelligence (AAAI'20, CCF A类, Acceptance rate 20.6%, Oral), New York, NY, USA, Feb. 7-Feb. 12, 2020, pp. 3513-3520.
10) Pei-Zhen Li (Graduate student), Ling Huang (Graduate student), Chang-Dong Wang*, Jian-Huang Lai and Dong Huang. Community Detection by Motif-aware Label Propagation.? ACM Transactions on Knowledge Discovery from Data,?Vol. 14, No. 2, Article 22, pp. 1-19, Feb. 2020.
11)?Juanhui Li(Graduate student), Yao Ma, Yiqi Wang, Charu Aggarwal, Chang-Dong Wang*, and Jiliang Tang. Graph Pooling with Representativeness. In Proc. of the 20th Int. Conf. on Data Mining (ICDM'20, CCF B类, Acceptance rate 19.7%), Sorrento, Italy, Nov. 17-20, 2020, pp. 302-311.
12）Ling Huang, Xing-Xing Liu (Graduate student), Shu-Qiang Huang*, Chang-Dong Wang, Wei Tu, Jia-Meng Xie and Wendi Xie. Temporal Hierarchical Graph Attention Network for Traffic Prediction. ACM Transactions on Intelligent Systems and Technology, in press, 2020.
13) 陈碧毅(Undergraduate), 黄玲(Graduate student)*, 王昌栋, 景丽萍. 基于显式反馈与隐式反馈的协同过滤推荐算法. 《软件学报》（CCF推荐A类中文期刊）, Vol. 31, No. 3, pp. 1-13, Jan. 2020.
14）Heng-Ping He (Graduate student), Peizhen Li (Graduate student), Ling Huang (Graduate student), Yu-Xuan Ji (Graduate student) and Chang-Dong Wang*. Latent Space Clustering via Dual Discriminator GAN. In Proc. of the 25th International Conference on Database Systems for Advanced Applications (DASFAA'20, CCF B类), Jeju, South Korea, May 21-24, 2020, pp. 671–679.
2019:
1) Chang-Dong Wang, Zhi-Hong Deng (Undergraduate), Jian-Huang Lai* and Philip S. Yu. Serendipitous Recommendation in E-commerce using Innovator-Based Collaborative Filtering.? IEEE Transactions on Cybernetics,?Vol. 49, No. 7, pp. 2678-2692, 2019.
2）Pei-Zhen Li (Graduate student), Ling Huang (Graduate student), Chang-Dong Wang*, Jian-Huang Lai. EdMot: An Edge Enhancement Approach for Motif-aware Community Detection.? In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’19, CCF A类, Oral presentation, Acceptance rate 9.1%), Anchorage, Alaska USA, August 4-8, 2019, pp. 479-487.
3) Wu-Dong Xi (Undergraduate), Ling Huang (Graduate student), Chang-Dong Wang*, Yin-Yu Zheng(Undergraduate), Jian-Huang Lai. BPAM: Recommendation Based on BP Neural Network with Attention Mechanism.? In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19, CCF A类, Acceptance rate 17.9%), Macao, China, August 10-16, 2019, pp. 3905-3911.
4) Zhi-Hong Deng (Graduate student), Ling Huang(Graduate student), Chang-Dong Wang*, Jian-Huang Lai and Philip S. Yu. DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System. In Proc. of the 33rd AAAI Conf. on Artificial Intelligence (AAAI'19, CCF A类, Acceptance rate 16.2%, Oral), Honolulu, Hawaii, USA, Jan. 27-Feb. 1, 2019, pp. 61-68.
5) Ling Huang (Graduate student), Chang-Dong Wang* and Hong-Yang Chao. Higher-Order Multi-layer Community Detection. In Proc. of the 33rd AAAI Conf. on Artificial Intelligence (AAAI'19, CCF A类，Poster Session), Honolulu, Hawaii, USA, Jan. 27-Feb. 1, 2019, pp. 9945-9946.
6) Ling Huang (Graduate student), Hong-Yang Chao and Chang-Dong Wang*. Multi-View Intact Space Clustering. Pattern Recognition,?Vol. 86, pp. 344-353, 2019.
7) Ling Huang (Graduate student), Chang-Dong Wang*?and Hong-Yang Chao. oComm: Overlapping Community Detection in Multi-view Brain Network. IEEE/ACM Transactions on Computational Biology and Bioinformatics,?In press, 2019.
