Translating world-class research into clinical solutions for one of the world's most complex problems: cancer.
I got into oncology the way most people get into something that truly matters - not by plan, but by circumstance. Watching people close to me navigate a healthcare system that was, in theory, one of the best in the world, I kept hitting the same wall: diagnoses that came too late, too incomplete, with too much left to chance. If that was the reality in Europe, I couldn't stop thinking about what it looked like for someone facing the same disease in a low-resource setting, with a fraction of the access.
These thoughts became an obsession. Imagine a system with access to all the data that has ever been collected: clinical history, diagnoses, treatments, survival data - a system that is available in every healthcare system around the world, has seen the most diverse collection of patients ever, and out of experience knows best what works and what doesn't for each patient individually: the world's oncologist. I truly believe that we live in a time where this concept is no longer science fiction, and lives in a complex intersection of oncology, biology, AI, ethics, regulatory, and financial return on investment, with patient benefit at the center.
My entry point into that vision is pathology: tissue slides that are routinely collected in hospitals everywhere, yet rarely fully utilized. They contain biological signals too complex for the naked eye, too expensive to extract with standard assays at scale, but learnable - if you know how to teach a model to look. My research is about showing what's scientifically possible, publication by publication. StratifAI is about turning those possibilities into solutions that work in the clinic, not just in a paper - and making them accessible far beyond the top academic medical centres.
When I'm not building StratifAI, I read philosophy, seek out great coffee, and find quiet corners of the city to think. I'm drawn to people building things that are genuinely difficult and would have huge impact if they succeed. I grew up in the Netherlands with roots in Egypt, built my research career in Germany, and have since expanded to the US - pursuing my passion has made me a world citizen, and it's only the beginning.
Doctoral research on deep learning for gigapixel whole-slide image analysis - weakly supervised regression models, multi-task transformers, and multimodal AI for novel biomarker development. Supervised by Prof. Jakob Nikolas Kather.
Always open to conversations with ambitious people looking to build or invest in healthcare AI, speaking invitations, and press. If you're working on something hard that matters - reach out.