Head of the group Mathematical Methods in Environmental Research, at the Department of Systems Analysis, Integrated Assessment and Modelling
Ph.D. in Theoretical Physics, Dr. Phys. ETH
M.Sc. in Mathematics, Dipl. Math. ETH
Modelling of environmental systems
I apply methods from statistical physics, nonlinear systems theory and statistics to detect and model predictable features of complex environmental systems. Of particular interest is a faithful quantification of uncertainty.
Bayesian Data Science
Quantifying the parametric uncertainty of a model that needs to be calibrated to data is a computationally hard problem, in particular, if the model is slow or stochastic. Statistical physics and non-equilibrium thermodynamics offer some great tools to make parameter inference with stochastic models more efficient. Mechanistic emulators are an efficient way of speeding up slow simulators and make them amenable to simulation-intense tasks such as parameter inference.
Courses Summer School in Environmental Systems Analysis