8) He Huang*, Changhu Wang, Philip S. Yu and Chang-Dong Wang. Generative Dual Adversarial Network for Generalized Zero-shot Learning. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'2019, CCF A类, Acceptance rate 25.2%), Long Beach, CA, USA, June 16- June 20, 2019, pp. 801-810.
9) Shi-Ting Zhong (Undergraduate), Ling Huang (Graduate student), Chang-Dong Wang*, and Jian-Huang Lai. Constrained Matrix Factorization for Course Score Prediction. In Proc. of the 19th Int. Conf. on Data Mining (ICDM'19, CCF B类, Acceptance rate 18.5%), Beijing China, Nov. 8-11, 2019, pp. 1510-1515.
10) Yu-Xuan Ji (Graduate student), Ling Huang (Graduate student), Heng-Ping He (Graduate student), Chang-Dong Wang*, Guangqiang Xie, Wei Shi (Graduate student), and Kun-Yu Lin (Graduate student). Multi-view Outlier Detection in Deep Intact Space. In Proc. of the 19th Int. Conf. on Data Mining (ICDM'19, CCF B类, Acceptance rate 18.5%), Beijing China, Nov. 8-11, 2019, pp. 1132-1137.
11) Youwei Liang (Undergraduate), Dong Huang*, and Chang-Dong Wang. Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multi-view Clustering.?In Proc. of the 19th Int. Conf. on Data Mining (ICDM'19, CCF B类, Acceptance rate 18.5%), Beijing China, Nov. 8-11, 2019, pp. 1204-1209.
12) Yi-Ming Wen (Undergraduate), Ling Huang (Graduate student), Chang-Dong Wang*, and Kun-Yu Lin (Graduate student).? Direction Recovery in Undirected Social Networks Based on Community Structure and Popularity. Information Sciences, Vol. 473, pp. 31-43, 2019.
13) Han Zhang(Undergraduate), Chang-Dong Wang*, Jian-Huang Lai and Philip S. Yu. Community Detection Using Multilayer Edge Mixture Model. Knowledge and Information Systems,?Vol. 60, pp. 757-779, 2019.
14) Ling Huang (Graduate student), Zhi-Lin Zhao (Graduate student), Chang-Dong Wang*, Dong Huang and Hong-Yang Chao. LSCD: Low-Rank and Sparse Cross-Domain Recommendation. Neurocomputing,?Vol. 366, pp. 86-96, 2019
15) Qi-Ying Hu (Graduate student), Ling Huang (Graduate student), Chang-Dong Wang* and Hong-Yang Chao. Item Orientated Recommendation by Multi-view Intact Space Learning with Overlapping. Knowledge-Based Systems,?Vol. 164, pp. 358-370, 2019.
16) Pei-Zhen Li (Graduate student), Ling Huang (Graduate student)*, Chang-Dong Wang, Chuan Li, Jian-Huang Lai. Brain Network Analysis for Auditory Disease: A Twofold Study. Neurocomputing. Vol. 347, pp. 230-239, 2019.
17) Xiuchun Xiao, Neal Xiong*, Jianhuang Lai, Chang-Dong Wang, Zhenan Sun and Jingwen Yan. A Local Consensus Index Scheme for Random-Valued Impulse Noise Detection Systems. IEEE Transactions on Systems Man Cybernetics-Systems,?In press, 2019.
18) Dong Huang, Xiaosha Cai, and Chang-Dong Wang*.?Unsupervised Feature Selection with Multi-Subspace Randomization and Collaboration. Knowledge-Based Systems,?Vol. 182, pp. 104856, 2019.
19) Man-Sheng Chen (Graduate student), Ling Huang (Graduate student), Chang-Dong Wang*, and Dong Huang. Multi-view Spectral Clustering via Multi-view Weighted Consensus and Matrix-decomposition based Discretization. In Proc. of the 24th International Conference on Database Systems for Advanced Applications (DASFAA'19, CCF B类), Chiang Mai, Thailand, 22-25 April 2019, pp. 175-190.
