Vorträge

In this talk, we present new explicit bounds on the Gaussian approximation of Poisson functionals, based on novel estimates of moments of Skorohod integrals. Combining these with the Malliavin-Stein method, we derived bounds in the Wasserstein and Kolmogorov distances whose application requires minimal moment assumptions on add-one cost operators — thereby extending the results from (Last, Peccati and Schulte, 2016). Our main application is a CLT for the Online Nearest Neighbour graph, whose validity was conjectured in (Wade, 2009; Penrose and Wade, 2009). We also applied our techniques to derive quantitative CLTs for edge functionals of the Gilbert graph, of the k-Nearest Neighbour graph and of the Radial Spanning Tree, both in cases where qualitative CLTs are known and unknown.

This talk will be devoted to several topics in classical and modern control theory. Classical stabilization techniques for linear and nonlinear control systems as well as modern attempts to linearize nonlinear systems will constitute the core for this presentation. In particular, the first part will consist of a brief summary of my Master's Thesis on the foundations of mathematical control theory in finite dimension. During the second part, we will catch a glimpse into modern control theory involving the Koopman operator focussing on advances and difficulties. The third part will be on my current PhD topic "Time-Optimal Control of Linear Systems in Non-Reflexive Banach Spaces". The official introduction to my person will also not come too short.

This talk will start with a high-level overview on how machine learning can be used to improve weather and climate predictions. Afterwards, the talk will provide more detail on recent developments of machine learned weather forecast models and how they compare to conventional models and numerical methods.