Justin Engelmann Biomedical AI at the University of Edinburgh

I’m a candidate at the Centre for Doctoral Training in Biomedical AI at the University of Edinburgh. I’m currently completing the MSc by Research stage of the programme where I’m writing my thesis on diagnosing disease from ultra-widefield Scanning Laster Ophthalmoscopy retina images using deep learning.


My primary interest is using Machine Learning (ML) in healthcare to improve patient outcomes through ML-assisted explainable diagnosis and by using causal ML to recommend optimal treatments on a patient-level. I’m also interested in using generative modelling to create synthetic yet realistic versions of real-world datasets for privacy-preserving data sharing and for resampling to address bias or class imbalance. Beyond that, I’m always interested in working on problems that arise in clinical practice or biomedical research that could be tackled with ML or other quantitative methods.


Previously, I completed an MSc in Business Administration at Humboldt University Berlin where I focussed on Machine Learning, Microeconomics and Operations Research, and wrote my thesis on addressing class imbalance with generative modelling in a tabular data context [arxiv / Expert Systems with Applications (forthcoming)]. During my MSc, I worked as a student research assistant for computational statistics and interned at both BCG and its data science branch BCG GAMMA. For my undergraduate degree, I studied Philosophy, Politics and Economics at the University of Oxford.