Data Analytics Research and Technology in Healthcare
(DARTH group)
Faculty
Athanasios Tsanas ('Thanasis')
Chair (Full Professor) in Digital Health and Data Science
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Thanasis studied Engineering for his undergraduate and MSc degrees and completed a PhD in Applied Mathematics at the University of Oxford (2012). He continued working at Oxford as a Research Fellow in Biomedical Engineering and Applied Mathematics (2012-2016), Stipendiary Lecturer in Engineering Science (2014-2016), and Lecturer in Statistical Research Methods (2016-2019). He currently holds the Chair (Full Professor, tenured) in Digital Health and Data Science at the Usher Institute, Edinburgh Medical School, University of Edinburgh.
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He received the Andrew Goudie award (top PhD student across all disciplines, St. Cross College, University of Oxford, 2011), the EPSRC Doctoral Prize award (2012) as one of only 8 Oxford PhD students across 11 departments, the young scientist award (MAVEBA, 2013), the EPSRC Statistics and Machine Learning award (2015), and the BIOSTEC/Biosignals Best paper award (2021). He is Co-founder of the NHS Digital Academy, the first national digital health informatics leadership programme, where he led the development and delivery of the 'Clinical Decision Support and Actionable Data Analytics' theme (2018-2022). He is a Senior Member of IEEE, a Fellow of the Higher Education Academy, and a Fellow of the Royal Society of Medicine.
Post-doctoral researchers
Yue Zhang
Post-doctoral researcher​
Yue obtained her Master's degree in Electrical Engineering from the University of Science and Technology Beijing in 2019. Following that, she completed her PhD in Electronic and Electrical Engineering at the University of Leeds. Her doctoral research project centered on the development of an accurate and reliable Brain-Computer Interface (BCI) system utilizing EEG signals. The main works include recognition method development, classification confidence analysis, transfer learning, and implantation of EEG-controlled robotic systems.
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Yue's post-doctoral work is funded through the AUKCAR renewal award.
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PhD students
Filip Mendusic
PhD student​
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Filip comes from a Computing background (Zagreb, Croatia), and has completed an MSc in Artificial Intelligence at the University of Edinburgh (2018). He has been passionate about computers since his childhood years.
His research focuses on developing novel and applying existing machine learning approaches to healthcare, in an effort to improve patient diagnosis and recovery. He is currently focused on clustering and feature selection techniques to tackle myocardial infarction. Filip is affiliated with both the Usher Institute of Population Health Sciences and Informatics​ and the Centre for Cardiovascular Science and is co-supervised by Prof. Nick Mills.
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Funding: Filip is a British Heart Foundation (BHF) funded research student on the BHF PhD programme in Cardiovascular Science at the University of Edinburgh (full 3-year scholarship covering fees+stipend).
Evi Valavani
PhD student​
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Evi has completed her medical training (MBBS) at the Aristotle University of Thessaloniki, and has an MSc in Data analytics from the same university. In both her degrees she graduated as top student of her class. She has worked as an intern at the Chelsea and Westminster Hospital in London, specializing in paediatric gastroenterology. She has worked as a GP and towards her residency in Paediatrics (ST1 equivalent) at the County General Hospital of Agios Nikolaos, Crete, Greece. She has also worked as an intern at the Children's hospital of Philadephia in the Immunogenetics and Transplantation laboratory in the USA.
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She is working on neonatal monitoring and childhood development by mining questionnaires and additional signal modalities including MRI. Evi is co-supervised by Prof. James Boardman, Dr Donald MacIntyre, and Dr Richard Chin.
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Funding: CF funds from the University of Edinburgh (full 3-year scholarship covering fees+stipend).
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Awards during PhD:
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SPR MD/PhD Student Research Award at the Society for Pediatrics Research conference (one of the biggest international academic meetings in the area of Pediatrics) for her research paper: “Machine Learning for Stratification of children at risk of language delay following Preterm Birth", PAS conference (March 2021)
Katherine Edgley
PhD student​
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Katherine studied Applied Mathematics at Brown University (USA) and completed an MSc in Computational Applied Mathematics at the University of Edinburgh. Her MSc thesis focused on actigraphy data analysis for stroke patients which she completed within the DARTH group. Katherine had previously worked as a counselor at a summer youth camp and had taught English as a foreign language in Germany.
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Her PhD focuses on using advanced data analytics and the use of wearable sensors to provide new insights into endometriosis.
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Katherine is co-supervised by Prof. Andrew Horne and Prof. Philippa Saunders at the Centre for Reproductive Health at the University of Edinburgh.
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Funding: Katherine is on a full 3-year scholarship covering fees+stipend from Standard Life + University of Edinburgh top-up.
Craig Nicolson
PhD student​
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Craig comes from a background of clinical medicine at the University of Edinburgh where he completed his medical degree (MBChB 2018) and a BSc Hons in Physiology (2015). Following his foundation training as a doctor he has completed a Masters in Biomedical Artificial Intelligence (MSC (R) 2021) as part of the Biomedical AI CDT which continues into a 3 year PhD.
His research is focused on the potential for AI and machine learning methods to aid in clinical decision making, particularly using physiological data. He is currently focused on developing predictive models for optimising organ donation in intensive care environments using physiological and clinical data.
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Craig is co-supervised by Dr Naz Lone at Edinburgh, Dr Kathryn Puxty (NHS GGC and University of Glasgow) and Martin Shaw (NHS GGC and University of Glasgow).
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Funding: Craig is funded through the UKRI CDT Biomedical AI with a 4-year studentship, covering tuition fees, stipend and travel/research support.