School of Mathematics

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Probability, Stochastic Modelling and Financial Mathematics

Probability, Stochastic Modelling and Financial Mathematics

Our research focuses on the study and modelling of systems and processes featured by uncertainty and/or complexity, using advanced theoretical, simulation and numerical methods. It covers a vast variety of modern topics both in probability (including theory of random processes and stochastic analysis) and in a wide range of applications in mathematical and other sciences, spanning from nonlinear dynamical systems and mathematical physics through mathematical biology and complexity theory to mathematical finance and economics.

Research areas
  • Stochastic (partial) differential equations, including smoothing, filtering, control, and numerical methods.
  • Markov processes including applications in cell biology, immunology and social dynamics.
  • Mathematical finance and economics, including portfolio management, hedging, and evolutionary finance.
  • Queuing theory including communication systems and networks.
  • Multivariate extreme value modelling with applications in transport pollution problems.
  • Evolutionary game theory with applications in behavioural science and complexity theory.
  • Statistical physics of disordered systems and random media theory.
  • Random combinatorial structures including random partitions.
  • Stochastic particle dynamics, including applications in spatial ecology.
  • Modern methods in numerical sampling, including molecular dynamics and MCMC.

For further information about our research please visit our Group pages