Kai Shu  

Gladwin Development Chair Assistant Professor

Department of Computer Science
College of Computing

Illinois Institute of Technology

Office: Stuart Building 226D

Mail: 10 W. 31st Street Room 226D, Chicago, IL 60616

E-mail: kshu at iit.edu
Twitter: @KaiShu0327

Google Scholar
Curriculum Vitae

 

News and Highlights

[9/2021] Received a seed grant from Illinois Institute of Technology, joint with IIT Chem. and Bio. Engineering.

[8/2021] Received a seed grant from Discovery Partners of Institute, joint with UIC Medicine.

[8/2021] Greatful to receive a NSF grant to study the intersection of HPC and AI.

[8/2021] One journal paper is accepted in IEEE Transactions on Neural Networks and Learning Systems (TNNLS).

[8/2021] Invited to serve as a PC member for AAAI'22, WWW'22.

[7/2021] Interviewed by the George Washington Today on recent disinformation research.

[5/2021] Invited to serve as a PC member for CIKM 2021, WSDM 2022.

[5/2021] Two papers are accepted in SIGKDD 2021.

[4/2021] Invited to serve as a PC member for EMNLP 2021.

[4/2021] One paper is accepted in SIGIR 2021.

[3/2021] Happy to participate in a panel on social ethics with Women and Gender Minorities in S.T.E.M. (WiSTEM) at IIT.

[3/2021] Invited to participate in the panel on AI bias and fairness.

[3/2021] Research is featured in collegegazette: The 10 Best Technology Colleges in the US.

[2/2021] Invited to serve as the PC Chair for ASONAM 2021.

[1/2021] Received the IDDP Research Fellowship Award from GWU to support research on disinformation.

[1/2021] Computer Science Professor to Expand Award-Winning Fake News Research

[1/2021] Invited to serve on the editorial board of SNAM journal.

[12/2020] Invited to serve as a PC member for KDD 2021, ACL 2021.

Sponsors





Kai Shu is a Gladwin Development Chair Assistant Professor in the Department of Computer Science at Illinois Institute of Technology since Fall 2020. His research lies in machine learning, data mining, social computing with applications such as disinformation, education, and healthcare. He obtained his PhD in Computer Science at Arizona State University in July 2020, under the supervision of Professor Huan Liu. He was the recipients of 2020 ASU Engineering Dean's Dissertation Award, 2020/2015 ASU CIDSE Doctoral Fellowship, 2018 SBP Disinformation Challenge Winner. He interned at Microsoft Research AI, Yahoo Research and HP Labs.

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.

Research Interests 

  • AI for social good: disinformation/misinformation, user privacy, security.
  • Responsible AI systems: interpretable, robust, fair.
  • Learning with weak data: weak supervision, data generation, meta learning, adversarial learning.
  • Representation Learning: NLP/graph, multi-modality learning, domain adaptation.

Detecting Fake News on Social Media
    Table of Contents
    Order Hard-cover or PDF
    Tools and Datasets
     		     Disinformation, Misinformation and Fake News in Social Media     Table of Contents
    An Introductory Chapter
    [A Chinese Introduction Blog][Vito]

Recent Publications [Full List]

  • Cross-domain Anomaly Detection on Attributed Networks. [PDF]
    Kaize Ding, Kai Shu, Xuan Shan, Jundong Li, and Huan Liu
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021.
  • Temporally Evolving Graph Neural Network for Fake News Detection. [PDF]
    Chenguang Song, Kai Shu, and Bin Wu
    Information Processing and Management (IPM), Elsevier, 2021.
  • Causal Understanding of Fake News Dissemination on Social Media. [PDF]
    Lu Cheng, Ruocheng Guo, Kai Shu, and Huan Liu.
    Proceedings of 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021)
  • Labeled Data Generation with Inexact Supervision. [PDF]
    Enyan Dai, Kai Shu, Yiwei Sun, and Suhang Wang.
    Proceedings of 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021)
  • User Preference-aware Fake News Detection. [PDF][Code][PyG Example][DGL Example][Data]
    Yingtong Dou, Kai Shu, Congying Xia, Philip Yu, and Lichao Sun.
    Proceedings of 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021) (Short paper)
  • Mining Dual Emotion for Fake News Detection. [PDF]
    Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, and Kai Shu.
    Proceedings of 30th The Web Conference (WWW 2021)
  • Fact-enhanced Synthetic News Generation. [PDF][Poster]
    Kai Shu*, Yichuan Li*, Kaize Ding, and Huan Liu.
    Proceedings of The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021)
  • 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)
  • 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)
    Media coverage: [Techxplore Today]
  • 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]
  • 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).
  • Fake News Detection on Social Media: A Data Mining Perspective. [PDF][Data]
    Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu.
    SIGKDD Explorations, 2017.