Founded in 2000 as a small team focused on preference modelling and fuzzy set theory, KERMIT (an acronym for “Knowledge Extraction, Representation and Management using Intelligent Techniques”) has grown into a leading research unit shaping the future of intelligent techniques and their applications. Over time, KERMIT evolved into a comprehensive team spanning all stages from data analysis to decision-making, with a focus on knowledge-based, predictive and spatio-temporal modelling paradigms. By maintaining a unique balance between theoretical advancements and practical applications, KERMIT has achieved remarkable success in output, visibility, and recognition. To accommodate growing specialization and enhance its reach, three subunits officially branched off in 2024: BionamiX, BioML and Biovism. Despite this structural evolution, KERMIT remains dedicated to its holistic philosophy, integrating diverse disciplines to tackle complex challenges.

KERMIT’s mission is to harness mathematics and computation to unravel life's complexities, optimize biological functions, and drive innovation in biodesign and decision-making under uncertainty. Focused on applied biological sciences—including biotechnology, environmental technology, plant breeding and food technology—, KERMIT refines existing methods and develops cutting-edge approaches across disciplines. The team is committed to creating accessible software tools that transform data streams into actionable and interpretable insights. Valuing continuous learning, interdisciplinary collaboration, and mental well-being, KERMIT embraces a holistic approach to solving challenges in our data-driven, interconnected world.

Mathematical modelling at KERMIT emphasizes intuitively appealing, rule-based paradigms—such as fuzzy modelling, cellular automata, and formal concept analysis—as well as cross-fertilizations thereof. The team has a particular interest in exploring the underutilized diversity of underlying mathematical structures and functions, contributing significantly to the foundations of order theory, uncertainty modelling and aggregation theory. Computational modelling at KERMIT is dedicated to developing and applying cutting-edge techniques—such as differentiable, probabilistic, and evolutionary computation—to enhance the understanding and engineering of biological systems. By integrating AI-driven simulations, the team bridges the gap between theoretical models and real-world applications.

Towards the end of May 2026, four Chinese students successfully defended their Ph.D. thesis at their home university in China, co-supervised by Bernard De Baets. They all spent one year at KERMIT supported by the China Scholarship Council.

1. May 20: Jieqiong Shi defended her Ph.D. thesis “Various functional equations involving four classes of aggregation functions” at the School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, China. She was awarded the title of Doctor of Pure Mathematics. Her domestic supervisor was Bin Zhao.

2. May 22: Qian Pan defended her Ph.D. thesis “Preference uncertainty and behavioral heterogeneity in the multi-attribute graph model for conflict resolution” at the School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, China. She was awarded the title of Doctor of Management. Her domestic supervisor was Peide Liu.

3. May 22: Yuankai Hu defended his Ph.D. thesis “Algebraic analysis of preference matrices under uncertain environments and its applications” at the School of Mathematics, Guangxi University, Nanning, China. He was awarded the title of Doctor of Mathematics. His domestic supervisor was Fang Liu.

4. May 23: Yuan Gao defended her Ph.D. thesis “A study of lattice-valued mathematical structures based on ⊤-filters and their topological properties” at the School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China. She was awarded the title of Doctor of Mathematics. Her domestic supervisor was Bin Pang.