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About DEIS

The DEIS research group covers mathematical foundation, verification tools, validation methodologies, probabilistic graphical models and machine learning focusing on distributed, embedded and intelligent systems.

This includes the following areas:

  • Semantic theories for modelling the behaviour of computer programs and systems
  • Design, implementation and models for analysis and construction of distributed, embedded and intelligent systems
  • Algorithms, methods and tools for verification, and validation of programs and systems
  • Probabilistic models and algorithms for intelligent decision making and machine learning

model-driven development

Each of the four research directions mentioned above constitutes an area in its own right. 

Moreover, the areas are interrelated in a number of ways:

Semantic models offer important guidelines for the development of languages and paradigms for distributed systems, and they are necessary prerequisites for the development of verification and inference algorithms and tools.

The development of validation and inference tools provides new insight into the underlying models on the one hand, and are applied in environments for the construction and analysis of distributed, embedded and intelligent systems on the other.

The usage of distributed, embedded and intelligent software in increasingly complex systems provides valuable insight into the strengths and weaknesses of existing models, algorithms and tools, and serve as inspiration for the development of new ones. Machine learning is central for data-driven development of intelligent software. This complements and completes the unit's focus on model-driven development.


  • Mathematical and logical theory for modelling and specifying concurrent processes, including quantitative and security aspects
  • Tools, algorithms and datastructures for model checking, performance analysis and synthesis for complex systems
  • Model-based methodologies for embedded and cyber-physical systems
  • Analysis and construction of services and protocols for networks
  • Inference and learning of probabilistic graphical models
  • Machine learning using statistical as well as logic and relational-based methods
  • Applications in a variety of domains, including transport, energy, water management, and health.