20) Weixin Zeng, Xiang Zhao*, Jiuyang Tang, Jinzhi Liao and Chang-Dong Wang. Relevance-Based Entity Embedding. In Proc. of the 24th International Conference on Database Systems for Advanced Applications (DASFAA'19, CCF B类), Chiang Mai, Thailand, 22-25 April 2019, pp. 300-304.
21) Zhi-Ran Sun(Undergraduate), Yue-Xin Cai*, Shao-Ju Wang, Chang-Dong Wang, Yi-Qing Zheng, Yan-Hong Chen and Yu-Chen Chen. Multi-view Intact Space Learning for Tinnitus Classification in Resting State EEG. Neural Processing Letters,?Vol. 49, No. 2, pp. 611-624, 2019.
22) Pei-Zhen Li(Graduate student), Yue-Xin Cai*, Chang-Dong Wang, Mao-Jin Liang and Yi-Qing Zheng. Higher-order Brain Network Analysis for Auditory Disease. Neural Processing Letters,?Vol. 49, pp. 879-897, 2019.
23) Yuexin Cai, Suijun Chen, Yanhong Chen, Jiahong Li, Chang-Dong Wang, Fei Zhao, Caiping Dang, Jianheng Liang, Nannan He, Maojin Liang and Yiqing Zheng*. Altered Resting-State EEG Microstate in Sudden Sensorineural Hearing Loss Patients with Tinnitus. Frontiers in Neuroscience,?Vol. 13, pp. 1-9, 2019.
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2018:
1）Dong Huang, Chang-Dong Wang*?and Jian-Huang Lai. Locally Weighted Ensemble Clustering. IEEE Transactions on Cybernetics,?Vol. 48, No. 5, pp. 1460-1473, 2018.
2）Juan-Hui Li(Undergraduate), Chang-Dong Wang*,?Pei-Zhen Li(Undergraduate) and Jian-Huang Lai. Discriminative Metric Learning for Multi-view Graph Partitioning. Pattern Recognition,?Vol. 75, pp. 199-213, 2018.
3) Ling Huang (Graduate student), Chang-Dong Wang* and Hong-Yang Chao. A Harmonic Motif Modularity Approach for Multi-layer Network Community Detection. In Proc. of the 18th Int. Conf. on Data Mining (ICDM'18, CCF B类, Acceptance rate 19.94%), Singapore, Nov. 17-20, 2018, pp. 1043–1048.
4)?Ling Huang(Graduate student), Chang-Dong Wang* and Hong-Yang Chao. Overlapping Community Detection in Multi-view Brain Network. In Proc. of the 2018 Int. Conf. on Bioinformatics and Biomedicine (BIBM'18, CCF B类), Madrid, Spain, Dec. 3-6, 2018,?pp. 655-658.
5) He Huang, Bokai Cao, Philip S. Yu*, Chang-Dong Wang, and Alex D. Leow. dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction. In Proc. of the 18th Int. Conf. on Data Mining (ICDM'18, CCF B类, Acceptance rate 19.94%), Singapore, Nov. 17-20, 2018, pp. 157–166.
6) Guang-Yu Zhang(Graduate student), Chang-Dong Wang*, Dong Huang, Wei-Shi Zheng and Yu-Ren Zhou. TW-Co-k-means: Two-level Weighted Collaborative k-means for Multi-view Clustering. Knowledge-Based Systems,?Vol. 150, pp. 127-138, 2018.
7）Lei Xu(Undergraduate), Chang-Dong Wang*, Mao-Jin Liang, Yue-Xin Cai and Yi-Qing Zheng. Brain Network Regional Synchrony Analysis in Deafness. Biomed Research International,?Vol. 2018, pp. 1-11, 2018.
8）Juan-Hui Li(Graduate student), Chang-Dong Wang*, Ling Huang(Graduate student), Dong Huang, Jian-Huang Lai and Pei Chen. Attributed Network Embedding with Micro-Meso Structure. In Proc. of The 23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018, CCF B类), Gold Coast, Australia, May 21-24, 2018, pp. 20-36.
9）Kun-Yu Lin(Graduate student), Ling Huang(Graduate student), Chang-Dong Wang*?and Hong-Yang Chao. Multi-view Proximity Learning for Clustering. In Proc. of The 23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018, CCF B类), Gold Coast, Australia, May 21-24, 2018, pp. 407-423.
