Probabilistic modelling
Bayesian inference, Gaussian processes, active learning, uncertainty quantification, and simulation-based reasoning.
Hi, I’m Jonas!
I turn complex data into systems that work.
I’m a quantitative researcher, scientific software developer, and co-founder of Bright Interface GmbH, where I serve as Managing Director and CTO. I lead the development of integrated data, automation, and AI systems for medical practices while continuing my research in probabilistic machine learning, Bayesian inference, and cosmology as a postdoc at KBFI/NICPB in Tallinn.
From mathematical model to usable software.
My work usually starts with a complex, fragmented, or statistically difficult problem and ends with a system that can be tested, documented, and used. That applies both to scientific inference and to operational software: understanding uncertain data, designing reliable computational methods, and turning them into tools that work in practice. I am also interested in how neural networks learn internal representations, what those representations encode, and how understanding them can make AI systems more interpretable and reliable.
Computational methods
JAX and Python implementations, differentiable models, accelerator-aware numerical work, and reproducible analysis pipelines.
Research software
Open-source packages with documented interfaces, validation, releases, and interactive explanations of the underlying methods.
Product & technology leadership
Data integration, workflow automation, applied AI, and engineering systems designed around real users and operational constraints.
Most of the time I’m a pretty chill dude. Outside work, I’m usually hiking, climbing, skiing, surfing, skydiving, or finding another questionable way to spend time in the mountains. I also make jewellery, which I share at @jeg_jewellery.