Tim Ruhkopf received his M.Sc. in Applied Statistics and B.Sc. in Economics from the University of Göttingen. In his studies, he focused on Machine & Deep Learning, (Bayesian) Generalized Linear Regression methods and Econometrics respectively. His 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, he is pursuing his Ph.D. as a member of Prof. Lindauer’s group.
His current research interests are Bayesian- and multi-fidelity optimization in particular, aiming at boosting the performance of machine learning algorithms by choosing appropriate hyperparameters in a data-driven, principled and efficient manner. His distinct objects of study are Knowledge Graphs and Graph Neural Networks.