Alexia holds a BEng and MEng in Environmental Engineering by Democritus University of Thrace. During her undergraduate studies she was part of a research team where she focused on Big Data analysis, statistical modelling, infodemiology and Internet behaviour. Then she moved to Scotland and completed a MSc in Big Data with distinction, at the University of Stirling. For her dissertation, she applied unsupervised learning techniques to predict water quality parameters using remote sensing data.
Whilst in Stirling, she decided to undertake the exciting opportunity to join the Centre for Health Informatics in the University of Manchester. She started her PhD on Health Informatics which was supervised by Prof Peek, Prof Geifman and Dr Couch, and funded by EPSRC. Her research focused on probabilistic methods to integrate structured biomedical data.
During her PhD, she co-authored research papers, was management committee member in different European Networks, participated in training schools, completed Short Term Scientific Missions and presented her research on multiple international and national meetings, symposia and conferences. She was also involved in teaching of many UG and PG courses which led her to obtain a Fellowship by Advance HE.
Alexia’s post doctoral work at the CEU mainly focuses on longer-term effects of Covid-19 using whole population electronic health records within the CVD-COVID-UK consortium.
Alexia’s goal is to pursue a career in researching concepts by aggregating environmental and health data, developing AI technologies, applying machine learning algorithms and learning more about epidemiology, population health data, missing data, and prediction modelling.
1. Peters S, Vienneau D, Sampri A, Turner M, Castaño Vinyals G, Bugge M, Vermeulen R, (2021) Occupational exposure assessment tools in Europe: a comprehensive inventory overview. Annals of Work Exposure and Health.
2. Peters S, Vienneau D, Sampri A, Turner M, Castaño Vinyals G, Bugge M, Vermeulen R, (2021) S-263 OMEGANET Inventory of Occupational Exposure Assessment Tools. Occupational and Environmental Medicine, 78, A156.
3. Moreno-Indias et al, (2021) Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Frontiers in Microbiology, 12, 277.
4. Sampri A, Geifman N, Le Sueur H, Doherty P, Couch P, Bruce I, Peek N, MASTERplans Consortium (2020) Probabilistic Approaches to Overcome Content Heterogeneity in Data Integration: A Study Case in Systematic Lupus Erythematosus. Studies in Health Technology and Informatics, 270, 387-391.
5. Mavragani A, Sampri A, Sypsa K, Tsagarakis KP (2018) Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era. JMIR Public Health and Surveillance, 4(1):e24.
6. Sampri A, Mavragani A, Tsagarakis KP (2016) Evaluating Google Trends as a tool for integrating the ‘Smart Health’ concept in the Smart Cities’ governance in USA. Procedia Engineering, 162, 585-592.
7. Mavragani A, Sypsa K, Sampri A, Tsagarakis KP (2016) Quantifying the UK Online Interest in substances of the EU Watch List for Water Monitoring: Diclofenac, Estradiol, and the Macrolide Antibiotics, Water, 8(11), 542.
8. Mavragani A, Sampri A, Tsagarakis KP (2016) Quantifying the online behavior towards organic micropollutants of the EU watchlist: The cases of Diclofenac and the Macrolide Antibiotics. Procedia Engineering, 162, 576-584.