I sit at the crossroads of physics and machine learning — using mathematical rigour to design models that actually work, and making them openly available for the community.
Trained as a computational physicist, I apply simulation, inference and probabilistic reasoning to hard real-world problems. Currently active in natural language processing and clinical AI at UMCU.
Technical areas I work in and care about:
I believe research and tooling should be freely available. Pre-trained models, datasets and libraries I build are released publicly on GitHub and Hugging Face.
I write about machine learning, physics and their intersections on Medium to make complex ideas accessible to a wider audience.
A concise view of the path from computational and statistical physics to machine learning for language and healthcare.
Experienced data scientist and machine learner with a PhD in applied mathematics and an MSc in aerospace engineering. I combine numerical modelling and scientific computing with applied AI for healthcare.
Lead developer of Argus for cardiovascular disease prediction, with additional work on ECG foundation models, multi-omics biomarker discovery and time-series clustering.
Training language models from scratch and developing clinical NER, entity classification, embeddings and summarisation workflows for healthcare data.
Applied machine learning and data science to network quality, customer experience, market segmentation, anomaly detection and operational decision-making at Tele2 and VEON.
Research at CWI/FOM DIFFER on finite-volume and finite-difference methods for highly anisotropic diffusion in nuclear-fusion plasma simulations.
Building from first principles: training Dutch medical language models from scratch, alongside NLP pipelines and general machine learning tooling.
Peer-reviewed work spanning clinical NLP, information extraction and computational methods. Full list on Google Scholar.
Accessible articles on machine learning, AI and physics — bridging the gap between cutting-edge research and curious minds.
