Stefan Grosskinsky is a Reader at the Warwick Mathematics Institute and a member of the Centre for Complexity Science. He works on emergent phenomena in complex interacting systems with applications in social and biological sciences, using methods from probability, statistical mechanics and computational approaches. A particular focus of his research is the probabilistic modelling of aggregation and transport processes in the framework of stochastic particle systems, and their applications in physics, biology and economics.

Stefan obtained his PhD in Mathematical Physics at TU Munich in 2004 under the supervision of Herbert Spohn. After a fixed-term appointment as Lecturer in Applicable Mathematics at the Statistical Laboratory in Cambridge and a teaching fellowship at Churchill College, he moved to Warwick in 2007. Since then he has been part of the management team of the Centre for Doctoral Training in Complexity Science and later in Mathematics for Real-World Systems, and is currently coordinator of the associated MSc programme.

Research interests

Stefan’s research on dynamic large deviations in stochastic particle systems (SPS) is related to the programme in data-centric engineering. SPS are minimal models of complex systems, and rare fluctuations of dynamic quantities such as particle or energy currents in physical systems have recently attracted major research interest in statistical mechanics and beyond. Current simulation techniques in that field are based on the classical idea of evolutionary algorithms where a large number of realisations of the process is run in parallel, and subject to selection based on their performance. Optimising such algorithms and providing rigorous bounds on convergence properties continues to be a question of significant research interest across disciplines, and we recently completed a first contribution in collaboration with Letizia Angeli (PhD Enrichment Student), Adam Johansen (Data-Centric Engineering Group Leader) and Andrea Pizzoferrato (Research Associate).

Stefan recently started working on the impact of the monetary system on wealth inequality, as a natural application area for agent-based modelling of aggregation phenomena. The aim is to build on foundational work from econophysics in combination with data from the UK economy, to gain a detailed understanding of monetary dynamics and its role during and since the financial crisis in 2008. This is related to the Turing's economic data science programme, and current collaborators are Alexander Karalis Isaac (Warwick Economics) and Samuel Forbes (PhD student at the CDT Mathematics for Real-World Systems in Warwick).