We seek to make future computing systems more adaptive, user friendly, and robust. More specifically, we are interested in

  • adaptation and learning in neuronal systems and
  • adaptation and learning in software systems.

We investigate neuronal systems for multiple reasons. First and foremost, we have a genuine interest in how the brain works and how it relates to the world we are experiencing and living in. Second, a deeper understanding of how the brain processes  information allows for designing computing systems, which respect these principles when interacting with or assisting users. Third, humans still outperform supercomputers in many apparently simple tasks. Therefore, we seek to reverse-engineer the brain in order to understand it and make computing smarter.

We investigate adaptation and learning in software systems, because they will be key properties of future software systems. In fact, there is already a high demand for software, which is not hard-coded by vendors and updated once a while. Instead, future software systems will be much more similar to evolving, learning and self-organizing biological systems. Therefore, we work on methods for developing adaptive and learning software, where we make use of both machine learning and bio-inspired principles.