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KAIST InfoLab is established and led by Min-Soo Kim, an associate professor of computer science at the KAIST School of Computing. The research areas of KAIST InfoLab are database, data mining & machine learning, medical/bioinformatics. In the database area, InfoLab focuses on efficient query processing on large-scale relational, graph, array, key-value, and blockchain data based on distributed systems and GPU computing. In the data mining and machine learning area, InfoLab focuses on large-scale BERT models, graph neural networks(GNN), neural architecture search(NAS) and DBMS-AI integration. In the medical/bioinformatics area, InfoLab focuses on rapid design of high-quality oligonucleotides for qPCR, large-scale genome-wide association study(GWAS), medical common data model(CDM), and medical AI. InfoLab combines the pursuit of academic excellence with industrial relevance, in particular, has been steadily publishing top conference and journal papers such as ACM SIGMOD, ACM KDD, IEEE ICDE, and Nucleic Acids Research (NAR) while actively collaborating with major hospitals and IT companies.