I received my M.Sc. in Applied Statistics and B.Sc. in Economics from the University of Göttingen. In my studies I focused on Machine & Deep Learning, (Bayesian) Generalized Linear Regression methods and Econometrics respectively. My thesis concerned itself with extracting main effects from Bayesian Neural Networks using grouped shrinkage priors and splines; inferring its parameters using Stochastic Gradient Markov Chain Monte Carlo methods.
Since Sep. 2021, I am pursuing my PhD as a member of Prof. Lindauer’s group. My current research interests are Bayesian- & multi-fidelity optimization and meta-learning, aiming at boosting the performance of machine learning algorithms by choosing appropriate hyperparameters in a data driven, principled and efficient manner. My distinct objects of study are Knowledge Graphs and Graph Neural Networks. Recently, I am interested reinforcement learning for algorithm selection.
