Hi there, I’m Eike.

I am a Senior Scienstist working in Medical AI at Fraunhofer Institute for Digital Medicine MEVIS and a guest researcher at the radiology department of Hannover Medical School (MHH).

In my research, I aim to contribute to the robustness, fairness, and overall trustworthiness of AI and machine learning models in medicine. This encompasses questions such as:

While I mostly work in deep learning-based medical image analysis, my interests also span the broader medical domain, including, e.g., EHR and time series data.

I am also a co-organizer of the FAIMI initiative for the fairness of AI in medical imaging. We regularly organize workshops and other events - if you want to keep up to date with what we’re doing, you should sign up for our newsletter or join our Slack community!

Finally, I am also involved in the AI Act’s implementation in terms of harmonized technical standards. Specifically, I am contributing to the development of a technical standard on bias assessment and mitigation in CEN/CENELEC JTC21, first via the Dutch NGO AlgorithmAudit and now via NoLeFa-84.

Recent news

Previously, I was a postdoctoral researcher at DTU Compute, in the section for Visual Computing. Within the project Bias and Fairness in Medicine, I (together with my amazing collaborators) worked on the fairness of risk score models in medicine.

Pre-Previously, I worked at the University of Lübeck, in the Institute for Electrical Engineering in Medicine. In a project executed together with the research unit of Dräger Medical, we worked hard to bring surface electromyographic monitoring of respiratory effort into clinical practice for improving mechanical ventilation. My research in this context spanned mathematical modeling, signal processing, parameter identification & statistical inference, all related to either surface electromyographic measurements, respiration, or both. See my previous publications for some of the work we did.