Teaching Profile
My teaching focuses on machine learning fundamentals, with an emphasis on bridging theoretical foundations and practical ML system design. I highlight reliability, generalization, and real-world deployment throughout my courses.
Teaching Profile
My teaching focuses on machine learning fundamentals, with an emphasis on bridging theoretical foundations and practical ML system design. I highlight reliability, generalization, and real-world deployment throughout my courses.
Core courses in Artificial Intelligence Curriculum
Machine Learning: Basic Techniques, WS25, WS26
Machine Learning: Advanced Techniques, WS26
Learning Theory and Applications, SS23, SS25, SS26, SS27 [skript]
Advanced Seminars & Series
International Seminar on Learning Theory, JKU–TU Braunschweig–UNI Genova–UNI LUT, 2023-Now [link]
Industrial Research Seminar S3AI, 2020-2022 [link]
Examination & Academic Service
AI Master Exams, examiner, WS24 - Now
PhD Pre-defenses, examiner, WS24 - Now
Supervision
I supervise and co-supervise PhD and MSc students in machine learning, domain adaptation, and robustness, often in close collaboration with academic and industrial partners.
PhD Students
Stephanie Holly, Guidance for generative models (ongoing)
Midhun Bhaskaran, Representation learning for two-sample testing (ongoing)
Chiara Angeloro, Robustification of agentic systems (ongoing)
Maximilian Dürr, Robustness of agentic engineering systems (ongoing)
Lukas Gruber, Density ratio estimation (ongoing, co-supervision with Sepp Hochreiter)
Marius-Constantin Dinu, Parameter choice and neuro-symbolic approaches for domain-invariant learning (co-supervised with Sepp Hochreiter), 2024
Hoan Duc Nguyen, Regularization in RKHSs for covariate shift adaptation (co-supervised with Sergei Pereverzyev), 2023
MSc Theses (selected)
Andrea Huber, Parameter choice methods for unsupervised domain adaptation, 2023
Johannes Zigsch, Domain generalization by deep learning-based domain encoding, 2023