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 transparent modelling and scalable analysis of systems with complex, self-organised dynamics. We broadly combine formal methods (especially model reduction, probabilistic model checking and parameter synthesis), mathematical modelling (especially stochastic systems and Bayesian inference), applications to systems biology, synthetic biology, and collective behaviour.
Ongoing projects:
- Data-aware Multilayer Collectives
- Automated reductions for stochastic chemical reaction networks
- Data-informed verification for population Markov models
- Model checking gene regulation
- Collective defense in honeybee colonies
- Environmental uncertainty shapes rat foraging behavior in large scale environments
Software Tools: