Philipp studied mathematics at the University of Würzburg, graduating with a final grade of 1.0.

His studies focused on stochastics, particularly sampling theory under prior information, which was the topic of his bachelor’s thesis; Carleson measures, which he investigated within the “Measure Theory” working group; and multivariate statistics, especially online PCA algorithms, which he explored in his master’s thesis.

Philipp joined our lab as a PhD student in May 2021. He is working on deep learning theory for causal learning, presented in his NeurIPS 2024 paper, “Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention.” He is also involved in deep learning applications in biology, focusing on codon optimization and therapeutic RNA sequence optimization. Additionally, he is working on a deep learning application for logistics in collaboration with Green Convenience GmbH.

Philipp is also interested in SELEX and collaborated with the Suess group at TU Darmstadt on two publications.