Teaching models to see in three dimensions, from millions of CT and MRI volumes.
I’m a Machine Learning Engineer and Researcher at the Medical Image Computing group of the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg, Germany, specializing in 3D radiological image analysis. Over the last two years, my work has strongly focused on large-scale self-supervised representation learning for medical imaging, alongside interactive and applied segmentation research.
Co-lead of the Human Radiome Project, which aims to develop a radiological 3D vision foundation model trained on 4.8 million 3D images to improve diagnostics and support treatment decisions across diverse populations.
Frequent maintainer, contributor and daily user of nnU-Net, and many of my projects build upon its philosophy of strong, generalizable baselines extended toward interactive and foundation-model–based systems.
Large-scale self-supervised representation learning DINOv2 / DINOv3
My main research focus over the past two years has been DINO-based foundation models for medical imaging. Within the Human Radiome Project I worked extensively with DINOv2, contributing to large-scale pretraining on more than 2.5 million 3D CT and MRI volumes. I currently work with DINOv3, focusing on parameter-efficient fine-tuning (PEFT) strategies across medical and non-medical image domains.
Methodological & interactive deep learning
3D medical image segmentation methods with an emphasis on weak supervision, interactive and prompt-based segmentation, and usability-driven model design, including large-scale benchmarking, Napari-based interactive systems, and scalable training and inference pipelines.
Applied & clinical projects
Collaboration with clinical partners on applied radiological segmentation tasks, translating methodological advances into clinically meaningful and validated systems.
Tooling & infrastructure
Design and maintenance of open-source tools for patch-based processing, large-volume data handling, compressed storage, experiment configuration, and reproducible research workflows.
publications