Rate My Professor Thanasis Tsanas

Athanasios (Thanasis) Tsanas holds the Personal Chair in Digital Health and Data Science at the Usher Institute within Edinburgh Medical School, University of Edinburgh, where he also serves as Director of Knowledge Exchange and Research Impact. He earned a BSc in Biomedical Engineering from the Technological Educational Institute of Athens (2005), a BEng in Electrical Engineering and Electronics from the University of Liverpool (2007), an MSc in Signal Processing and Communications from Newcastle University (2008), and a DPhil in Applied Mathematics from the University of Oxford (2012). Following his doctorate, Tsanas served as a Research Fellow in Biomedical Engineering and Applied Mathematics at Oxford (2012-2016), Stipendiary Lecturer in Engineering Science at St Peter’s College, Oxford (2014-2016), and Lecturer in Statistical Research Methods at Oxford’s Said Business School (2016-2019). He joined the University of Edinburgh in January 2017 as a Chancellor’s Fellow, securing early tenure in December 2019, advancing to Associate Professor in 2020, and to Full Professor in 2022. Tsanas founded and leads the Data Analytics Research and Technology in Healthcare (DARTH) group, co-directs the Telescot telemedicine programme, and co-founded the NHS Digital Academy, leading its Clinical Decision Support and Actionable Data Analytics theme from 2018 to 2022. He is an affiliate of the School of Mathematics and has held roles such as Turing Fellow (2021-2024), Bayes Innovation Fellow (2023-2024), and scientific advisor to the Greek Ministry of Health (2020-2023).

Tsanas specializes in time-series analysis, signal processing, pattern recognition, and statistical machine learning, with applications in healthcare including Parkinson’s disease speech analysis, wearable sensor data for activity and sleep monitoring, and telemedicine. His tools have been adopted by the NHS and partners like Intel, Roche, Abbott, GSK, LSVT Global, Aculab, and Mirador Analytics. Key publications include “Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests” (IEEE Transactions on Biomedical Engineering, 2010), “Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools” (Energy and Buildings, 2012), and “Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease” (IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014). Recent works cover preterm birth outcomes, endometriosis symptom tracking, and AI priorities in global health. Awards include the EPSRC Doctoral Prize (2012), Young Scientist Award at MAVEBA (2013), BIOSTEC/Biosignals Best Paper (2021), Fellow of the Royal Society of Edinburgh (FRSE, 2025), Founding Fellow of the Academy for the Mathematical Sciences (FAcadMathSci, 2026), and Fellow of the Royal Society of Medicine. He serves on editorial boards for Frontiers in Neurology, JMIR mHealth and uHealth, and JMIR Mental Health.