Ph.D.
Brickyard Suit, 699 S Mill Ave, Tempe, AZ 85281

E-mail: kai.shu at asu.edu
Twitter: @KaiShu0327

Google Scholar
Curriculum Vitae

 

News and Highlights

[7/2020] Two papers are accepted in CIKM 2020.

[7/2020] An algorithm to detect fake news - an interview together with my advisor Prof. Huan Liu.

[7/2020] Invited to serve as a PC member for SDM 2021.

[6/2020] Invited to speak at the 1st WHO Infodemiology Conference. [Where is Kai?]

[6/2020] Received the Best Reviewer Award in ICWSM 2020.

[6/2020] Invited to serve as a PC member for WSDM 2021.

[6/2020] Two papers are accepted in ECML-PKDD 2020.

[5/2020] FakeNewsTracker is among the Top ML Projects To Fight Fake News Fatigue During COVID-19.

[5/2020] One paper is accepted in IEEE Intelligent Systems. Media coverage: Venturebeat, Digital Information World, Report Door.

[5/2020] Invited to deliver research talks (remotely) in Google Research, Illinois Tech, and ICT, CAS.

[5/2020] Co-presented (remotely) a keynote talk in the Workshop on Data Science for Fake News at PAKDD 2020.

[4/2020] One paper is accepted in SIGIR'20.

[4/2020] Microsoft claims its AI framework spots fake news better than state-of-the-art baselines.

[3/2020] Received the ASU CIDSE Doctoral Fellowship.

[3/2020] The Springer book is officially out! - Disinformation, Misinformation, and Fake News in Social Media. [ToC]

[11/2019] One paper is accepted in ICWSM'20

[9/2019] Fires in the Amazon: Arizona researchers determine what’s true, what’s not, an interview with Arizona PBS Cronkite News on AI and fake news [video].

[9/2019] AI Squares Off Against Fake News. -- feature news about KDD'19 tutorial.

 

Research Experience

  
[Aug. 2015 - Present]
Research Assistant at DMML, Arizona State University, Tempe, AZ, USA.

  
[May 2019 - August 2019]
Research Intern at Microsoft AI Research, Redmond, WA, USA.

  
[May 2018 - August 2018]
Research Intern at Yahoo! Research, Sunnyvale, CA, USA.

  
[Jul. 2014 - June 2015]
Research visiting student at ICT, Chinese Academy of Sciences, Beijing, China.

  
[Mar. 2012 - Sept. 2013]
Research Intern at HP Labs China, Beijing, China.

This website will no longer be maintained. You will be redirected to my new homepage in 5 seconds. If not, please visit http://www.cs.iit.edu/~kshu/.

Kai Shu obtained his PhD of Computer Science in Summer 2020 at Arizona State University, under the supervision of Professor Huan Liu. He is a research assistant at the Data Mining and Machine Learning Lab (DMML). In general, his research lies in machine learning, data mining, social computing, and applications in disinformation, education, healthcare. His current interests include: (1) Data science for societal good: disinformation/fake news detection, user privacy, security; (2) Intelligent learning systems: interpretable, robust, fair; (3) Learning with limited and noisy data (weak supervision, data generation, meta learning, few-shot learning); (4) Representation Learning: feature learning for text/image/network, multi-modality fusion, domain adaptation.

I will be a Gladwin Development Chair Assistant Professor in the Department of Computer Science at Illinois Institute of Technology (Illinois Tech) since Fall 2020.

I am actively looking for self-motivated PhD students to conduct research in the area of data mining, machine learning and social media mining. Interested students please feel free to drop me an email with your CV and transcript.

