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.



Snell LB, Wang W, Alcolea-Medina A, et al., 2022, Descriptive comparison of admission characteristics between pandemic waves and multivariable analysis of the association of the Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 with disease severity in inner London., Bmj Open, Vol:12

Carr E, Bendayan R, Bean D, et al., 2021, Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study, Bmc Medicine, Vol:19

Hay AD, Moore M, Taylor J, et al., 2021, Immediate oral versus immediate topical versus delayed oral antibiotics for children with acute otitis media with discharge: the REST three-arm non-inferiority electronic platform-supported RCT Introduction, Health Technology Assessment, Vol:25, ISSN:1366-5278, Pages:1-+

Chapman M, Mumtaz S, Rasmussen LV, et al., 2021, Desiderata for the development of next-generation electronic health record phenotype libraries., Gigascience, Vol:10

Wongkoblap A, Vadillo MA, Curcin V, 2021, Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study, Jmir Mental Health, Vol:8, ISSN:2368-7959

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