Welcome!

We are Data Science & Artificial Intelligence Lab (DSAIL) at KAIST led by Prof. Chanyoung Park. Due to the recent expansion of social media and online communities, online platforms in the digital economy are inundated with vast amounts of usergenerated multimodal (heterogeneous) data from various sources, which can be categorized into structured (e.g., graphs such as social network) and unstructured data (e.g., text, image, video, and audio). When properly analyzed, such multimodal data can be a valuable asset to the companies, but it is challenging not only due to the difficulty in extracting meaningful information from the inherently sparse and noisy data, but also in combining and customizing the extracted knowledge from different modalities with different statistical properties to facilitate various target applications.

Research Area

Our goal is to mine meaningful knowledge from multimodal data, and develop artificial intelligence solutions for various real-world applications across different disciplines. Two underlying themes of our research are:

1. Representation: How can we extract knowledge from different modalities of data and represent them in a unified way such that the relations among different modalities are captured, and the synergy within the multimodality is facilitated?
2. Fusion: How can we combine the extracted knowledge and customize it to facilitate various underlying target applications?

Our main research interests include Data-centric AI, Machine Learning, Deep Learning, Multi-modal Data Mining, and their applications including but not limited to the following:

• Recommender system
• AI for Science (Chemistry/Bioinformatics/Materials Science)
• Graph Neural Network and its Applications
• Molecular design and Drug discovery
• (Multi-modal) Representation learning
• Large Language Models
• Explainable AI
• Robust machine learning
• Scene understanding
• Knowledge graphs
• Continual learning
• Causal learning
• Social network analysis
• Graph mining
• Fraud/Anomaly detection
• Sentiment analysis
• Purchase/Click prediction
• Time-series and spatio-temporal analysis,
• AI for finance
• etc…

MUSE: Music Recommender System with Shuffle Play Recommendation Enhancement , CIKM23

Task-Equivariant Graph Few-shot Learning , KDD23

Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning, Bioinformatics (2023)

LTE4G: Long-Tail Experts for Graph Neural Networks, CIKM22

Relational Self-Supervised Learning on Graphs, CIKM22

GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment, SIGIR22

Augmentation-Free Self-Supervised Learning on Graphs, AAAI22

Multi-aspect Network Embedding, KDD20

Multiplex Network Embedding, AAAI20

Pair embedding in Heterogeneous Network, CIKM19

Heterogeneous User Behavior Prediction, IJCAI19

Sequential and Diverse Recommendation, IJCAI19

Translation-based Recommendation, ICDM18

Sentiment-based Recommendation, SIGIR18

Visual Matrix Co-Factorization, WWW17

Convolutional Matrix Factorization, RecSys16

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Interested?

If you’re interested in joining our lab, send an email with your interests, CV, and transcript to cy.park (at) kaist.ac.kr.