Data Mining Lab.

Welcome to Data Mining Laboratory in the Department of Computer Science and Engineering at Seoul National University. Our research interests lie in artificial intelligence (AI), data mining, and machine learning to find models, algorithms, and systems for data analysis. Specifically, we focus on the following research topics: deep learning & machine learning, recommendation system, graphs/tensors, and financial AI.

What's New

  • [Oct. 2022] Prof. Kang got tenure and won the Shinyang Engineering Academic Award

    Prof. Kang got tenure and won the Shinyang Engineering Academic Award. The Shinyang Engineering Academic Award is given to a professor with outstanding achievements among young professors under the age of 49 who are promoted to tenure and associate professors at Seoul National University of Technology. Congratulations!

  • [Oct. 2022] Two papers accepted in BigData 2022, a top tier data mining conference.

    2 papers from Data Mining Lab are accepted in BigData 2022, a top tier data mining conference. Congratulations!

  • [Aug. 2022] Hyunsik Jeon won CIKM 2022 Travel Award

    Ph.D. student Hyunsik Jeon won CIKM 2022 Travel Award for his conference registration. Hyunsik will present his paper "Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge" in CIKM 2022. Congratulations!

  • [Aug. 2022] A paper accepted to CIKM 2022, a top tier data mining conference.

    The paper "Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge" is accepted to CIKM 2022, a top tier data mining conference. The paper proposed a SmartSense, an accurate action recommendation method for smart home.

  • [Aug. 2022] A paper accepted to KDD 2022, the world-best data science and AI conference.

    The paper "Accurate Node Feature Estimation with Structured Variational Graph Autoencoder" is accepted to KDD 2022, the world-best data science and AI conference. The paper proposed a SVGA (Structured Variational Graph Autoencoder), an accurate method for feature estimation.

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