10）Zhi-Lin Zhao(Graduate student), Ling Huang(Graduate student), Chang-Dong Wang*?and Dong Huang. Low-Rank and Sparse Cross-Domain Recommendation Algorithm. In Proc. of The 23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018, CCF B类), Gold Coast, Australia, May 21-24, 2018, pp. 150-157.
11）Yi-Ming Wen(Undergraduate), Chang-Dong Wang*?and Kun-Yu Lin(Graduate student). Direction Recovery in Undirected Social Networks Based on Community Structure and Popularity. In Proc. of The 23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018, CCF B类), Gold Coast, Australia, May 21-24, 2018, pp. 529-537.
12) Yi-Kun Qin, Zhu-Liang Yu*, Chang-Dong Wang, Zheng-Hui Gu and Yuan-Qing Li. A Novel Clustering Method based on Hybrid K-Nearest-Neighbor Graph.?Pattern Recognition,?Vol. 74, pp. 1-14, 2018.
13）Yuexin Cai, Dong Huang, Yanhong Chen, Haidi Yang, Chang-Dong Wang, Fei Zhao, Jiahao Liu, Yingfeng Sun, Guisheng Chen, Xiaoting Chen, Hao Xiong, Yiqing Zheng*. Deviant dynamics of resting state electroencephalogram microstate in patients with subjective tinnitus. Frontiers in Behavioral Neuroscience,?Vol. 12, pp. 1-9, 2018.
14）Ming-Chuan Tsai(Undergraduate), Yue-Xin Cai*, Chang-Dong Wang, Yiqing Zheng, Jia-Ling Ou and Yanhong Chen. Tinnitus Abnormal Brain Region Detection Based on Dynamic Causal Modeling and Exponential Ranking. Biomed Research International,?Vol. 2018, pp. 1-10, 2018.
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2017:
1) Guang-Yu Zhang(Graduate student), Chang-Dong Wang*, Dong Huang and Wei-Shi Zheng. Multi-View Collaborative Locally Adaptive Clustering with Minkowski Metric. Expert Systems with Applications?,?Vol. 86, pp. 307-320, 2017.
2) Qi-Ying Hu(Graduate student), Zhi-Lin Zhao(Graduate student),?Chang-Dong Wang*, Jian-Huang Lai. An Item Orientated Recommendation Algorithm from the Multi-view Perspective. Neurocomputing. Vol. 269, pp. 261-272, 2017.
3) Chao Chen(Undergraduate), Kun-Yu Lin(Undergraduate),?Chang-Dong Wang*, Jian-Bo Liu(Undergraduate), Dong Huang. CCMS: A Nonlinear Clustering Method Based on Crowd Movement and Selection. Neurocomputing. Vol. 269, pp. 120-131, 2017.
4) Zhi-Lin Zhao(Graduate student), Chang-Dong Wang*,?Kun-Yu Lin(Graduate student), and Jian-Huang Lai. Missing Value Learning. In Proc. of The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017, CCF B类), Pan Pacific,? Singapore, Nov. 6-10, 2017, pp. 2427-2430.
5) Zhi-Lin Zhao(Graduate student), Chang-Dong Wang*, Yuan-Yu Wan and Jian-Huang Lai. Recommendation in Feature Space Sphere. Electronic Commerce Research and Applications?,?Vol. 26, pp.109-118, 2017.
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2016:
1) Chang-Dong Wang, Jian-Huang Lai* and Philip S. Yu. Multi-View Clustering Based on Belief Propagation. IEEE Transactions on Knowledge and Data Engineering,?Vol. 28, No. 4, pp. 1007-1021, April, 2016.
2) Dong Huang, Jian-Huang Lai* and Chang-Dong Wang. Robust Ensemble Clustering Using Probability Trajectories. IEEE Transactions on Knowledge and Data Engineering,?Vol. 28, No. 5, pp. 1312-1326, May, 2016.
3) Yu-Meng Xu(Graduate student), Chang-Dong Wang*?and Jian-Huang Lai. Weighted Multi-view Clustering with Feature Selection. Pattern Recognition,?Vol. 53, pp. 25-35, 2016.
4) Dong Huang, Jian-Huang Lai* and Chang-Dong Wang. Ensemble Clustering Using Factor Graph. Pattern Recognition,?Vol. 50, pp. 131-142, 2016.
