Andrew P. Creagh
Postdoctoral Research Associate @ University of Oxford | GSK Postdoctoral Fellow
D.Phil. in Clinical Machine Learning from the University of Oxford.

Institute of Biomedical Engineering (IBME),
Old Road Campus Research Building (ORCRB),
University of Oxford,
OX37DQ, UK.
Welcome
I am a Postdoctoral Researcher at the Computational Health Informatics (CHI) laboratory at the University of Oxford led by Prof. David A. Clifton.
I am concurrently a GSK Postdoctoral Fellow in Digital Biomarkers, a Junior Research Fellow at St Cross College, University of Oxford, and a Postdoctoral Researcher at the Wearables Laboratory led by Prof. Aiden Doherty at the Big Data Institute (BDI), University of Oxford.
Links:
My Departmental Website
My College Website
Research
My research aims to explore how we can capture digital biomarkers of disease, through continuously collecting smartphone and smartwatch measurements when patients are at-home. I work mainly with patients who have neurdegenerative and autoimmune diseases, such as multiple sclerosis, rheumatoid arthritis, Parkinson’s disease and dementia.
Clinical applications of machine learning (ML) can act as powerful tools to learn complex and unseen digital patterns of disease, to help remotely monitor and identify signs of degeneration before they occur, and to understand new facets of habitual disease and disease phenotypes. I have a specific interest in creating interpretable, robust, and transparent models through explainable AI (XAI) frameworks.
Background
I obtained my DPhil. (PhD) in Clinical Machine Learning at the University of Oxford, developing digital biomarkers in collaboration with industrial partners, F. Hoffmann-La Roche.
Prior to my DPhil. I hold a bachelor’s degree (BAI, BA) in Biomedical Engineering and master’s degree (MAI) in Neural Engineering from Trinity College, the University of Dublin. My research at Trinity investigated the use of machine learning techniques to predict the onset of dementia in later life, through the characterisation of gait and cognitive performance from routine clinical assessments conducted during the Irish Longitudinal Study on Aging (TILDA).
news
Jun 18, 2022 | Our new preprint: “Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data” is now available on arXiv 📢. |
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Feb 24, 2022 | Our new preprint: “Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation using Smartphones” is now available on medrXiv 📢. |
Nov 1, 2021 | Announced as this year's recipient of the prestigious IET William James Award 🥳. |
Jul 15, 2021 | Our new explainable AI (XAI) paper is now available online in Nature Scientific Reports 🥳. |
Jun 16, 2021 | Awarded Junior Research Fellowship (JRF) St Cross College, University of Oxford. |
Apr 12, 2021 | Awarded STEM for Britain Medal at the UK Houses of Parliament |