Biography. I am a Machine Learning Researcher/Postdoc at the Heinz College for Information Systems, Public Policy and Managment at Carnegie Mellon University, Pittsburgh, PA USA under the supervision of Prof. Leman Akoglu. 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 PhD at the Chair of Data Science and Data Engineering of TU Dortmund University, Germany under the supervision of Prof. Emmanuel Müller in 2025, with a focus on improving anomaly detection methods. I achieved this by making them more accurate, performant, explainable and adaptable. Before this, 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.

Research Interests. I am interested in building reliable, efficient and powerful machine learning and data mining algorithms. Specifically, my research centers around making anomaly detection algorithms more usable and the ability to abstract learned knowledge into tabular foundation models.

Research Keywords: (1) Machine Learning and Data Mining, (2) Anomaly/Outlier/Out-of-Distribution (OOD) Detection and Unsupervised ML, (3) Tabular Foundation Models (4) Abstraction, (5) Re-Identification, (6) Ensemble Methods, (7) Automated ML, (8) AI for Science