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Methods and Technologies

Our methods and techs.

Our research group employs a diverse range of methods and technologies to tackle complex problems in various domains of application:

  • Conceptual Modeling: Developing abstract representations of domain concepts and relationships to facilitate understanding, communication, and system design.
  • Database Management Systems (DBMS): Utilizing various types of databases, including relational, document, graphs, and vectors, to efficiently store, retrieve, and analyze large volumes of data.
  • Data Mining and Machine Learning: Employing advanced computational techniques to extract valuable insights from complex datasets, encompassing knowledge extraction, pattern recognition, predictive modeling, and optimization.
  • Embedded/Edge Analytics: Investigating methods to perform analytics and inference directly on edge devices or embedded systems, enabling efficient processing and decision-making at the edge of the network.
  • Explainability: Focusing on developing transparent and interpretable machine learning models to facilitate understanding and trust in automated decision-making systems.
  • Graph Theory and Network Science: Leveraging mathematical models and algorithms to analyze and understand the structure and dynamics of complex networks.
  • Human-Computer Interaction: Exploring ways to enhance collaboration and interaction between humans and intelligent systems, ensuring effective communication and user experience.
  • Natural Language Processing (NLP): Applying computational techniques to analyze and understand human language, enabling tasks such as text classification, sentiment analysis, and machine translation.
  • Stream Processing: Implementing real-time data processing techniques to analyze continuous streams of data and extract valuable insights in dynamic environments.

By integrating these methodologies, we aim to advance knowledge and develop innovative solutions to address contemporary challenges in our research areas.