Jie Tang (Tang, Jie) 唐 杰

Professor, IEEE Fellow
Associate Chair
NSFC Distinguished Young Scholar

Director of Tsinghua-CAE Joint Research Center for Knowledge & Intelligence

Knowledge Engineering Group (KEG),
Department of Computer Science,
Tsinghua University


Office: 1-308, FIT Building, Tsinghua University, Beijing, 100084. China PR.

E-Mail:  jietang at tsinghua . edu . cn

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I am a Full Professor and the Associate Chair of the Department of Computer Science and Technology of Tsinghua University. I obtained my Ph.D. in DCST of Tsinghua University in 2006. My research interests include artificial intelligence, data mining, social networks , machine learning and knowledge graph, with an emphasis on designing new algorithms for mining social and knowledge networks.

I have been visiting scholar at Cornell University (working with Prof. John Hopcroft and Jon Kleinberg), University of Southampton (working with Dame Wendy Hall), KU Leuven (working with Marie-Francine Moens), University of Illinois at Urbana-Champaign (short term, working with Jiawei Han), Chinese University of Hong Kong (working with Jeffrey Yu), and Hong Kong University of Science and Technology (working with Qiong Luo). During my graduate career, I have been an intern at NLC group of Microsoft Research Asia from 2004 to 2005. I also have attended the internship program of IBM China Research Lab in 2004.

I have published more than 200 journal/conference papers and hold 20 patents. I served as PC Co-Chair of CIKM’16, WSDM’15, Associate General Chair of KDD’18, and Acting Editor-in-Chief of ACM TKDD, Editors of IEEE TKDE, IEEE TBD, and ACM TIST. I am leading the project AMiner.org for academic social network analysis and mining, which has attracted more than 10 million independent IP accesses from 220 countries/regions in the world. I was honored with the UK Royal Society-Newton Advanced Fellowship Award, CCF Young Scientist Award, NSFC for Distinguished Young Scholar, and KDD’18 Service Award.

I am looking for highly-motivated students to work with me on the exciting area of social network, data mining, and machine learning. I also have a few open Postdoctoral Positions to investigate underlying theory and algorithms in data mining, social networks, machine learning, and knowledge graph. If you want me to write a recommendation letter for you, please first read this.

*New Cognitive Graph
*New From Knowledge Graph to Cognitive Graph
*New Next AI'10
*New Our ArnetMiner paper has been awarded the (SIGKDD Test-of-Time Award)!

I am recently working on representation learning on networks, social network mining, academic knowledge graph and AI driven MOOCs.

Representation Learning on Networks   [GNN&Self-supervised Learning] [人工智能下一个十年] [HPC Online Talk on GNN & Reasoning] [ADL Online Talk on GNN & Reasoning] [图神经网络及认知推理] [GNN&Cognitive Graph] [Cognitive Graph] [GNN&Reasoning] [Talk at Brain-and-AI] [Talk at WeBank] [Keynote at CNCC'19 Forum] [Keynote at ASONAM'19] [Keynote at KDD'19 DL Day] [Keynote at KDDFeed'19] [Talk at KDD China'19] [Tutorial at WWW'19]  [Tutorial]
The goal is to automatically encode network structure into low-dimensional space (embeddings), using techniques such as neural networks.
    We theoretically prove that recent models such as DeepWalk, LINE, PTE, and node2vec can be unified into the matrix factorization framework with closed forms. we present a new method NetMF, which significantly outperforms DeepWalk and LINE for conventional network mining tasks (Qiu et al., WSDM'18). Based on the learned representations, we further propose a multi-head attention network for predicting user behavior (Qiu et al., KDD'18) and NetSMF for large scale networks (Qiu et al., WWW'19). Further, we incorporate user feedback into the prediction and propose a bandit learning model (Qi et al., NIPS'18).

Datasets and codes: [NetMF]  [DeepInf]
Social Network Mining  [IC2S2'19 Tutorial]  [KDD'18 Tutorial]   [Book]  [Survey]
Online social networks already become a bridge to connect our physical daily life with the virtual information space, producing huge volume of networked data. We aim to understand the mechanism underlying the dynamics of social interaction between users and information diffusion in the network.
   We propose a new method Topical Affinity Propagation (TAP) to model the topic-level social influence (Tang et al., KDD'09), conformity influence analysis (Tang et al., KDD'13), structural influence (Zhang et al., AAAI'17), inferring social tie (Tang et al., WSDM'12, Tang et al., TOIS'16), and user demographics (Dong et al., KDD'14). At the macro-level, we focus on mining top-k structural hole spanners, who control the information diffusion across different communities (Lou and Tang, WWW'13) and following link diffusion (Zhang et al., TKDE'15).

