I am a PhD student at the machine learning group of Prof. Dr. Ulf Brefeld at the Leuphana University of Lüneburg. From April 2019 till March 2023 I was employed at SAP SE, Berlin. Since April 2023 I am a research assistant at the Leuphana. Prior to my PhD studies I received a Master of Science in Management & Data Science from Leuphana University of Lüneburg and a Bachelor of Science in Economics from University of Hagen. I also hold a Diploma in Fine Art from Braunschweig University of Art and have a professional background in publishing.

Within my PhD studies I focus on machine learning problems and solutions involving sequential data with sets of features, such as multiagent trajectories in team sports and student interaction data within the educational domain. This has led to research on generative models such as variational autoencoders, graph neural networks and transformer based architectures for learning interactions, as well as self-improvement training schemes and self-supervised pretraining.

• Exercise: Introduction to Artificial Intelligence (S25)
• Lecture: Introduction to Artificial Intelligence, Professional School (W24/25)
• Exercise: Statistics for Computer Scientists (W24/25)
• Exercise: Machine Learning and Data Mining (W23/24)
• Exercise: Statistics for Computer Scientists (W23/24)
• Exercise: Forcasting and Simulation (S23)
• Exercise: Introduction to Artificial Intelligence (S23)
• Exercise: DATAx, Data analysis with Python (W22/23)

• Y. Rudolph and U. Brefeld. Masked Autoencoder for Multiagent Trajectories. Machine Learning, 114, 44, 2025. [link]
• Y. Rudolph and U. Brefeld. Masked Autoencoder Pretraining for Event Classification in Elite Soccer. Workshop on Machine Learning and Data Mining for Sports Analytics @ ECML PKDD, 2023. [link]
• Y. Rudolph and U. Brefeld. Modeling Conditional Dependencies in Multiagent Trajectories. International Conference on Artificial Intelligence and Statistics, 2022. [link]
• Y. Rudolph, S. G. Fadel, S. Mair, and U. Brefeld. Studying the Propagation of Information in VAE Decoders. (abstract). Northern Lights Deep Learning Conference, 2022.
• Y. Rudolph, U. Brefeld and U. Dick. Graph Conditional Variational Models: Too Complex for Multiagent Trajectories?. I Can’t Believe It’s Not Better Workshop @ NeurIPS, 2020. [link]
• S. Mair, Y. Rudolph, V. Closius and U. Brefeld. Frame-based Optimal Design. Proceedings of the European Conference on Machine Learning, 2018. [link]