Research

The goal of our research

Data science lab.은 추천시스템과 지식진화의 연구그룹으로 구성됩니다.

Recommendation system

Recommendation systems are computer programs that provide personalized recommendations to users. They analyze users’ past behavior to understand their preferences and interests, and then recommend various products, services, or content to them accordingly.

  • Recommendation algorithms: This is the core technology of recommendation systems. Researchers develop algorithms that analyze users’ past behavior data to provide personalized recommendations.

  • User experience: Researchers study user experience to improve the quality of recommendations. This includes factors such as transparency, diversity, and trustworthiness.

  • Use of recommendation systems: Researchers study how users interact with recommendation systems. For example, how recommendation systems may influence users’ preferences or purchasing behavior.

  • Application areas: Recommendation systems are applied in various fields, such as music, movies, books, shopping, and travel. Researchers study how recommendation systems can be tailored to specific application areas.

Knowledge evolution

The topic of the knowledge evolution is to combine artificial intelligence, the semantic web, and (stream-)database integration techniques to solve complex, fast-changing information on the Web. We leverage general research techniques across information-intensive disciplines, including smart energy, data integration and the social web.

🌿 Sustainable Web

  • Energy-Efficient Web Design and Frontend Optimization
  • Carbon-Aware Web Workload Scheduling
  • Lifecycle Sustainability Analytics and User Engagement

🧠 Multi-modal Knowledge Graph Reasoning

  • Automated construction of multimodal knowledge graphs integrating text, images, and sensor data
  • Designing Graph Neural Networks for reasoning over multimodal KGs
  • Event reasoning and explainability using KGs
  • Semantic Table Annotation for KG Enrichment
  • Anomaly Detection using Multimodal KGs

⚡ Carbon-aware Computing

  • Cloud workload scheduling algorithms based on carbon emission predictions
  • VM placement optimization using power mix and time-dependent carbon intensity
  • Edge-cloud collaborative environments for carbon-performance trade-off optimization
  • Real-time monitoring and reduction techniques for carbon footprints during AI model training and inference

Application areas

We are expanding existing application domains to include knowledge-driven management and optimization systems in diverse fields:

  1. Smart Grid and Energy Management Systems
    • Smart grids, microgrids, energy management systems
    • Virtual power plants, electric vehicle charging optimization
    • Digital twin of energy infrastructure for predictive control
    • Carbon-intelligent workload migration and scheduling
  2. Manufacturing and Quality Control
    • 3D coordinate measuring machine data analysis for precision inspection
    • Manufacturing process monitoring and optimization
    • Digital twin integration for shop-floor visualization and predictive maintenance
    • Anomaly detection in manufacturing robots and processes
  3. Real-Time Environmental Monitoring
    • Real-time air pollution and emission monitoring (e.g., TMS systems)
    • Digital carbon emission tracking and reporting
    • Light pollution monitoring and analysis using multimodal data
    • Environmental digital twins for urban sustainability management
  4. Knowledge Tracing
    • Learner modeling and knowledge tracing using multimodal data
    • Microlearning module design and short-form educational video personalization
  5. Digital Twin-enhanced Robotics and Automation
    • Integration of manufacturing robots with digital twins for real-time monitoring
    • Robot-assisted microlearning environments for skill training
    • Anomaly detection in robot operations using multimodal sensor data
    • Energy-efficient robot scheduling and workload optimization using carbon-aware computing