Jaemin Yoo
Assistant Professor @ SNU CSE
I am an assistant professor in the Department of Computer Science and Engineering at Seoul National University. Previously, I was a postdoctoral fellow at Carnegie Mellon University, where I worked with Leman Akoglu and Christos Faloutsos. My graduate work was supported by 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.
- Email: jaeminyoo (at) snu.ac.kr
- Research Group: SNU Data AI Lab
- External Links: [ Google Scholar | DBLP | LinkedIn ]
About Me
Positions
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Seoul National University (Mar. 2026 - Present)
Assistant Professor, Department of Computer Science and Engineering
Adjunct Professor, Interdisciplinary Program in Artificial Intelligence
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KAIST (Aug. 2023 - Feb. 2026)
Assistant Professor, School of Electrical Engineering
Adjunct Professor, Kim Jaechul Graduate School of AI
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Carnegie Mellon University (Mar. 2022 - Jun. 2023)
Postdoctoral Fellow, Heinz College of Information Systems and Public Policy
Advisors: Prof. Leman Akoglu and Prof. Christos Faloutsos
Education
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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
Advisor: Prof. U Kang
Awards and Honors
- Distinguished PC Member of ECML PKDD 2025 (Nov. 2025)
- S-OIL Outstanding Dissertation Award (Dec. 2022)
- SNU CSE Outstanding Dissertation Award (Feb. 2022)
- One of the Best-Ranked Papers of ICDM 2021 (Dec. 2021)
- SNU BK21 Outstanding Graduate Student Award (Jul. 2021)
- Qualcomm Innovation Fellowship (Dec. 2020)
- Youlchon AI Star Award (Sep. 2020)
- Google PhD Fellowship (Sep. 2019)
- Samsung HumanTech Paper Award (Feb. 2019)
Publications
2026
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PULSE: Socially-Aware User Representation Modeling Toward Parameter-Efficient Graph
Collaborative Filtering
Doyun Choi*, Cheonwoo Lee*, Biniyam Aschalew Tolera, Taewook Ham, Chanyoung Park and Jaemin Yoo
WWW 2026 [ paper | code ] -
Feature-Centric Unsupervised Node Representation Learning Without Homophily
Assumption
Sunwoo Kim, Soo Yong Lee, Kyungho Kim, Hyunjin Hwang, Jaemin Yoo, and Kijung Shin
AAAI 2026 [ paper | code ]
2025
-
Simple and Behavior-Driven Augmentation for Recommendation with Rich Collaborative
Signals
Doyun Choi, Cheonwoo Lee, and Jaemin Yoo
BigData 2025 [ paper | code ] -
Parameter-Free Hypergraph Neural Network for Few-Shot Node Classification
Chaewoon Bae, Doyun Choi, Jaehyun Lee, and Jaemin Yoo
NeurIPS 2025 [ paper | code ] -
'Hello, World!': Making GNNs Talk with LLMs
Sunwoo Kim, Soo Yong Lee, Jaemin Yoo, and Kijung Shin
EMNLP 2025 Findings [ 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
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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
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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 [ 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 ] -
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
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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
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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 ]
Miscellaneous
Teaching Experience
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Instructor @ KAIST EE
Foundation of Big Data Analytics [ 2023F | 2024F | 2025F ]
Programming Structure for Electrical Engineering [ 2025S ]
Introduction to Modern Software Development [ 2025S ]
Discrete Methods for Electrical Engineering [ 2024S ]
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Teaching Assistant @ SNU CSE
Large Data Analysis [ 2017F ]
Introduction to Data Mining [ 2017S ]
Data Structures [ 2016F ]
Professional Services
- Organizing Committee of PAKDD GLFM Workshop (2025)
- Area Chair of KDD (2026)
- Area Chair of ICML (2026)
- Area Chair of CIKM (2025)
- Reviewer of KDD (2021 - 2026)
- Reviewer of WWW (2024 - 2026)
- Reviewer of ICLR (2023 - 2026)
- Reviewer of ICML (2025)
- Reviewer of NeurIPS (2023 - 2025)
- Reviewer of AAAI (2021 - 2026)
- Reviewer of WSDM (2025 - 2026)
- Reviewer of CIKM (2025)
- Reviewer of DASFAA (2026)
- Reviewer of ECML PKDD (2023 - 2026)
- Reviewer of LoG (2022 - 2025)
- Reviewer of TMLR (2024 - 2026)
Jaemin Yoo @ SNU CSE