NCT Data Science Seminar

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We have all been there: we read about an exciting new method in a paper, only to discover that the accompanying code is missing, incomplete, or nearly impossible to run—far from allowing us to reproduce the reported results. In the fast-paced world of looming computer science, machine learning, and computer vision conference deadlines, ensuring reproducibility often takes a back seat. This problem can also be seen or is even more pronounced for medical applications, where datasets are often not publicly available.
In this talk, I will share our experiences from a joint initiative between the University of Erlangen (Bernhard Egger and Andreas Kist) and the University of Würzburg (myself) to address this issue by integrating reproducibility into the curriculum for AI and computer science students. After first experiences with a dedicated Reproducibility Hackathon, we have subsequently established student projects for both Bachelor’s and Master’s students, focusing on reproducing results from published research papers. I will discuss the lessons we have learned, the challenges we have encountered, and our efforts to embed reproducibility as a core element of student education.

Bio:
Katharina Breininger leads the Pattern Recognition Group at the Center for AI and Data Science at the University of Würzburg. With her team, she develops labeling strategies and robust machine learning approaches for small-data settings in different interdisciplinary domains, with a focus on medicine and medical imaging. 
After studying computer science in Marburg and Erlangen, she completed her PhD on image fusion during minimally invasive interventions at the Pattern Recognition Lab (Friedrich-Alexander-University Erlangen-Nürnberg) and Siemens Healthineers. Before joining the University of Würzburg in 2024, Katharina served as an assistant professor at FAU Erlangen-Nürnberg, leading the "Artificial Intelligence in Medical Imaging" group.