I firmly believe that being a researcher goes far beyond just publishing papers. Through dedicated and engaging teaching, as well as mentoring students via thesis supervision, we can make a far greater impact than any single publication could achieve. As such, I purposely devote a substantial amount of my time to contribute to the growth of future researchers and other professionals.

• 2024 Bernold, N. “Generative B-Cos Networks”
• 2024 Hoffmann, W. “Post-Hoc Stochastic Concept Bottleneck Models”
• 2024 Ebeling, N. “Time and Spectral Diffusion Models for Cardiac Time-Series”
• 2024 Makonnen, M. “Measuring Leakage in Concept Bottleneck Models”
• 2024 Carballo, A. “Interpretable Capabilities of Concept-Enhanced Diffusion and Prototype Networks”
• 2024 Dekas, D. “Clinical Evaluation of Antibiotic Reducing ML Method”
• 2024 Gonçalves, J. “Enhancing TreeVAE with Diffusion Models”
• 2023 Scherrer, D. “DREAM: Detecting and Reducing Excessive Antibiotic Medication using ML”

• 2024 Advanced Machine Learning (252-0535-00)
• 2023 – 2024 Machine Learning for Healthcare (261-5120-00)
• 2023 Foundations of Computer Science (252-0852-00)
• 2019 – 2022 Statistics (03SM22AOEC10)
• 2019 – 2022 Introductory Econometrics (BOEC0004)

• 2024 Neural Information Processing Systems
• 2024 Models of Human Feedback for AI Alignment Workshop at ICML
• 2023 Deep Generative Models for Health Workshop at NeurIPS