jaemin_yoo

Jaemin Yoo

Postdoctoral Researcher at Carnegie Mellon University

I am a postdoctoral research fellow at Carnegie Mellon University, working with 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 and the Qualcomm Innovation Fellowship. My research interests include graph mining, graph neural networks, time series forecasting, and interpretable tree models.

Email: jaeminyoo at cmu.edu
Links: [ Google Scholar | DBLP | LinkedIn ]

News

About Me

Positions

  • Carnegie Mellon University (Mar. 2022 - Present)
    Postdoctoral Research Fellow
    Heinz College of Information Systems and Public Policy
    Advisor: Prof. Leman Akoglu

Education

  • Seoul National University (Mar. 2016 - Feb. 2022)
    Ph.D. in Computer Science and Engineering
    Thesis: Probabilistic Approaches for Node and Graph Classification
    Advisor: Prof. U Kang
  • Seoul National University (Mar. 2012 - Feb. 2016)
    B.S. in Computer Science and Engineering

Awards and Honors

Research Interests

  • Missing feature estimation in graphs [KDD-22]
  • Graph structure augmentation [TheWebConf-22]
  • Interpretable tree models [ICDM-19, PAKDD-21, SDM-22]
  • Node classification in graphs [ICDM-17, IJCAI-19, ICDM-21]
  • Time series forecasting [SDM-21, KDD-21, BigData-22]
  • Subgraph sampling [WSDM-20]
  • Zero-shot knowledge distillation [NeurIPS-19]

Publications

  1. UltraProp: Principled and Explainable Propagation on Large Graphs
    Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, and Christos Faloutsos
    arXiv Preprint (2023) [ paper | bib ]
  2. Mining of Real-world Hypergraphs: Concepts, Patterns, and Generators
    Geon Lee, Jaemin Yoo, and Kijung Shin
    TheWebConf 2023 (Tutorial) [ slides and videos (long) ]
  3. SlenderGNN: Accurate, Robust, and Interpretable GNN, and the Reasons for its Success
    Jaemin Yoo*, Meng-Chieh Lee*, Shubhranshu Shekhar, and Christos Faloutsos (*equal contribution)
    arXiv Preprint (2022) [ paper | bib ]
  4. Self-supervision is not magic: Understanding Data Augmentation in Image Anomaly Detection
    Jaemin Yoo, Tiancheng Zhao, and Leman Akoglu
    arXiv Preprint (2022) [ paper | bib ]
  5. Accurate Stock Movement Prediction with Self-supervised Learning from Sparse Noisy Tweets
    Yejun Soun*, Jaemin Yoo*, Minyong Cho, Jihyeong Jeon, and U Kang (*equal contribution)
    BigData 2022 [ paper | bib ]
  6. Mining of Real-world Hypergraphs: Concepts, Patterns, and Generators
    Geon Lee, Jaemin Yoo, and Kijung Shin
    ICDM 2022 and CIKM 2022 (Tutorial) [ proposal | slides and videos | bib ]
  7. Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators
    Sunwoo Kim, Minyoung Choe, Jaemin Yoo, and Kijung Shin
    ICDM 2022 [ paper | slides | code and datasets | bib ]
  8. Accurate Node Feature Estimation with Structured Variational Graph Autoencoder
    Jaemin Yoo, Hyunsik Jeon, Jinhong Jung, and U Kang
    KDD 2022 [ paper | slides | code and datasets | bib | blog (Korean) ]
  9. Graph-based PU Learning for Binary and Multiclass Classification without Class Prior
    Jaemin Yoo*, Junghun Kim*, Hoyoung Yoon*, Geonsoo Kim, Changwon Jang, and U Kang (*equal contribution)
    Knowledge and Information Systems (2022) [ paper | bib | blog (Korean) ]
  10. Signed Random Walk Diffusion for Effective Representation Learning in Signed Graphs
    Jinhong Jung, Jaemin Yoo, and U Kang
    PLOS ONE (2022) [ paper | code and datasets | bib ]
  11. Model-Agnostic Augmentation for Accurate Graph Classification
    Jaemin Yoo, Sooyeon Shim, and U Kang
    TheWebConf 2022 [ paper | slides | code and datasets | bib | blog (Korean) ]
  12. MiDaS: Representative Sampling from Real-world Hypergraphs
    Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin
    TheWebConf 2022 [ paper | slides | code and datasets | bib ]
  13. Transition Matrix Representation of Trees with Transposed Convolutions
    Jaemin Yoo and Lee Sael
    SDM 2022 [ paper | slides | code and datasets | bib ]
  14. 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
  15. 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 ]
  16. Gaussian Soft Decision Trees for Interpretable Feature-Based Classification
    Jaemin Yoo and Lee Sael
    PAKDD 2021 [ paper | slides | code and datasets | bib ]
  17. Attention-Based Autoregression for Accurate and Efficient Multivariate Time Series Forecasting
    Jaemin Yoo and U Kang
    SDM 2021 [ paper | slides | bib | blog (Korean) ]
  18. 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
  19. Knowledge Extraction with No Observable Data
    Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang
    NeurIPS 2019 [ paper | slides | code and datasets | poster | bib | blog (Korean) ]
    Qualcomm Innovation Fellowship Korea
  20. EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees
    Jaemin Yoo and Lee Sael
    ICDM 2019 [ paper | slides | code and datasets | bib ]
  21. Belief Propagation Network for Hard Inductive Semi-Supervised Learning
    Jaemin Yoo, Hyunsik Jeon, and U Kang
    IJCAI 2019 [ paper | slides | code and datasets | poster | bib | blog (Korean) ]
  22. Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs
    Saehan Jo, Jaemin Yoo, and U Kang
    WSDM 2018 [ paper | slides | poster | code and datasets | bib ]
  23. Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets
    Mauro Scanagatta, Giorgio Corani, Marco Zaffalon, Jaemin Yoo, and U Kang
    International Journal of Approximate Reasoning (2018) [ paper | bib ]
  24. Supervised Belief Propagation: Scalable Supervised Inference on Attributed Networks
    Jaemin Yoo, Saehan Jo, and U Kang
    ICDM 2017 [ paper | slides | code and datasets | bib | blog (Korean) ]

Miscellaneous

Invited Talks

Professional Services

Jaemin Yoo @ CMU