Machine learning · Causal inference · Healthcare
From clinical data to
reliable models and evidence.
I build machine learning systems for healthcare and develop methods to estimate treatment effects from observational data.
Machine Learning Engineer at Aiomic · Copenhagen
Research depth. Engineering ownership.
At Aiomic, I develop the clinical ML stack, from data requirements and model training to blinded validation and production integration. My work combines transformers, clinical NLP, and classical machine learning.
I completed my PhD at the University of Copenhagen and the Pioneer Centre for AI, working on scalable causal inference with electronic health records in the PHAIR project. My earlier training is in physics and computational physics.
PhD thesis · University of Copenhagen · 2026
Connecting EHR transformers with target trial emulation: from clinical prediction to treatment-effect estimation and large-scale drug-safety screening.
Read the thesis PDF · 11 MBA shared data standard and ecosystem for machine learning on structured medical records.
Systematic improvements to EHR data representation, model architecture, and training, evaluated across 25 clinical prediction tasks.
Interactive teaching tool · Runs in the browser
Guided lessons and a full simulator for seeing how confounding, adjustment, overlap, and model choices affect causal estimates against the known true effect.
A Python library for treatment-effect estimation from propensity scores and outcome predictions, including IPW, AIPW, TMLE, and matching.
A collaborative framework for transformer modelling of electronic health records. I contributed across the codebase and coordinated development of the PHAIR-EHR causal-inference extension.