Jaemin Yoo

Postdoctoral Researcher at Carnegie Mellon University

I am a postdoctoral research fellow at Carnegie Mellon University, hosted by Prof. Leman Akoglu. I received my Ph.D. and B.S. in Computer Science and Engineering from Seoul National University, where I was advised by Prof. U Kang. I am a recipient of the Google PhD Fellowship Program and the Qualcomm Innovation Fellowship Korea. My research interests include graph mining, graph neural networks, time series forecasting, and interpretable tree models.

Email: jaeminyoo at cmu.edu
Research: [ Google Scholar | DBLP | Download C.V. ]


About Me

Position and Education

  • Carnegie Mellon University
    Postdoctoral Research Fellow (Mar. 2022 - Present)
    DATA Lab (by Prof. Leman Akoglu)
  • Seoul National University
    Ph.D. in Computer Science and Engineering (Feb. 2022)
    B.S. in Computer Science and Engineering (Feb. 2016)

Research Interests

  • Node classification in graphs (ICDM'17, IJCAI'19, ICDM'21)
  • Interpretable tree models (ICDM'19, PAKDD'21, SDM'22)
  • Multivariate time series forecasting (SDM'21, KDD'21)
  • Missing feature estimation in graphs (KDD'22)
  • Graph structure augmentation (WWW'22)
  • Subgraph sampling (WSDM'20)
  • Zero-shot knowledge distillation (NeurIPS'19)

Awards and Honors


  • Accurate Node Feature Estimation with Structured Variational Graph Autoencoder
    Jaemin Yoo, Hyunsik Jeon, Jinhong Jung, and U Kang
    KDD 2022 (to appear)
  • Probabilistic Approaches for Node and Graph Classification
    Jaemin Yoo
    Ph.D. Thesis [ paper | slides | bib ]
    Best Ph.D. Thesis Award in SNU CSE
  • Accurate Graph-Based PU Learning without Class Prior
    Jaemin Yoo*, Junghun Kim*, Hoyoung Yoon*, Geonsoo Kim, Changwon Jang, and U Kang (*equal contribution)
    ICDM 2021 [ paper | slides | bib | blog (Korean) ]
    One of the best-ranked papers of ICDM 2021 for fast-track journal invitation
  • Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts
    Jaemin Yoo, Yejun Soun, Yong-chan Park, and U Kang
    KDD 2021 [ paper | slides | datasets | bib ]
  • Sampling Subgraphs with Guaranteed Treewidth for Accurate and Efficient Graphical Inference
    Jaemin Yoo, U Kang, Mauro Scanagatta, Giorgio Corani, and Marco Zaffalon
    WSDM 2020 [ paper | poster | code and datasets | bib | blog (Korean) ]
    Samsung HumanTech Paper Award, Qualcomm Innovation Fellowship Korea


Invited Talks

Academic Services

Jaemin Yoo @ CMU