5) Dong Huang, Jian-Huang Lai* and Chang-Dong Wang. Ensembling Over-Segmentations: From Weak Evidence to Strong Segmentation. Neurocomputing,?Vol. 207, pp. 416-427, 2016.
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2015:
1) Cheng-Xu Ye, Wu-Shao Wen* and Chang-Dong Wang. Chinese-Tibetan Bilingual Clustering Based on Random Walk. Neurocomputing ,?Vol. 158, pp. 32–41, 2015.
2) Dong Huang, Jian-Huang Lai* and Chang-Dong Wang. Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis. Neurocomputing ,?Vol. 170, pp. 240-250, 2015.
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2014:
1) Chang-Dong Wang, Jian-Huang Lai* and Philip S. Yu. NEIWalk: Community Discovery in Dynamic Content-based Networks. IEEE Transactions on Knowledge and Data Engineering,?Vol. 26, No. 7, pp. 1734-1748, July, 2014.
2) Qing-Song Zeng, Jian-Huang Lai* and Chang-Dong Wang. Multi-Local Model Image Set Matching Based on Domain Description. Pattern Recognition,?Vol. 47, No. 2, pp. 697-704, 2014.
3) Xiu-Chun Xiao, Jian-Huang Lai* and Chang-Dong Wang. Parameter Estimation of the Exponentially Damped Sinusoids Signal Using a Specific Neural Network. Neurocomputing,?Vol. 143, pp. 331-338, 2014.
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2013:
1) Chang-Dong Wang, Jian-Huang Lai*, Ching Y. Suen and Jun-Yong Zhu. Multi-Exemplar Affinity Propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence,?Vol. 35, No. 9, pp.2223-2237, Sept. 2013.
2) Chang-Dong Wang, Jian-Huang Lai*, Dong Huang and Wei-Shi Zheng. SVStream: A Support Vector Based Algorithm for Clustering Data Streams. IEEE Transactions on Knowledge and Data Engineering,?Vol. 25, No. 6, pp. 1410-1424, June, 2013.
3) Chang-Dong Wang?and Jian-Huang Lai*. Position Regularized Support Vector Domain Description. Pattern Recognition,?Vol. 46, pp. 875-884, 2013.
4) Chang-Dong Wang, Jian-Huang Lai* and Philip S Yu. Dynamic Community Detection in Weighted Graph Streams. In Proc. of 2013 SIAM Int. Conf. on Data Mining (SDM’13,CCF B类), Austin, Texas, USA, May 2-4, 2013, pp. 151-161.
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2012:
1) Chang-Dong Wang, Jian-Huang Lai* and Jun-Yong Zhu. Graph-Based Multiprototype Competitive Learning and its Applications. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications & Reviews, Vol. 42, No. 6, pp. 934-946, Nov. 2012.
2) Chang-Dong Wang, Jian-Huang Lai* and Jun-Yong Zhu. Conscience Online Learning: An Efficient Approach for Robust Kernel-Based Clustering. Knowledge and Information Systems, Vol. 31, No. 1, pp. 79-104, 2012.
3) Jun Tan, Jian-Huang Lai*, Chang-Dong Wang, Wen-Xiao Wang and Xiao-Xiong Zuo. A New Handwritten Character Segmentation Method Based on Nonlinear Clustering. Neurocomputing,?Vol. 89, pp. 213-219, 2012.
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2011:
1) Chang-Dong Wang?and Jian-Huang Lai*. Energy Based Competitive Learning. Neurocomputing?,?Vol. 74, pp. 2265-2275, 2011.
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2010:
1) Chang-Dong Wang, Jian-Huang Lai* and Jun-Yong Zhu. A Conscience Online Learning Approach for Kernel-Based Clustering. In Proc. of the 10th Int. Conf. on Data Mining (ICDM'10, CCF B类), Sydney, Australia, Dec. 14-17, 2010, pp. 531–540. (Regular paper, acceptance rate 72/797=9%). This paper is selected as a honorable mention for the "Best Research Paper" award, ranking the 4th among 155 accepted papers.
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Book Chapters:
1) Chang-Dong Wang?and Jian-Huang Lai*. Nonlinear Clustering: Methods and Applications, in Book “Unsupervised Learning Algorithms”, edited by M. Emre Celebi and Kemal Aydin. Springer, 2016, pp. 253-302.