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Ask why patterns exist before deciding to change them
Only add complexity that justifies its existence
Classical ML or LLMs. Whatever the problem actually needs
From academic research to enterprise AI applications
During my PhD, I worked on machine learning for super-resolution microscopy. Early on, my instinct was to build things from scratch—to understand every moving part by re-deriving it myself.
Over time, the more interesting work happened when I stopped asking "how do I replace this?" and started asking "why does this pattern exist in the first place?" Combining, adapting, and slightly improving existing ideas turned out not to be a lack of originality, but how complex systems actually grow.
I learned to pay attention to which ideas survived repeated use, where they broke down, and how small changes propagated through the system. In practice, novelty often came from understanding rather than invention.
"Understanding accumulated solutions is a form of originality."
Today, I apply the same way of thinking when building AI systems inside existing enterprise landscapes:
Running on BW/4HANA
Via Cloud Foundry & SAP AI Core
Tools, feedback loops, deployment paths
LoRA, chosen pragmatically
Deep down, I'm still a scientist. I like understanding things properly. I like systems that behave predictably, and complexity that earns its keep.
For now, I build things that run, learn from the parts that don't, and keep choosing problems that are difficult for the right reasons.
How I balance work, training, and recovery
Key milestones in my professional development
Enterprise AI implementations and showcases. Bridging academic research with large-scale business applications and system integrations.
Developed and launched full-stack AI platform for endurance sports analytics. Demonstrates end-to-end product development from research to production.
University of Wurzburg - Developed ReCSAI and other novel AI algorithms for super-resolution microscopy. Published 12+ papers with 372+ citations.
Built foundational expertise in biophysics, image processing, and early AI applications. First publications in correlative microscopy and image registration.
Core competencies across the AI development stack
How endurance sports shape my approach to research and innovation
Triathlon isn't just a hobby - it's a laboratory for understanding human performance, data optimization, and the psychology of pushing limits. The same methodical approach I apply to training periodization directly influences how I structure AI research projects.
Racing Ironman distances has taught me that breakthrough innovations, like endurance achievements, require patience, systematic progression, and the ability to maintain focus during extended periods of uncertainty. These qualities are essential when developing novel AI architectures or debugging complex neural networks.
European Championship
German Triathlon League
1203km, 17700m elevation
Sports analytics platform
Research presentations and speaking engagements
Conference Presentation
Team Collaboration
Award Ceremony
Ready to tackle your most challenging AI problems with a research-driven, results-focused approach?