I am a PhD student in the machine learning group of Prof. Dr. Ulf Brefeld at Leuphana University of Lüneburg. From April 2019 to March 2023, I worked at SAP SE in Berlin. From April 2023 to September 2025, I have been a research assistant at 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 the University of Hagen. I also hold a Diploma in Fine Art from Braunschweig University of Art and have a professional background in publishing.

My research centers on modeling set-structured trajectory data, such as multiagent trajectories in team sports and student interactions in educational settings. I investigate graph neural networks and transformer-based architectures to capture interactions both across sets of trajectories and within set-structured features of individual trajectories. Together with colleagues, I have published work contributing to technical advances in probabilistic modeling, self-supervised pretraining, representation learning, structured prediction, and policy learning for set-structured trajectory data.

• 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)

• K. Neubauer*, Y. Rudolph*, and U. Brefeld. Principled Transformers for Predictive Performance in Knowledge Tracing. Journal of Educational Data Mining, 18(1), 89-112, 2026. [link]
• Y. Rudolph*, K. Neubauer*, and U. Brefeld. Self-improvement for Computerized Adaptive Testing. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025. [link]
• 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]