Andrew holds a Bachelor’s degree (BAI, BA) in Biomedical Engineering (2015) and Master’s degree (MAI) in Neural Engineering (2016) from the University of Dublin, Trinity College. During his time at Trinity, Andrew’s research 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).
After leaving Trinity, Andrew joined the Institute of Biomedical Engineering (IBME) at the University of Oxford to pursue a DPhil in 2016. His research has focused on the development of digital biomarkers for neurodegenerative diseases using multimodal data from consumer wearable devices, such as smartphones and smartwatches. By leveraging advanced signal processing and machine learning techniques, his work hopes to further understand and interpret the underlying pattern and function of these diseases outside of the clinic.
- Digital biomarkers
- Multimodal data
- Advanced signal processing
- Machine learning techniques
Andrew works on the Digital Biomarkers project, which investigate models to analyse multivariate time-series data and summarise these to form digital biomarkers capable of measuring clinically meaningful differences in disease states/progression and quality of life in one or more clinical condition; specifically, digital biomarkers that:
a. Can serve as outcome measures in randomized clinical trials, possessing greater sensitivity than current clinical measures and scales or that can be related to clinical outcomes
b. Can supplement existing biomarkers in medical domains that still rely heavily on subjective and observational assessments
c. Can be associated with disease progression that may be differentially regulated by genetic risk factors
d. Are meaningful to patients, clinicians and other stakeholders.
Digital Biomarkers Project
Analysing data to form digital biomarkers capable of measuring clinically meaningful differences in disease states/progression and quality of life in one of more clinical condition.