Data & Adaptive Intelligence Systems Lab

We are currently seeking undergraduate researchers starting in January 2025 [Official post (Korean)]

📰 News

2024.10  

• Susik gave talks at AI Tech Day and Kongju National University

• A paper on continual network coreness prediction (in collaboration with UNIST) received the best paper award (silver prize) at KDBC 2024

2024.09  

• A paper on time series segmentation accepted to NeurIPS 2024

2024.08  

• We started the Global Basic Research Laboratory project on compound disaster prediction with multi-modal AI (funded by National Research Foundation of Korea, in collaboration with Kongju National University and the University at Buffalo)

• We started the project on a foundation model for structured data with KT

2024.07 

• Twelve undergraduate students, one researcher, and one master student joined DAIS Lab

• 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

• 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 

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

• 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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