Data Analytics Research and Technology in Healthcare
(DARTH group)
Faculty
Athanasios Tsanas ('Thanasis') FAcadMathSci FRSE FRSM
Chair (Full Professor) in Digital Health and Data Science
Thanasis studied Biomedical Engineering, and completed a DPhil (PhD) in Applied Mathematics at the University of Oxford (2012). He joined the Usher Institute, University of Edinburgh in 2017 as a Chancellor’s Fellow, a prestigious 5-year tenure-track post. He secured tenure 2+ years early, and was promoted to a Personal Chair (Full Professor) in Digital Health and Data Science in 2022.
He has been consistently ranked amongst the world’s top scientists since 2020 according to the Stanford/Elsevier annual ranking list, particularly in the areas of Biomedical Engineering and AI. ScholarGPS named him “Top Scholar" for 2024, and currently rank him globally in the top 0.1% for lifetime impact in Machine Learning. His portfolio includes >£50m in grant income, with outputs of his work used in the NHS and explored by industrial partners including Intel, LSVT Global, GSK, Abbott, Roche, and others. He is Co-founder of the NHS Digital Academy, the world’s first national digital health informatics leadership programme (funded by NHS England), where he led the development and delivery of the 'Clinical Decision Support and Actionable Data Analytics' theme (2018-2022). Internationally, he served as a Scientific Advisor to the Ministry of Health, Greece.
He is a Founding Fellow of the Academy for the Mathematical Sciences (UK National Academy of Mathematics), a Fellow of the Royal Society of Edinburgh (Scotland's National Academy of Sciences), and a Fellow of the Royal Society of Medicine.
Post-doctoral researchers
PhD students
Lauren Thomas
PhD student
Lauren studied Computer Science (Artificial Intelligence) at the University of Sheffield as an integrated Masters degree. During her final year she worked as a Research Assistant in the machine learning group on a project using machine learning to track Bumblebees. Her Masters research project was on improving accuracy of Prostate cancer diagnosis algorithms with continuous and federated learning.
Her PhD focuses on generating clinically actionable insights into endometriosis symptom trajectories on longitudinal self-reports, biological samples and information from digital technologies. She is supervised by Thanasis Tsanas, Andrew Horne and Philippa Saunders and is funded through the Precision Medicine DTP.
Dónal Heelan
PhD student
Dónal studied Computer Science, Linguistics and French at Trinity College Dublin, then went on to work as a Software Engineer at Unity Technologies. He subsequently completed a MSc in AI for Medicine and Medical Research at University College Dublin, where he interned at UCD’s GOLD Lab. His MSc thesis focused on evaluating machine learning algorithms' ability at identifying drivers of non-small cell lung cancer.
His PhD topic centres on “Clinically actionable insights into endometriosis symptom trajectories using longitudinal self-reports, biological samples and data from digital technologies”. The supervisory team is Thanasis Tsanas, Andrew Horne and Philippa Saunders and his funding is through the UKRI AI CDT in Biomedical Innovation.