Daniel is an AI researcher specializing in Medical Imaging. He works in an interdisciplinary collaboration at Ulm University between the Clinic of Radiology and the Visual Computing Group (Website: Radiology–VISCOM collaboration). He received his Master’s degree in Electrical Engineering with a focus on automation technology in November 2020.
Research
Daniel’s goal is to investigate and advance AI algorithms to assist physicians and improve patient care.
His main research areas are:
- Self-Supervised Pre-Training: Pre-training AI models on large-scale medical imaging datasets using methods such as masked autoencoders and contrastive learning. (e.g., Scientific Reports 2023 paper)
- Vision-Language Models: Evaluating Vision-Language Models (e.g., ChatGPT, Gemini) on medical images and analyzing their limitations. (e.g., MICCAI 2025 paper)
You can find all his publications on Google Scholar.
Further, Daniel has served as a reviewer for the journals IEEE Transactions on Medical Imaging, Computers in Biology and Medicine (Elsevier), Scientific Reports (Nature), and the MICCAI conference.
Projects
Racoon:
Daniel is the local lead computer scientist in a national initiative connecting radiology departments across Germany for AI-based research. The project is funded by the German Federal Ministry of Research, Technology and Space (BMFTR). Website Racoon
His tasks are:
- Setting up and maintaining server infrastructure
- Working on ethical and data security clearances
- Installing, maintaining, and coordinating AI research software in collaboration with industry partners (e.g., Brainlab, Mint Medical, ImFusion)
- Training clinical staff in the use of AI research tools and software
KEMAI:
Since 2025, Daniel is an associated researcher in the Graduate School for Medical AI in Ulm (KEMAI). The program is funded by the German Research Foundation (DFG).
Website KEMAI
Teaching
Daniel is passionate about teaching and is currently completing the Baden-Württemberg Certificate for Teaching in Higher Education.
His teaching activities include:
- Lecture: Deep Learning with PyTorch (Bachelor Computer Science; Modules on Self-Supervised Pre-Training and Vision-Language Models)
- Lecture: Medizinische Bildanalyse (Master Computer Science; Modules on Self-Supervised Pre-Trainign and Vision-Language Models in Medicine)
- Project Course: Visual Deep Learning (Bachelor Computer Science; Supervision of student projects in Medical AI)
- Project Course: Advanced Visual Deep Learning (Master Computer Science; Supervision of student projects in Medical AI)
- Seminar Course: Research Trends in Visual Computing (Master Computer Science; Coordination of a seminar on literature research and scientific writing in medical AI)
- Thesis: Supervision of Bachelor’s and Master’s theses on topics in medical AI
Supervised Students
- Omar Abdelbaky: Bachelor’s Thesis (2026) Vision-Language Models for Medical Imagining
- Jessica Thiessen: Project (2025) Attention in Vision-Language Models; Master’s Thesis (2026) Bias in Vision-Language Models
- Simon Abel: Project (2025) Prompting Vision-Language Models; Master’s Thesis (2026) AI agents for medical image analysis
- Casey Benjamin Thiessen: Project (2025) Attention in Vision-Language Models; Master’s Thesis (2026) Attention in SAM
- Patricia Stöhr: Master’s Thesis (2025) Test Time Training in Medical Imaging
- Tharani Srinivasan, Gunjan Yadav: Project (2025) Fine-Tuning Vision-Language Models
- Patrick Moravec, Jerome Schwander: Project (2025) Medical Volume Segmentation
- Helena Kaczmarek, Alina Gerl: Project (2025) Medical Volume Segmentation
- Marina Grigoreva: Master Thesis (2024) From 2D slices to 3D volumes in medical imaging
- Sabitha Manoj: Project (2023) Radiomics
- Konstantin Müller: Project (2023) Radiomics
- Gesa Mittmann: Master Thesis (2022) Sarcopenia detection with Deep Learning
- Monalisa Nayak: Project (2022) 3D Medical Image Visualization
Talks
- Talk: Pharma KI Konferenz Concept Heidelberg (2025) Karlsruhe Link
- Paper Oral: German Conference on Medical Image Computing (2026) Lübeck Link Award for the Best Oral Presentation
- Keynote: Jahrestagung der Südwestdeutsche Gesellschaft für NUKLEARMEDIZIN (2025) Karlsruhe Link
- YouTube: Intro into Generative AI at the Clinical AI Academy Channel (2026) Link
- Talk: ECA Academy AI Conference (2025) Copenhagen Link
- Guest Lecture: Copenhagen Business School, Department of Digitalization, Prof. Maike Greve (2025)
- Talk: GMP Pharma-Kongress (2025) Wiesbaden Link
- Talk: Pharma KI Konferenz Concept Heidelberg (2025) Mannheim Link
- Podcast: ChaosHacker-Talk (2025) Link
- Keynote: ISPE France & GAMP Francophone - Intelligence Artificielle en environnement Pharma BPx/GxP (2024) Mulhouse Link