Datasets and codes: [Topic-Influence [Structural hole] [Datasets for SNA]
Academic Knowledge Graph   [Tutorial]  [System]   [Career Trajectory]
We focus on building large-scale knowledge graph, particularly for scholarly data. In this research, we work on various topics including Expert Finding ( Qian et al., IJCAI'18; Tang et al., Machine Learning J'18 ) Career Trajectory Mining ( Wu et al., IJCAI'18 ) Social Recommendation (Tang et al., KDD'12), Information/knowledge Integration (Zhong et al., SIGMOD'09, Wang et al., WWW'12, Wang et al., IJCAI'13), Name Disambiguation (Tang et al., TKDE'12, Zhang et al., KDD'18 ) Summarization (Yang et al., SIGIR'11), Content Alignment (Hou et al., IJCAI'13; Hou et al., TOIS'17 ), Similarity Search (Zhang et al., TOIS'17).
   Based on these research, we have developed a system AMiner (ArnetMiner) (Tang et al., KDD'08 (SIGKDD Test-of-Time Award); Tang et al., TKDD'10), for academic search and mining. The system has over 136 million researchers and 200 million papers. Since 2006, the system has attracted over 10 million independent IP accesses from more than 220 countries/regions.

Datasets and codes: [AMiner Dataset]  [Open Academic Graph]
AI Driven MOOCs   [Tutorial]  [System]
Massive Open Online Courses (MOOCs), which collect complete records of all student interactions in an online learning environment, offer us an unprecedented opportunity to analyze students’ learning behavior at a very fine granularity than ever before. Using dataset from xuetangX, one of the largest MOOCs from China, we analyze key factors that influence students’ engagement in MOOCs and study to what extent we could infer a student’s learning effectiveness ( Qiu et al., WSDM'16 ), extract concept relationships ( Pan et al., ACL'17 ), predict students' dropouts (Feng et al., AAAI'19). XuetangX has attracted over 10 million registered users.

Datasets and codes: [MoocData]


Social Network / Data Mining / Machine Learning

Knowledge Graph



TALKS   Go Top

  • Keynote about Representation Learning on Networks to be given at ASONAM'19
  • Keynote about AI driven MOOCs given at EDM'17
  • Invited talk about academic knowledge graph given at WSDM'16
  • Keynote about social network mining given at SocInfo'15


Post Doc

  • Yunpeng Gao
  • Yunpeng Gao
  • Jing Xu (Graduated)
  • Sha Yuan (Leader @ BAAI)
  • Yutao Zhang(CTO or Recurrent.ai)
  • Huaiyu Wan (Assistant Professor @ Beijing Jiaotong University)
  • Daifeng Li (Associate Professor @ Sun Yat-Sen University)

PhD Students

  • Chenghui Zhang
  • Jiezhong Qiu
  • Kan Wu
  • Wenzheng Feng
  • Fanjin Zhang
  • Aoao Feng
  • Ming Ding
  • Xu Zou
  • Mengyang Sun
  • Ming Zhou
  • Yukuo Cen
  • Zhengxiao Du
  • Xu Cheng
  • Yu Han (Graduated, Alibaba)
  • Yutao Zhang (Graduated)
  • Yang Yang (Assistant Professor @ Zhejiang University)
  • Jing Zhang (Assistant Professor @ Renmin University)

Master Students

  • Qingyang Zhong
  • Kun Zhang
  • Pengcheng Wang
  • Alexandre Boulenger
  • Yonglin Tan
  • Xiaoyan Zhang
  • Qingsong Lv
  • Jialin Zhao (graduated in 2021)
  • Gan Luo (graduated in 2021)
  • Gan Luo (graduated in 2021)
  • Vincent Couverchel (graduated in 2019)
  • Edaurd Ergenzinger
  • Valentin Kao (graduated in 2019)
  • Da Yin (graduated in 2021)
  • Yan Wang (M) (graduated in 2020)
  • Runzhi Gao (graduated in 2021)
  • Yan Wang (F) (graduated in 2019)
  • Jie Zhang (graduated in 2019)
  • Zhenhuan Chen (graduated in 2019)
  • Yifeng Zhao (graduated in 2019)
  • Mogford Michael (graduated in 2019)
  • Ben Keller (graduated in 2019)
  • Rytis Kumpa (graduated in 2019)
  • Zhengyang Song (graduated in 2019)
  • Ziwu Sun (graduated in 2018)
  • Chaoyang Li (graduated in 2018)
  • Xiaochen Wang (graduated in 2018)
  • Fang Zhang (graduated in 2018)
  • Tianji Zhao (graduated in 2017)
  • Hong Yang (graduated in 2017, Assistant Professor @ Qinghai University)
  • Zhanpeng Fang (graduated in 2016, Google US)
  • Mu Yang (graduated in 2016, CTO @ Face++)
  • Wenbin Tang (graduated in 2013, CTO @ Face++)
  • Marcel Lee (graduated in 2013)
  • Lenin Mookiah (graduated in 2013, PhD @ Tennessee Technological University)
  • Yongliang Zhu (graduated in 2012, work @ Fuzhou)
  • Wenyuan Xu (graduated in 2012)
  • Zi Yang (graduated in 2011, now PhD at CMU)
  • Limin Yao (co-advisor, graduated in 2008, @Twitter)
  • Duo Zhang (co-advisor, graduated in 2007, @Twitter...)

