The research unit KERMIT (Knowledge-based Systems) adopts a holistic view on mathematical and computational modelling, acknowledging the needs of our modern information society with a particular focus on the applied biological sciences. It strives to keep a unique balance between theoretical developments and practical applications, a strategy that has proven particularly successful, regarding the output, visibility and recognition of the team. It plays a pioneering role by promoting existing as well as developing new methods in a broad range of disciplines shown below.

KERMIT started out in 2000 with two men and a dog as a team in fuzzy set theory and preference modelling, and quickly expanded into the fields of artificial/computational intelligence and operations research. Over the years, it has evolved into a holistic team covering the entire data-to-decision pipeline. It provides methodological expertise in the following areas:

• image processing and computer vision (e.g. mathematical morphology, filters)
• knowledge-based systems (e.g. bioportals, ontologies)
• dynamical modelling (e.g. differential equations, cellular automata, individual-based models)
• data science and machine learning (e.g. time series prediction, structured prediction, deep learning, big data)
• management of imprecision and uncertainty (e.g. probability, possibility and fuzzy set theory; stochastic modelling, theory of copulas)
• optimization and operations research (e.g. preference modelling, multi-criteria decision making)

On July 24, 2023, Leonardo Felipe dos Santos Scabini successfully defended his Ph.D. thesis "Patterns and randomness in networks for computer vision: from graphs to neural networks" and was awarded the title of Doctor of Applied Physics: Computational Physics from the University of São Paulo, Brazil. Leonardo was supervised by Odemir Bruno and Bernard De Baets.