Our research focuses on the study of evolution with the help of stochastic models. Among others, we use game-theoretic, Markov and branching process models, and examine which combinations of the parameters yield evolutionarily stable strategies. We are also interested in the evolutionary background of moral rules (e.g. envy, selfishness, self-sacrifice, altruism).

In our research, we investigate models of multilayer random graphs and hypergraphs, as well as the spread of epidemics and opinions on these networks. We study how the parameters of graph models and spreading processes can be estimated from certain statistical properties of the processes, using both classical and machine learning methods.

Development and study of models of random permutations and graphs, including determining the dimension of their parameter spaces. Fitting models to real-world data. Algebraic statistical analysis.