Sundong Kim (김선동)

Senior Researcher, IBS, Republic of Korea
E-mail: sundong (at)

[C.V.] [LinkedIn]

About Me

I am a senior researcher in the Data Science Group at Institute for Basic Science, working with Meeyoung Cha. My research interests include predictive analytics with real-world data with temporal, imbalanced, and incomplete in nature. Currently, I am leading a Customs Data Analytics project in collaboration with the World Customs Organization. I obtained my Ph.D. in Knowledge Service Engineering at Korea Advanced Institute of Science and Technology (KAIST), advised by Jae-Gil Lee. My thesis research focused on customer revisit prediction using in-store sensor data.

Selected Publications (full list)

  • DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection
    Sundong Kim*, Yu-Che Tsai*, Karandeep Singh, Yeonsoo Choi, Etim Ibok, Cheng-Te Li, Meeyoung Cha
    KDD 2020 (Applied Data Science)
    [Link] [PDF] [Slides] [Presentation] [Github] [Project] [Promotional video] [WCO News] [Press]

  • Revisit Prediction by Deep Survival Analysis
    Sundong Kim, Hwanjun Song, Sejin Kim, Beomyoung Kim, Jae-Gil Lee
    PAKDD 2020
    [Link] [PDF] [Slides] [Video]

  • A Systematic Framework of Predicting Customer Revisit with In-Store Sensors
    Sundong Kim, Jae-Gil Lee
    Knowledge and Information Systems 62(3), 2020
    [Link] [PDF]

  • Utilizing In-Store Sensors for Revisit Prediction
    Sundong Kim, Jae-Gil Lee
    ICDM 2018
    (Selected as one of the best papers in ICDM 2018)
    [Link] [PDF] [Slides] [Poster] [Video] [Github]

  • Friend Recommendation with a Target User in Social Networking Services
    Sundong Kim
    ICDE 2015 (Ph.D. Symposium)
    [Link] [PDF] [Slides]

Recent News


If you are interested in predictive analytics projects using customs dataset or point-of-interest (POI) dataset, please contact me. Listed below are some projects I did with interns.

  • Active Learning for Customs Selection [Slides]
  • Hierarchical POI Embedding with Heterogeneuos Information [Slides]
  • POI & Location Embedding Results [Github]
  • Customer Revisit Prediction using Macroscale Mobility [PDF]

Academic Services


  • Research:
  • Others:
    • Why did I become a researcher? [Video] [PDF]
    • What I have done so far, what I am interested in. [PDF]
    • Machine learning approach for customs fraud detection [PDF]
    • Tools and approaches for applied science in the era of big data [PDF]

Contact Details

  • E-mail: sundong (at)
  • WWW:
  • Office: B233 (Theory), Institute of Basic Science, 55 Expo-ro, Yuseong-gu, Daejeon, Republic of Korea