Department of Statistics

Applied statistics including spatio-temporal statistical modelling, Bayesian statistics, uncertainty analysis, financial risk modelling, optimal design of experiments, industrial statistics, and, more recently, statistical learning methods.

Environmental statistics and risk evaluation including environmental monitoring networks, environmental health risk analysis, pollution dispersion modelling, stochastical differential equations, and pattern recognition.

Computational statistics including the development of specific packages in R and the numerical solution of applied problems.

Stochastic processes including numerical methods for stochastic differential equations, stochastic analysis, (quasi-)Monte Carlo methods, stochastic optimization and stochastic optimal control, quantitative financial mathematics, insurance mathematics, machine learning for SDEs.