KAN Min Yen

Associate Professor
Assistant Dean, Undergraduate Studies

  • Ph.D. (Computer Science, Columbia University, 2002)
  • M.Sc. (Computer Science, Columbia University, 1998)
  • B.S. (Computer Science, Columbia University, 1996)

Min-Yen Kan (BS;MS;PhD Columbia Univ.) is an associate professor at the National University of Singapore. He is a senior member of the ACM and a member of the IEEE. Currently, he is an associate editor for the journal "Information Retrieval" and is the Editor for the ACL Anthology, the computational linguistics community's largest archive of published research. His research interests include digital libraries and applied natural language processing. Specific projects include work in the areas of scientific discourse analysis, full-text literature mining, machine translation and applied text summarization.


  • Natural Language Processing
Artificial Intelligence
  • Machine Learning


  • Natural Language Processing

  • Digital Libraries

  • Applied Machine Learning

  • Information Retrieval

  • Human-Computer Interaction



Web, Information Retrieval, Natural Language Processing Group (WING)

Min leads WING, a group of postgraduate and undergraduate researchers examining issues in digital libraries, information retrieval and natural language processing research. Find out more at http://wing.comp.nus.edu.sg.


Student Submission Integrity Diagnosis (SSID)

SSID is an instructor-centric source code plagiarism detection system (i.e., for programming assignments). It aims to streamline the checking process and helps instructors manage plagiarism detection workflows.


  • Kishaloy Halder, Lahari Poddar and Min-Yen Kan 2017 Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach. In Proceedings of 8th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis WASSA '17. September 2017. Copenhagen, Denmark.
  • Wenqiang Lei, Xuancong Wang, Meichun Liu, Ilija Ilievski, Xiangnan He and Min-Yen Kan 2017 SWIM: A Simple Word Interaction Model for Implicit Discourse Relation Recognition. In Proceedings of the International Joint Conference on Artificial Intelligence IJCAI '17, August 2017, Melbourne, Australia.
  • Muthu Kumar Chandrasekaran, Carrie Demmans Epp, Min-Yen Kan and Diane Litman 2017. Using Discourse Signals for Robust Instructor Intervention Prediction. In Proceedings of the Thirty-First AAAI conference on Artificial Intelligence AAAI-17, San Francisco, USA, 3415-3421, AAAI.
  • Tao Chen, Xiangnan He and Min-Yen Kan 2016. Context-aware Image Tweet Modelling and Recommendation. In Proceedings of the 24th ACM International Conference on Multimedia MM'16, Amsterdam, The Netherlands. 15-19 Oct.
  • Xiangnan He, Hanwang Zhang, Min-Yen Kan and Tat-Seng Chua 2016.Fast Matrix Factorization for Online Recommendation with Implicit Feedback. In Proceedings of Special Interest Group on Information Retrieval SIGIR '16. Pisa, Italy. July 17-21.
  • Bang Hui Lim, Dongyuan Lu, Tao Chen and Min-Yen Kan 2015.#mytweet via Instagram: Exploring User Behaviour across Multiple Social Networks. In Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM '15, Paris, France.
  • Tao Chen, Naijia Zheng, Yue Zhao, Muthu Kumar Chandrasekaran, Min-Yen Kan 2015. Interactive Second Language Learning from News Websites. In Proceedings of 2nd Workshop on Natural Language Processing Techniques for Educational Applications NLP-TEA '15, Beijing, China.
  • Muthu Kumar Chandrasekaran, Min-Yen Kan, Kiruthika Ragupathi and Bernard C. Y. Tan 2015. Learning instructor intervention from MOOCforums: Early Results and Issues. In Proceedings of Education Data Mining EDM '15, Madrid, Spain.


  • Vannevar Bush Best Paper Award, 2012, Joint Conference on Digital Libraries (JCDL 2012)

  • 1st place in automated ROUGE measures among all teams, TAC 2011 Guided Summarization task, Text Analysis Conference

  • Senior Member ACM

  • Best Paper Award, 2019, Conference on Information and Knowledge Management (CIKM 2019)


Machine Learning
Natural Language Processing