Institute of Biomedical Engineering (IBME),
Old Road Campus Research Building (ORCRB),
University of Oxford,
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.
My research aims to explore how we can capture digital biomarkers of disease using counsumer digital health technologies (DHT)—by 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 AI/ML (machine learning) 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.
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).
|Aug 23, 2022||We have now publicly released our code for our paper: “Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling” from ICML on GitHub .|
|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 📢.|
|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|
|Dec 21, 2020||Our Draw a Shape paper was the most cited paper in Physiological Measurements in 2020!|
medrXivLongitudinal Trend Monitoring of Multiple Sclerosis Ambulation using SmartphonesmedrXiv 2022
Sci. ReportsInterpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones2021
IEEE J-BHISmartphone- and Smartwatch-Based Remote Characterisation of Ambulation in Multiple Sclerosis During the Two-Minute Walk Test2021
Physiol. Meas.Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the Draw a Shape Test2020
arXivSelf-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable DataarXiv preprint 2022