About Me
As a Machine Learning Engineer and recent PhD graduate (Dr. Ing.), I am driven by a profound interest in applying artificial intelligence to tackle complex challenges in the medical domain. My expertise lies in architecting and implementing deep learning solutions for medical imaging, with the goal of significantly improving diagnostic processes and patient outcomes. I am deeply involved int the entire lifecycle of AI development, from research and sophisticated model design to robustly integrating these solutions into production environments.
My Approach to Medical AI
I believe in a collaborative, data-driven approach, working closely with clinicians to ensure AI tools are not only technologically advanced but also clinically relevant, interpretable, and seamlessly integrated into existing workflows. My focus is on creating solutions that empower medical professionals and lead to measurable improvements, prioritizing real-world impact.
Career Journey
Advanced Machine Learning Engineer
Snke OS GmbH - a Brainlab subsidiary | Munich, Germany
March 2023 - Present
- Leading two machine learning projects focused on different tasks in CT/MR scans.
- Developing foundation models for MR images, trained on large datasets for use across multiple teams.
- Leading data annotation campaigns with external companies, including defining annotation guidelines.
- Core contributor to the machine learning software platform, ensuring structured and repeatable ML results through integration of modern development tools.
- Supervising a research collaboration with CAMP Lab, TUM, for synthetic data generation and segmentation.
PhD Student
Pattern Recognition Lab (Prof. Maier), Friedrich-Alexander-Universität Erlangen-Nürnberg | Erlangen, Germany
August 2020 - November 2023
- Doctoral research on "Detection and quantization of rheumatic diseases using deep learning for various imaging modalities."
- Published research in well-recognized scientific journals and presented at international conferences.
- Awarded for best work and presentation at QMSKI 2022 in the Netherlands.
- Explored side topic: Training of 2D diffusion models for generation of synthetic breast MRI and X-ray images.
- Served as a Tutor for the Deep Learning course.
- My doctoral work culminated in the 'DeepNAPSI' tool (featured below), demonstrating a novel application of AI for psoriasis assessment.
Intern in Medical Technology R&D
Siemens Healthineers | Princeton, USA
October 2019 - April 2020
- Worked on the classification of Rotator Cuff tears in MRI using Neural Networks.
- Developed software for the automatic evaluation of radiologists' annotations.
- Focused on programming with clean code principles.
Working Student in Medical Technology Development
Siemens Healthineers | Forchheim, Germany
January 2019 - October 2019
- Contributed to the annotation of medical images.
- Trained neural networks for patient table segmentation in CT images.
- Participated in joint evaluation of project progress within the team.
Intern and Working Student in Medical Technology R&D
Dräger Medizintechnik | Lübeck, Germany
October 2017 - April 2018
- Developed prototypes for the ventilation of infants.
- Programmed microcontrollers for medical devices.
- Conducted system modeling of gas mixtures for anesthesia machines.
Education
Doctor of Philosophy (Dr. Ing.)
Friedrich-Alexander-Universität Erlangen-Nürnberg
2020 - 2023
Thesis: Detection and quantization of rheumatic diseases using deep learning for various imaging modalities.
Final Grade: Very Good
Master of Science, Medical Engineering
Friedrich-Alexander-Universität Erlangen-Nürnberg
2017 - 2020
Thesis: Classification of Rotator Cuff tears in MRI using Neural Networks.
Research Project Publication: Dilated deeply supervised networks for hippocampus segmentation in MRI.
Final Grade: 1.3
Bachelor of Science, Medical Engineering
Universität zu Lübeck
2014 - 2017
Thesis: Development of a module for mixing gases for a modularized anesthesia machine.
Final Grade: 1.7
Abitur
Gymnasium Ernestinum Celle
2006 - 2014
Final Grade: 2.4
PhD Project Highlight: DeepNAPSI
This demo showcases 'DeepNAPSI', a key project from my PhD focused on the "Detection and quantization of rheumatic diseases". We employed deep learning techniques to automatically segment nail regions from hand photographs. These extracted nail images are then individually processed by a classification network to determine the Nail Psoriasis Severity Index (NAPSI) score for each nail. Finally, the application aggregates these individual scores to calculate the total NAPSI score for the patient, providing a quantitative measure of psoriasis severity in the nails.
DeepNAPSI is a prime example of my commitment to translating complex research into user-friendly tools that address real clinical needs and have the potential to directly benefit patient assessment and treatment strategies.
Technical Skills & Personal Interests
Technical Skills
I leverage a diverse technical toolkit to develop and deploy robust AI solutions in the medical domain:
Languages & Hobbies
German: Native
English: Full professional proficiency
Spanish: Novice
Beyond technology, I enjoy Swimming, Running, Road biking, and Ski touring. These activities reflect the dedication and endurance I bring to my professional work.