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We are interested in the statistical analysis of complex stochastic models, with a particular focus on the development and in-depth investigation of advanced data-based methods for inference. Our research ranges from the analysis of probabilistic properties of processes described by stochastic (partial) differential equations to the development of novel adaptive statistical procedures and the construction of data-driven methods for stochastic control.


The activities are funded by the projects "Exploring the potential of nonparametric modelling of complex systems via stochastic partial differential equations" (Carlsberg Foundation Young Researcher Fellowship grant ) and "Learning diffusion dynamics and strategies for optimal control" (Research grant from Independent Research Fund Denmark). See PURE for more funding details.