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 May 20, 2023, Feiqiang Liu successfully defended his Ph.D. thesis "Image super-resolution based on deep learning" at the College of Electronics and Information Engineering of Sichuan University, China. He was awarded the title of Doctor of Signal and Information Processing. Feiqiang was supervised by Xiaomin Yang and Bernard De Baets. He spent one year at KERMIT supported by the China Scholarship Council.