Mission statement

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.

Methodological expertise

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)


Doctoral degree for Leonardo Felipe dos Santos Scabini

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.

All KERMIT news


Most recent journal publications
Biblio logo(731) When driving becomes risky: Micro-scale variants of the lane-changing maneuver in highway traffic
A. Qayyum, B. De Baets, S. Van Ackere, F. Witlox, G. De Tré and N. Van de Weghe
(2023) TRAFFIC INJURY PREVENTION. 24, 583-591.
Biblio logo(730) Non-uniform number-conserving Elementary Cellular Automata on the infinite grid: a tale of the unexpected
B. Wolnik, M. Dziemiańczuk and B. De Baets
(2023) INFORMATION SCIENCES. 649, 119680.
Biblio logo(729) DeepMTP: A Python-based deep learning framework for multi-target prediction
D.Iliadis, B. De Baets and W. Waegeman
(2023) SOFTWAREX. 23, 101516.
All KERMIT publications