I build and study machine learning systems at the boundary of theory and deployment — where inference, sequence models, and hardware interact under real constraints.
I came to computer science via boatbuilding — three years at a racing yacht yard in Kiel before moving into a CS degree at TU Hamburg. That background still shapes how I think: materials have constraints, structures have failure modes, and the most interesting problems sit at the boundary between what is theoretically possible and what actually holds together.
My work focuses on machine learning systems in real environments — models that operate under latency budgets, on constrained hardware, and with imperfect or shifting signals. I’m interested in how architectural choices propagate into behavior once a model leaves the lab, and how this behavior can be made stable, predictable, and understandable.
A recurring theme in my work is sequence modeling as an alternative to attention in domains where its inductive biases better match the structure of the problem. More broadly, I’m interested in how representations, constraints, and system design interact — and how small changes at one level shape outcomes at another.
I’m currently working on inference and system-level questions in applied settings, including model optimization and multi-agent architectures, with experience spanning both industrial research and applied deployments.
Alongside that, I care about how these systems present themselves — how interfaces, feedback, and interaction design influence whether a system feels reliable or opaque.
Outside of work, I run, read more than I should, and keep a paper notebook that doesn’t sync anywhere.
If any of this resonates — or you just want to trade notes — I'd like to hear from you.