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 Prof. Leman Akoglu and Prof. 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 awards from Seoul National University and 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, especially based on self-supervised learning with insufficient labels.

News

  • [Apr. 2024] I delivered an invited talk at KAIST EE Colloquium about anomaly detection [slides].
  • [Mar. 2024] I delivered an invited talk at Samsung Electronics about recent trends in AI.
  • [Jan. 2024] Our work on network effect analysis got accepted to PAKDD 2024.
  • [Jan. 2024] Our work on self-supervised learning for hypergraphs got accepted to ICLR 2024.
  • [Jan. 2024] I delivered a keynote talk at the ASTAD workshop during WACV 2024.
  • [Oct. 2023] Our vision paper on anomaly detection got accepted to BigData 2023.
  • [Oct. 2023] I delivered a guest lecture on graph augmentation at Yonsei University.
  • [Sep. 2023] I delivered an invited talk at the Learning on Graphs Seminar.
  • [Aug. 2023] I joined KAIST EE as an Assistant Professor [my research group].
  • [Jul. 2023] Our work on self-supervised anomaly detection got accepted to TMLR.
  • [Jun. 2023] Our work on self-supervised anomaly detection got accepted to ECML PKDD 2023.
  • [May 2023] We will present a tutorial on hypergraphs at KDD 2023.
  • [May 2023] 3 papers on graph mining got accepted to KDD 2023.

About Me

Positions

  • KAIST (Aug. 2023 - Present)
    Assistant Professor, School of Electrical Engineering
    Adjunct Professor, Kim Jaechul Graduate School of AI
    Research Group: KAIST Data AI Lab
  • 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
    Advisor: Prof. U Kang
  • Seoul National University (Mar. 2012 - Feb. 2016)
    B.S. in Computer Science and Engineering

Awards and Honors

Publications

2024

  • NetEffect: Discovery and Exploitation of Generalized Network Effects
    Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, Christos Faloutsos
    PAKDD 2024 [ paper | bib ]
  • HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
    Sunwoo Kim, Shinhwan Kang, Fanchen Bu, Soo Yong Lee, Jaemin Yoo, Kijung Shin
    ICLR 2024 [ paper | bib ]
  • Representative and Back-in-Time Sampling from Real-world Hypergraphs
    Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin
    Transactions on Knowledge Discovery from Data [ paper | code and datasets | bib ]

2023

2022

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 | 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 ]
  • Gaussian Soft Decision Trees for Interpretable Feature-Based Classification
    Jaemin Yoo and Lee Sael
    PAKDD 2021 [ paper | slides | code and datasets | bib ]
  • Attention-Based Autoregression for Accurate and Efficient Multivariate Time Series Forecasting
    Jaemin Yoo and U Kang
    SDM 2021 [ paper | slides | bib | blog (Korean) ]

2020

  • 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

2019

2017 - 2018

Miscellaneous

Teaching

  • EE213: Discrete Methods for Electrical Engineering (Spring 2024)
  • EE412: Foundation of Big Data Analytics (Fall 2023)

Professional Services

Invited Talks

Jaemin Yoo @ KAIST