I am a PhD student in the machine learning group of Prof. Dr. Ulf Brefeld at Leuphana University Lüneburg. Prior to that, I received a Master of Science in Management & Data Science from Leuphana and a Bachelor of Science in Business Administration from the Hamburg School of Business Administration. Currently, I work on the SynTrace project which aims at advancing machine learning methods for the privacy-preserving generation of synthetic movement datasets, especially for smart city applications and web analytics.

My research interests lie in the area of generative models for temporal data such as GPS sequences, click trajectories in the web, or music. A current focus is the exploration of methods for learning well-structured representation spaces, for example by using non-Euclidean geometry. One of the results of this work is Geodesic Ball Regularisation, a lightweight regulariser I proposed together with Tino.

• Deep Learning (Exercise; S26)
• Applied Machine Learning for Smart and Connected Systems (Lecture/Exercise; S26)
• Business Analytics (Tutorial; W24/25)

• M. Reinhardt, T. Paulsen, U. Brefeld. Regularising Latent Representations with Geodesic Balls. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2026. [paper] [code]
• M. Reinhardt, J. Scharfenberger, B. Funk. GUT-IS: A Data-Driven Approach to Integrating Constructs and Their Relations in Information Systems. Proceedings of the European Conference on Information Systems, 2026. [paper] [code]