Imperial College London


Faculty of MedicineSchool of Public Health

Honorary Lecturer



+44 (0)20 7594 0716vasa.curcin Website




320Reynolds BuildingCharing Cross Campus





Vasa Curcin is a Lecturer at Health Informatics at King's College London and a Visiting Lecturer at Department of Primary Care and Public Health, Imperial College London. His main web page is here.

He was the healthcare theme coordinator at the London e-Science Centre, while based at the Department of Computing.  He was the Scientific Project Manager of EU TRANSFoRm project, and Technical Lead of CLAHRC Northwest London initiative.

His main research is in the area of provenance-based reproducibility, with specific applications in medical informatics and Big Data analytics. His PhD was on the application of process algebras and other formal models to verification and optimization of scientific workflows. 

Other research interests include:

  • Big Data analytics using scientific workflows
  • Translational research across the whole biomedical spectrum
  • Process models for clinical guidelines

The software tools produced in Vasa's projects are being used by a wider scientific and medical community. These include the WISH tool for model-based clinical data collection and reporting, and the provenance infrastructure for heterogeneous software systems. Software is free of charge, and licensing details are available on request.



Wang Y, Koffman J, Gao W, et al., 2024, Social media for palliative and end-of-life care research: a systematic review., Bmj Support Palliat Care

Domínguez J, Prociuk D, Marović B, et al., 2024, ROAD2H: development and evaluation of an open-sourceexplainable artificial intelligence approach for managingco-morbidity and clinical guidelines, Learning Health Systems, Vol:8, ISSN:2379-6146

Shi M, Liu L, Wafa H, et al., 2024, Effectiveness and Safety of Non-Vitamin K Oral Anticoagulants versus Warfarin in Patients with Atrial Fibrillation and Previous Stroke: A Systematic Review and Meta-Analysis., Neuroepidemiology, Vol:58, Pages:1-14

Wang W, Otieno JA, Eriksson M, et al., 2023, Developing and externally validating a machine learning risk prediction model for 30-day mortality after stroke using national stroke registers in the UK and Sweden., Bmj Open, Vol:13

Zaman M, Goff L, L'Esperance V, et al., 2023, A concept mapping approach to assess factors influencing the delivery of community-based salon interventions to prevent cardiovascular disease and breast cancer among ethnically diverse women in south London, The British Journal of General Practice : the Journal of the Royal College of General Practitioners, Vol:73

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