Data & Adaptive Intelligence Systems Lab

📰 News

[2025.03] Six undergraduate students advanced to graduate studies at DAIS Lab

[2025.02] Susik will serve as an Area Chair for KDD 2025 Applied Data Science Track

[2025.01] Four undergraduate students joined DAIS Lab

[2024.12] Woojun won the runner-up award in the Capstone Design Competition at Korea University

[2024.12] Susik received the Seoktop Lecture Award (Data Science) from Korea University

[2024.11] Susik gave a talk at Chung-Ang University

[2024.10] Susik gave talks at AI Tech Day at Korea University and Kongju National University

[2024.10] A paper on continual coreness prediction (with UNIST) received the best paper award (silver prize) at KDBC 2024

[2024.09] A paper on time series segmentation accepted to NeurIPS 2024

[2024.07] Twelve undergraduate students, one researcher, and one master student joined DAIS Lab

[2024.07] A paper on the clique problem accepted to CIKM 2024

[2024.06] Susik gave a talk at CHA Medical School and Korea Computer Congress 2024

[2024.06] We presented two undergraduate poster papers (with one best paper award) at Korea Computer Congress 2024

[2024.05] A paper on online concept drift detection accepted to KDD 2024 

[2024.05] Susik had an interview with the ICT Creative Consilience Program at Korea University (Link(Korean))

[2024.05] A paper on adaptive prompt tuning for continual learning accepted to ICML 2024 

[2024.04] Two undergraduate students joined DAIS Lab 

[2024.03] DAIS Lab at Korea University opened 🎉

📋Research Area

We are interested in broad topics in Data Science (DS) and Artificial Intelligence (AI). We identify real-world challenges with significant practical impacts and address them through DS/AI methodologies by leveraging {Evolving, (Un)structured data} X {Foundation models} X {Human-curated knowledge}.

Examples of research topics include, but are not limited to,

• DS and AI methodologies

  • Data mining, Machine learning, Deep learning, Multi-modal learning

  • Anomaly detection, Classification, Clustering, Recommendation, Summarization

• Learning from evolving data

  • Real-time machine learning, Continual learning, Data stream mining, Time-series mining, Concept drift adaptation

• Learning from (un)structured data

  • Natural Language Processing, Text mining, Information retrieval, Knowledge structuration, Representation learning

• Exploiting Foundation models (i.e., pretrained language, vision, multimodal models)

  • Retrieval-augmented generation, Prompt tuning, Model adaptation

• Exploiting human-curated knowledge (i.e., Knowledge base, metadata, weak labels)

  • Semi/weakly-supervised learning, Active learning, Knowledge base, Fake detection

See our [publications]

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