Members of the group conduct interdisciplinary research at the intersection of statistical physics, complex systems theory, and applications in economics and the social sciences. Their work focuses on analyzing the dynamics of multi-component systems — from models of social opinion and structural balance, through stochastic processes and extreme value statistics, to the evolution of economic and sociological rankings. An important area of research is also the development of advanced data analysis methods, including the use of random matrix theory for covariance estimation and noise reduction in data with complex correlation structures.
The group also studies classical and generalized models of nonlinear dynamics, such as ecological systems and neural networks, analyzing their stability, phase transitions, and the emergence of complex behaviors (limit cycles, chaos). These efforts are complemented by research in percolation theory and phase transitions, including condensation phenomena in random systems.
In parallel, team members are involved in experimental research in high-energy physics, participating in data analyses from the LHCb experiment at CERN, where they investigate the properties of B mesons and violations of fundamental symmetries. Modern computational approaches, particularly artificial intelligence and machine learning methods, are applied to the analysis of hadronic spectra, especially in the context of identifying exotic states.