Undergraduate Students
Here list those working with me for at least 8 months. I may miss some. Please let me know if you found.
If you want me to write a recommendation letter for you, please first read this.

  • Current
    • xx
  • Graduated in 2021
    • Xiao Liu
    • Zhenyu Hou
  • Graduated in 2020
    • Zhengxiao Du
    • Qinyang Zhong
    • Yihan Wang
    • Yuntong Xie
    • Aohan Zeng
    • Leijie Wang
    • Zhenyu Hou
    • Jiaqi Wang
    • Han Yu
    • Xiao Liu
    • Kaiyuan Xu
  • Graduated in 2019
    • Peiran Yao
    • Qibin Chen
    • Yonglin Tan
    • Minda Hu
    • Zhicong Fang
    • Zhuoyue Xiao
    • Yuhui Ding
    • Jianan Yao
    • Chenyu Wang
    • Junlin Song
    • Xi Chen
    • Tong Xiao
  • Graduated in 2018
    • Da Yin
    • Peiran Yao
    • Gan Luo
    • Ming Ding
    • Yijian Qin
    • Xinyang Zhang
    • Yukuo Cen
    • Yuanhang Zheng
    • Lemeng Wu
    • Yifan Liu
  • Graduated in 2017
    • Xiaotao Gu (PhD @ UIUC)
    • Yujie Qian (PhD @ MIT)
    • Shaoxiong Wang (PhD @ MIT)
    • Miao Ren (MS @ CMU)
    • Qian Zhang (MS @ UCLA)
    • Sida Gao (MS @ CMU)
  • Graduated in 2016
    • Yuan Yuan (PhD @ MIT)
    • Honghao Wei (MS @ Stanford)
    • Fei Xia (PhD @ Stanford)
    • Jingqing Zhang (PhD @ Imperial College London)
    • Tianrun Li
    • Jiaqi Ma (PhD @ Mitch)
    • Shan Han
    • Yu Xia (PhD @ MIT)
  • Graduated in 2015
    • Zhilin Yang (PhD at CMU)
    • Xinyu Zhou (@Face++)
    • Weiran He
    • Cong Ma (PhD at Princeton)
    • Xunkai Zhang (@Google US)
    • Zhelun Wu
    • Wei Huang (PhD @ Tsinghua)
    • Ye Cao
  • Graduated in 2014
    • Qianru Zhu (MS at CMU)
    • Yang Liu
    • Yihan Sun (PhD at CMU)
    • Yaohui Ye
    • Ning Jiang (U Mitch)
    • Chenran Guan (MS at CMU)
    • Jingyuan Liu
  • Graduated in 2013
    • Honglei Zhuang (PhD at UIUC)
    • Sen Wu (MS at Stanford)
    • Zhanpeng Fang (MS at THU)
    • Liangtao Zhang (Work)
    • Bo Ma (MS at CMU)
    • Wei Chen (MS at CMU)
  • Graduated in 2012
    • Xiaowen Ding (MS at CMU)
    • Yanting Zhao (PhD at Columbia U.)
    • Yu Zhao (MS at CMU)
    • Cheng Chen (PhD at MIT)
    • Lin Xu
    • Hang Su (PhD at Georgia Tech.)
    • Tian Li (Dartmouth College)
    • Yuxiao Dong (PhD at Notre Dame U.)
    • Yubing Dong (MS at USC)
  • Graduated in 2011
    • Yuan Zhang (PhD at MIT)
    • Lixin Shi (PhD at MIT)
    • Tao Lei (PhD at MIT)
    • Xuezhi Wang (PhD at CMU)
    • Yuan Du (MS at Columbia U.)
    • Yiran Chen (PhD at MSU)
    • Jingyi Guo (PhD at UMass)
    • Rui Du (MS at CMU)
    • Wenjing Yu (MS at USC)
    • Haoquan Guo (MS at NYU)
  • Graduated in 2010
  • Graduated in 2009
    • Chi Wang (PhD at UIUC)
    • Fengjiao Wang (PhD at UIC)
  • Graduated before 2009
    • Liu Liu (CMU and now Google, US)
    • Yize Li (UCSC, and now StumbleUpon, US)

Other students collaborated with me

  • Ming Yin (2011, PhD at Havard)
  • Yajie Miao (2011, PhD at CMU)

Last updated date: June 16, 2019, by Jie Tang.