唐傑（清華教授）簡介資料(個人簡曆圖片)

教育背景

工學博士 (計算機科學與技術), 清華大學, 中國, 2006.

研究領域

人工智能

數據挖掘

社交網絡

機器學習

知識圖譜

研究概況

研究興趣主要包括人工智能、數據挖掘、社交網絡、機器學習以及知識圖譜。

主要創新性研究包括：

1）社會影響力分析：提出基於話題的社會網絡影響力模型，針對大規模社會網絡進行用戶級別的微觀建模，自動計算用戶之間基於不同話題層次的影響力強度，為定量化、細粒度的網絡影響力分析給出理論基礎，部分解決了影響力最大傳播模型的輸入假設問題。

2）社會網絡用戶行為建模：將社會網絡的基礎理論(結構平衡理論、兩階段傳播理論、結構洞理論等) 融入概率因子圖模型中對社會網絡關係和強度進行定量描述，實現了社會網絡關係挖掘的統一學習算法。

3）網絡行為建模和影響力分析，提出了針對社會網絡的微觀動態分析方法，並首次提出了社會影響力的量化分析方法，以及社會網絡行為和社會影響力關聯關係的分析方法。

4）應用上述研究成果，研發了完全自主知識產權的科技情報大數據挖掘與服務平台AMiner。係統2006年上線以來，吸引了來自全球220個國家/地區的1000多萬次獨立IP訪問。

研究課題

國家自然科學基金課題: 統一的語義內容標注模型研究 (2008-2010);

國家自然科學基金重點課題: 麵向Web的社會網絡理論與方法研究 (2010-2013);

863課題: 基於概率圖模型的異構XML數據集成與檢索 (2009-2010);

IBM國際合作項目: 社會網絡搜索和挖掘 (2007-2011);

Nokia國際合作項目: 基於移動終端的本體場景建模和管理 (2009-2011).

獎勵與榮譽

出版著作

[1] Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, and Jie Tang. NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization. In Proceedings of the Web Conference 2019 (WWW’19) (accepted).

[2] Yukuo Cen, Jing Zhang, Gaofei Wang, Yujie Qian, Chuizheng Meng, Zonghong Dai, Hongxia Yang, and Jie Tang. Trust Relationship Prediction in Alibaba E-Commerce Platform. IEEE Transaction on Knowledge and Data Engineering (TKDE), 2019 (accepted).

[3] Wenzheng Feng, Jie Tang, Tracy Xiao Liu, Shuhuai Zhang, and Jian Guan. Understanding Dropouts in MOOCs. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19).

[4] Yi Qi, Qingyun Wu, Hongning Wang, Jie Tang, and Maosong Sun. Bandit Learning with Implicit Feedback. In Proceedings of the Thirty-Second Annual Conference on Neural Information Processing Systems (NIPS’18).

[5] Yutao Zhang, Fanjin Zhang, Peiran Yao, and Jie Tang. Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop. In Proceedings of KDDu201918, pages 1002-1011.

[6] Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. DeepInf: Social Influence Prediction with Deep Learning. In Proceedings of the Twenty-Forth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’18).

[7] Hong Huang, Yuxiao Dong, Jie Tang, Hongxia Yang, Nitesh V. Chawla, and Xiaoming Fu . Will Triadic Closure Strengthen Ties in Social Networks? ACM Transactions on Knowledge Discovery from Data (TKDD), 2018, Volume 12 Issue 3, Article No. 30.

[8] Yang Yang, Jie Tang, and Juanzi Li. Learning to Infer Competitive Relationships in Heterogeneous Networks. ACM Transactions on Knowledge Discovery from Data (TKDD), 2018, Volume 12 Issue 1, Article No. 12

[9] Yujie Qian, Jie Tang, and Kan Wu. Weakly Learning to Match Experts in Online Community. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18).

[10] Kan Wu, Jie Tang, and Chenhui Zhang. Where have you been? Inferring career trajectory from academic social network. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18).

[11] Tiancheng Shen, Jia Jia, Guangyao Shen, Fuli Feng, Xiangnan He, Huanbo Luan, Jie Tang, Tatseng Chua, Thanassis Tiropanis, and Wendy Hall. Cross-Domain Depression Detection via Harvesting Social Media. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18).

[12] Xiaotao Gu, Hong Yang, Jie Tang, Jing Zhang, Fanjin Zhang, Debing Liu, Wendy Hall, and Xiao Fu. Profiling Web Users Using Big Data. Social Network Analysis and Mining (SNAM), accepted.

[13] Hong Huang, Yuxiao Dong, Jie Tang, Hongxia Yang, Nitesh V. Chawla, and Xiaoming Fu. Will Triadic Closure Strengthen Ties in Social Networks? ACM Transactions on Knowledge Discovery from Data (TKDD), accepted.

[14] Yutao Zhang, Robert Chen, Jie Tang, Jimeng Sun, and Walter Stewart. LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity. In Proceedings of the Twenty-Third ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’17), pages 1315-1324.

[15] Jing Zhang, Jie Tang, Yuanyi Zhong, Yuchen Mo, Juanzi Li, Guojie Song, Wendy Hall, and Jimeng Sun. StructInf: Mining Structural Influence from Social Streams. In Proceedings of AAAI’17, pages 73-79.

[16] Jie Tang. Computational Models for Social Network Analysis: A Brief Survey. In Proceedings of WWW’17, pages 921-925.

[17] Yu Han, Jie Tang, Hao Ye, and Bo Chen. Who to Invite Next? Predicting Invitees of Social Groups. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’17), pages 921-925.

[18] Liangming Pan, Chengjiang Li, Juanzi Li, and Jie Tang. Prerequisite Relation Learning for Concepts in MOOCs. In Proceedings of the 55th Annual Meeting of the Association of Computational Linguistics (ACL’17), pages 1447-1456.

[19] Jie Tang. Computational Models for Social Network Analysis: A Brief Survey. In Proceedings of the Twenty-Sixth World Wide Web Conference (WWW’17), pages 921-925.

[20] Jie Tang and Wendy Hal. Cross-domain Ranking via Latent Space Learning. In Proceedings of AAAI’17.

社會兼職

ACM TKDD執行主編；

IEEE TKDE、 ACM TIST、 IEEE TBD、 Science China編委；

Web Intelligence 2010: 程序委員會副主席 (2010);

KDD-LDMTA 2010, ICDM-LDMTA 2009-2010, WWW-SWSM 2008, CIKM-SWSM 2009: 聯合主席 (2008-2010);

KDD 2010, SIGIR 2009-2010, WWW 2010, ACL 2010, COLING 2010: 程序委員會委員 (2009-2010).

本文到此結束，希望對大家有所幫助呢。