About Me

Machine Learning Engineer bringing AI to industries where it actually matters: healthcare, next-generation wireless communications, and consumer health.

Whether it's helping clinicians navigate complex medical knowledge, enabling smarter infrastructure for 6G networks, or building everyday tools that people genuinely use, my focus is on taking AI from research all the way to something real and deployed. I work across domains by design. Based in Germany, open to relocation across Europe.

I work well both independently and as part of a team. I've led my own experiments, mentored students, and collaborated closely with clinical domain experts and hardware engineers. I'd describe myself as a generalist at heart: I learn fast, I'm comfortable moving across domains, and I find that the most interesting ML problems rarely stay inside one box.

That's also what keeps me genuinely excited about this field. Every project has its own story, its own constraints, and needs a fresh approach. There's no copy-paste solution.

Looking ahead, I think the most interesting problems won't be about beating benchmarks. Foundation models have raised the floor dramatically, and the real room for progress now is in efficiency, reliability, and trust: making models smaller and faster, and making industries that handle critical systems actually confident enough to deploy them. I also think robotics and reinforcement learning are quietly becoming the next frontier, as embodied intelligence is still a wide-open problem.