CALL FOR PAPERS:

 

Selected Publications [Full List]

  • Early Detection of Fake News with Multi-source Weak Social Supervision. [PDF]
    Kai Shu, Guoqing Zheng, Yichuan Li, Subhabrata Mukherjee, Ahmed Hassan Awadallah, Scott Ruston, and Huan Liu.
    Proceedings of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML-PKDD 2020)
  • Detecting Fake News with Weak Social Supervision. [PDF]
    Kai Shu, Ahmed Hassan Awadallah, Susan Dumais, and Huan Liu.
    IEEE Intelligent Systems, 2020.
  • Learning with Weak Supervision for Email Intent Detection. [PDF]
    Kai Shu, Subhabrata Mukherjee*, Guoqing Zheng*, Ahmed Hassan Awadallah, Milad Shokouhi and Susan Dumais.
    Proceedings of 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020)
  • Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation. [PDF]
    Kai Shu, Deepak Mahudeswaran, Suhang Wang, and Huan Liu.
    Proceedings of 14th the International AAAI Conference on Web and Social Media (ICWSM 2020)
  • Using Synthetic Clickbaits to Improve Prediction and Distinguish between Bot-Generated and Human-Written Headlines. [PDF]
    Thai Le, Kai Shu, Maria D. Molina, Dongwon Lee, S. Shyam Sundar, and Huan Liu.
    Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)
  • dEFEND: Explainable Fake News Detection. [PDF][Code]
    Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu.
    Proceedings of 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019)
  • Unsupervised Fake News Detection on Social Media: A Generative Approach. [PDF][Code]
    Shuo Yang, Kai Shu, Suhang Wang, Renjie Gu, Fan Wu, and Huan Liu.
    Proceedings of 33rd AAAI Conference on Artificial Intelligence (AAAI 2019) .
  • Beyond News Content: The Role of Social Context for Fake News Detection.
    [PDF][Poster]
    Kai Shu, Suhang Wang, and Huan Liu.
    Proceedings of 12th ACM International Conference on Web Search and Data Mining (WSDM 2019).
    Media coverage: [KDnuggets] [The Morning Paper]
  • FakeNewsTracker: A Tool for Fake News Collection, Detection, and Visualization. [PDF]Poster][Demo]
    Kai Shu, Deepak Mahudeswaran, and Huan Liu.
    Computational and Mathematical Organization Theory, 2019 (CMOT 2019). Best of SBP
  • Studying Fake News via Network Analysis: Detection and Mitigation. [PDF][A Course Blog from Cornell University]
    Kai Shu, H. Russell Bernard, and Huan Liu.
    Lecture Notes in Social Network (LNSN), Springer Press, 2018.
  • Deep Headline Generation for Clickbait Detection. [PDF][Slides]
    Kai Shu, Suhang Wang, Thai Le, Dongwon Lee, and Huan Liu.
    The 2018 IEEE International Conference on Data Mining (ICDM 2018) (Regular Paper)
  • Multimodal Fusion of Brain Networks with Longitudinal Couplings. [PDF]
    Wen Zhang, Kai Shu, Suhang Wang, Huan Liu, and Yalin Wang.
    21th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018).
  • CrossFire: Cross Media Joint Friend and Item Recommendations. [PDF][Slides][Poster]
    Kai Shu, Suhang Wang, Jiliang Tang, Yilin Wang, and Huan Liu.
    Proceedings of 11th ACM International Conference on Web Search and Data Mining (WSDM 2018).
  • What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation. [PDF]
    Suhang Wang, Yilin Wang, Jiliang Tang, Kai Shu, Suhas Ranganath, and Huan Liu.
    Proceedings of 26th International World Wide Web Conference (WWW 2017).
  • User Identity Linkage across Online Social Networks: A Review. [PDF]
    Kai Shu, Suhang Wang, Jiliang Tang, Reza Zafarani, and Huan Liu.
    SIGKDD Explorations, 2016.
  • Multi-Label Informed Feature Selection. [PDF]
    Ling Jian, Jundong Li, Kai Shu, and Huan Liu.
    Proceedings of 25th International Joint Conference on Artificial Intelligence (IJCAI 2016).
  • Deal or Deceit: Detecting Cheating in Distribution Channels. [PDF][Slides]
    Kai Shu, Ping Luo, Li Wan, Peifeng Yin, and Linpeng Tang.
    Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM 2014).