Our research projects and publications


  • Analysis and Simulation of Complex Networks
    We consider stochastic spreading processes on large complex networks and develop novel methods for efficient simulation, analysis, and control of the emerging dynamics.
  • Stochastic Modelling of DNA methylation Dynamics
    Together with the group of Prof. Dr. Jörn Walter we develop stochastic models that describe epigenetic modifications of the DNA.
    In this project we develop novel methods for the analysis of stochastic chemical reaction networks that exhibit multimodal behaviour.
  • NextAID (Saarland University)
    Our mission is to apply cutting-edge machine learning methods to accelerate compound screening, perform in-silico predictions of pharmacokinetic properties and toxicity of drug candidates, improve drug efficacy and effectiveness, and design new drug candidates.
  • MoDigPro/Reinforcement Learning (ERDF)
    We approximate near-optimal policies of stochastic decision making processes where process rewards are determined by the discrete-event simulation of a related reward model. Our focus is on combinations of Deep Reinforcement Learning methods and planning heuristics to solve planning benchmarks and applications from automotive industry.


  • Geobound
    Overview Geobound takes a transition class model and a polynomial Lyapunov function as input and symbolically computes the drift, i.e. a multivariate polynomial expressing the expected change in the Lyapunov function for each state.
  • H(O)TA
    Overview H(O)TA is a Matlab based tool that allows biologists to accurately measure the methylation and hydroxylation levels at a certain locus of the DNA and to determine the efficiencies of the enzymes that are responsible for maintenance (Dnmt1) and de novo (Dnmt3a/b) methylation as well as hydroxylation (Tets) at this locus over time.
  • LumPy
    The LumPy toolset implements lumping for Pair-Approximation (PA) and Degree-Based Mean Field (DBMF) equations for contact processes on complex networks. It reduces the large number of ODEs given by the PA or DBMF equations by clustering them and solving instead just a single ODE per cluster.
  • STAR
    STAR is a tool for the stochastic hybrid analysis of Markov population models, that is, Markov processes with an underlying population structure. It efficiently computes an accurate approximation of the probability distribution at a particular time instant based on a stochastic hybrid model that combines moment-based and state-based representations of probability distributions.