jaemin_yoo

Jaemin Yoo

Assistant Professor at KAIST

I am an Assistant Professor in the School of Electrical Engineering at KAIST and jointly affiliated with the Kim Jaechul Graduate School of AI. Previously, I was a postdoctoral research fellow at Carnegie Mellon University, where I worked with Leman Akoglu and Christos Faloutsos. I received my Ph.D. and B.S. in Computer Science and Engineering from Seoul National University. I received the Google PhD Fellowship, the Qualcomm Innovation Fellowship, and the Outstanding Dissertation Award from the Korean Academy of Science and Technology. My research interests cover various topics in data mining and machine learning, including graph neural networks, time series analysis, recommender systems, and anomaly detection.

About Me

Positions

  • KAIST (Aug. 2023 - Present)
    Assistant Professor, School of Electrical Engineering
    Adjunct Professor, Kim Jaechul Graduate School of AI
  • Carnegie Mellon University (Mar. 2022 - Jun. 2023)
    Postdoctoral Research Fellow, Heinz College
    Advisors: Prof. Leman Akoglu and Prof. Christos Faloutsos

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

Teaching @ KAIST

  • EE412: Foundation of Big Data Analytics (Fall 2025)
  • EE209: Programming Structure for Electrical Engineering (Spring 2025)
  • EE217: Introduction to Modern Software Development (Spring 2025)
  • EE412: Foundation of Big Data Analytics (Fall 2024)
  • EE213: Discrete Methods for Electrical Engineering (Spring 2024)
  • EE412: Foundation of Big Data Analytics (Fall 2023)

Publications

2025

  • Parameter-Free Hypergraph Neural Network for Few-Shot Node Classification
    Chaewoon Bae, Doyun Choi, Jaehyun Lee, and Jaemin Yoo
    NeurIPS 2025 [ to appear ]
  • 'Hello, World!': Making GNNs Talk with LLMs
    Sunwoo Kim, Soo Yong Lee, Jaemin Yoo, and Kijung Shin
    EMNLP 2025 Findings (Short) [ paper | code ]
  • Self-Tuning Self-Supervised Image Anomaly Detection
    Jaemin Yoo, Lingxiao Zhao, and Leman Akoglu
    KDD 2025 [ paper | code ]
  • Aggregation Buffer: Revisiting DropEdge with a New Parameter Block
    Dooho Lee, Myeong Kong, Sagad Hamid, Cheonwoo Lee, and Jaemin Yoo
    ICML 2025 [ paper | code ]
  • Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation
    Jin-Duk Park, Jaemin Yoo, and Won-Yong Shin
    WWW 2025 [ paper | code | bib ]
  • End-To-End Self-Tuning Self-Supervised Time Series Anomaly Detection
    Boje Deforce, Meng-Chieh Lee, Bart Baesens, Estefanía Serral Asensio, Jaemin Yoo and Leman Akoglu
    SDM 2025 [ paper | code | bib ]

2024

  • Rethinking Reconstruction-based Graph-level Anomaly Detectors: Limitations and a Remedy
    Sunwoo Kim, Soo Yong Lee, Fanchen Bu, Shinhwan Kang, Kyungho Kim, Jaemin Yoo, and Kijung Shin
    NeurIPS 2024 [ paper | code | bib ]
  • Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
    Soo Yong Lee, Sunwoo Kim, Fanchen Bu, Jaemin Yoo, Jiliang Tang, and Kijung Shin
    ICML 2024 [ paper | code | bib ]
  • NetEffect: Discovery and Exploitation of Generalized Network Effects
    Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, and Christos Faloutsos
    PAKDD 2024 [ paper | code | bib ]
  • HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
    Sunwoo Kim, Shinhwan Kang, Fanchen Bu, Soo Yong Lee, Jaemin Yoo, and Kijung Shin
    ICLR 2024 [ paper | code | bib ]
  • Representative and Back-in-Time Sampling from Real-world Hypergraphs
    Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin
    ACM Transactions on Knowledge Discovery from Data [ paper | code | bib ]

2023

  • Mining of Real-world Hypergraphs: Concepts, Patterns, and Generators
    Geon Lee, Jaemin Yoo, and Kijung Shin
    KDD 2023 and WWW 2023 (Tutorial) [ proposal | video | homepage | bib ]
  • Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities
    Leman Akoglu and Jaemin Yoo
    BigData 2023 (Short) [ paper | bib ]
  • DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection
    Jaemin Yoo, Yue Zhao, Lingxiao Zhao, and Leman Akoglu
    ECML PKDD 2023 [ paper | code | bib ]
  • Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining
    Jaemin Yoo*, Meng-Chieh Lee*, Shubhranshu Shekhar, and Christos Faloutsos
    KDD 2023 [ paper | code | bib ]
  • How Transitive Are Real-World Group Interactions? - Measurement and Reproduction
    Sunwoo Kim, Fanchen Bu, Minyoung Choe, Jaemin Yoo, and Kijung Shin
    KDD 2023 [ paper | appendix | code | bib ]
  • Classification of Edge-dependent Labels of Nodes in Hypergraphs
    Minyoung Choe, Sunwoo Kim, Jaemin Yoo, and Kijung Shin
    KDD 2023 [ paper | appendix | code | bib ]
  • Towards Deep Attention in Graph Neural Networks: Problems and Remedies
    Soo Yong Lee, Fanchen Bu, Jaemin Yoo, and Kijung Shin
    ICML 2023 [ paper | code | bib ]
  • Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success
    Jaemin Yoo, Tiancheng Zhao, and Leman Akoglu
    Transactions on Machine Learning Research [ paper | code | bib ]
  • Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators
    Sunwoo Kim, Minyoung Choe, Jaemin Yoo, and Kijung Shin
    Data Mining and Knowledge Discovery [ paper | code | bib ]

2022

  • Mining of Real-world Hypergraphs: Concepts, Patterns, and Generators
    Geon Lee, Jaemin Yoo, and Kijung Shin
    ICDM 2022 and CIKM 2022 (Tutorial) [ proposal | video | homepage | bib ]
  • Probabilistic Approaches for Node and Graph Classification
    Jaemin Yoo
    Ph.D. Thesis, Seoul National University [ web ]
    Outstanding Dissertation Awards from SNU CSE & KAST (한국과학기술한림원)
  • Accurate Stock Movement Prediction with Self-supervised Learning from Sparse Noisy Tweets
    Yejun Soun*, Jaemin Yoo*, Minyong Cho, Jihyeong Jeon, and U Kang
    BigData 2022 [ paper | bib ]
  • Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators
    Sunwoo Kim, Minyoung Choe, Jaemin Yoo, and Kijung Shin
    ICDM 2022 [ paper (extended) | code | bib ]
  • Accurate Node Feature Estimation with Structured Variational Graph Autoencoder
    Jaemin Yoo, Hyunsik Jeon, Jinhong Jung, and U Kang
    KDD 2022 [ paper | code | bib ]
  • Model-Agnostic Augmentation for Accurate Graph Classification
    Jaemin Yoo, Sooyeon Shim, and U Kang
    WWW 2022 [ paper | code | bib ]
  • MiDaS: Representative Sampling from Real-world Hypergraphs
    Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin
    WWW 2022 [ paper | code | bib ]
  • Transition Matrix Representation of Trees with Transposed Convolutions
    Jaemin Yoo and Lee Sael
    SDM 2022 [ paper | code | bib ]
  • 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
    Knowledge and Information Systems [ paper | bib ]
  • Signed Random Walk Diffusion for Effective Representation Learning in Signed Graphs
    Jinhong Jung, Jaemin Yoo, and U Kang
    PLOS One [ paper | code | bib ]

2021

  • Accurate Graph-Based PU Learning without Class Prior
    Jaemin Yoo*, Junghun Kim*, Hoyoung Yoon*, Geonsoo Kim, Changwon Jang, and U Kang
    ICDM 2021 [ paper | bib ]
    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 | datasets | bib ]
  • Gaussian Soft Decision Trees for Interpretable Feature-Based Classification
    Jaemin Yoo and Lee Sael
    PAKDD 2021 [ paper | code | bib ]
  • Attention-Based Autoregression for Accurate and Efficient Multivariate Time Series Forecasting
    Jaemin Yoo and U Kang
    SDM 2021 [ paper | code | bib ]

Before 2021

  • 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 | code | bib ]
    Samsung HumanTech Paper Award & Qualcomm Innovation Fellowship Korea
  • Knowledge Extraction with No Observable Data
    Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang
    NeurIPS 2019 [ paper | code | bib ]
    Qualcomm Innovation Fellowship Korea
  • EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees
    Jaemin Yoo and Lee Sael
    ICDM 2019 [ paper | code | bib ]
  • Belief Propagation Network for Hard Inductive Semi-Supervised Learning
    Jaemin Yoo, Hyunsik Jeon, and U Kang
    IJCAI 2019 [ paper | code | bib ]
  • Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs
    Saehan Jo, Jaemin Yoo, and U Kang
    WSDM 2018 [ paper | code | bib ]
  • Supervised Belief Propagation: Scalable Supervised Inference on Attributed Networks
    Jaemin Yoo, Saehan Jo, and U Kang
    ICDM 2017 [ paper | code | bib ]
  • 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 [ paper | bib ]

Professional Services

Jaemin Yoo @ KAIST