Welcome to the LearnData Lab
Machine Learning and Data Mining Research lab at SKKU
We are a dynamic research group at the Sungkyunkwan University (SKKU). Our aim is to develop Machine Learning/Data Mining models on Inter-Connected Data (e.g., human interactions, object graph in 3D, social networks, knowledge base, education log, sensor network, and multi-modal graph) and Natural Language Processing (e.g., knowledge-enhanced large language models (LLMs) and logical/symbolic reasoning). The current research topics include self-supervised graph learning, multi-modal learning (image/text or sensors), question/answering, logical reasoning, knowledge tracing, recommender systems, and explainable AI (XAI).
We are looking for passionate new Ph.D. students and Postdocs to join the team (more info) !
We are part of the College of Computing and Informatics (소프트웨어융합대학) at Sungkyunkwan University (SKKU) and affiliated with Computer Science Engineering (소프트웨어학과), Artificial Intelligence (인공지능대학원), and Intelligent Software (지능형소프트웨어) departments.
We are grateful for funding from the National Research Foundation of Korea (NRF), BK21, Information & Communications Technology Planning & Evaluation (IITP), National IT Industry Promotion Agency (NIPA), LG Electronics, NCSOFT, and NAVER.
News
Nov. 2024A paper, CIMAGE: Exploiting the Conditional Independence in Masked Graph Auto-encoder, is accepted at WSDM 2025. Congratulations, Jongwon and Heesoo!
Oct. 2024A paper, A Spectrum of Nonsense-mediated mRNA Decay Efficiency along the Degree of Mutational Constraint, is accepted at Communications Biology. Congratulations, Hyunju! (A joint work with Samsung Medical Center)
Aug. 2024A paper, Enhancing Knowledge Tracing with Concept Map and Response Disentanglement, is accepted at Knowledge-Based Systems. Congratulations, Soonwook! (A joint work with Poly Inspiration)
Jul. 2024I gave a talk at the Data Intelligence Workshop, organized by the KIISE Database Society, on recent trends in Knowledge Graph Embedding methods.
May 2024A paper, Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning, is accepted at Findings of ACL 2024. Congratulations, Jeonghoon Kim and Heesoo Jung!
Jan. 2024A paper, UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models, is accepted at ICLR 2024. Congratulations, Hyunju and Geonhee!
Oct. 2023A paper, Self-supervised Multimodal Graph Convolutional Network for Collaborative Filtering, is accepted at the Information Sciences Journal. (A joint work with NAVER and Korea Univ.)
Aug. 2023A paper, Toward a Better Understanding of Loss Functions for Collaborative Filtering, is accepted at CIKM 2023.
May 2023A paper, Exploiting Relation-aware Attribute Representation Learning in Knowledge Graph Embedding for Numerical Reasoning, is accepted to SIGKDD 2023. Congratulations, Sookyung, Gayeong, and Heesoo!