The creation and collection of large amounts of data in various domains — biology, Internet of things or in financial markets — has promising potential towards better understanding of the underlying systems, which can further inform and improve our decision-making. One of the major challenges towards new knowledge are predictive, dynamic models which summarise our hypotheses and can be executed and analysed on a computer.  

Our research mission is to enable rigorous modelling and scalable analysis of systems with complex, often self-evolving dynamics, through mathematical frameworks, program design and program analysis. More concretely, in our work we combine formal methods for program analysis (such as model-checking, SAT solvers, automated reasoning in general), mathematical modeling (such as Markov chains, model reduction, statistical inference, machine learning), and the domain-specific modelling languages and theories (such as rule-based modeling, stochastic chemical kinetics).