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
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[Dec. 2019] Seungcheol Park won Google Travel Award
M.S./Ph.D. student Seungcheol Park won Google Travel Award for his IROS 2019 conference trip. Google Travel Award is given to a selected primary author of a paper in a top-tier conference in Computer Science. Seungcheol presented his paper "Curved-Voxel Clustering for Accurate Segmentation of 3D LIDAR Point Clouds with Real Time Performance" in IROS 2019. Congratulations!
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[Nov. 2019] Hyunsik Jeon won BigData 2019 Travel Award
Ph.D. student Hyunsik Jeon won BigData 2019 travel award for his conference trip. Hyunsik Jeon will present his paper “Data Context Adaptation for Accurate Recommendation with Additional Information” in BigData 2019 Conference.
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[Nov. 2019] A paper accepted to EDBT 2020, a top tier database conference.
A paper is accepted to EDBT 2020, a top tier database conference. The paper "BalanSiNG: Fast and Scalable Generation of Realistic Signed Networks" proposed a scalable and fully parallelizable method for generating large-scale signed networks following realistic properties.
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[Oct. 2019] A paper accepted to WSDM 2020, a top tier data mining conference.
A paper is accepted to WSDM 2020, a top tier data mining conference. The paper “Sampling Subgraphs with Guaranteed Treewidth for Accurate and Efficient Graphical Inference” proposed a graph sampling method that generates bounded-treewidth subgraphs for efficient belief propagation.
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[Oct. 2019] A paper accepted to BigData 2019, a top tier data mining conference.
A paper is accepted to BigData 2019, a top tier data mining conference. The paper “Data Context Adaptation for Accurate Recommendation with Additional Information” proposed a neural network based algorithm for recommendation that factorizes a rating matrix and an auxiliary matrix.