Biography. I am a Machine Learning Researcher/Postdoc at the Chair of Data Science and Data Engineering at TU Dortmund University, Germany under supervision of Prof. Emmanuel Müller and working with Prof. Leman Akoglu from Carnegie Mellon University. I am working on machine learning and data mining algorithms, with a focus on anomaly detection, machine learning abstraction, ensemble methods and contrastive learning. I am also interested in the application of machine learning in science and industry. I received my M.Sc. degree in Theoretical Particle Physics from RWTH Aachen University, Germany, in 2021. During my master's thesis, I worked on the detection of signs of new physics through anomaly detection. Afterwards Prof. Müller gave me the opportunity to join his group and work on more fundamental anomaly detection research. This finally resulted in me earning my PhD in June 2025 with a focus on better anomaly detection methods.

Research Interests. I am interested in building reliable, efficient and powerful machine learning and data mining algorithms. Specifically, my research centers around anomaly detection algorithms, the ability to abstract learned knowledge, ensemble methods and contrastive learning.

Research Keywords: (1) Machine Learning and Data Mining, (2) Anomaly/Outlier/Out-of-Distribution (OOD) Detection and Unsupervised ML, (3) Abstraction, (4) Re-Identification, (5) Ensemble Methods, (6) Automated ML, (7) AI for Science