Maria-Cruz Villa-Uriol is a Lecturer in Computer Science and member of the Organisations, Information and Knowledge Group (Oak) at the University of Sheffield, UK. She is also member of the INSIGNEO institute for in silico medicine. She holds a degree in Telecommunications and Electronics Engineering (Barcelona, Spain), and a PhD in Computer Engineering (University of California, Irvine, USA).
Previously, she held a postdoctoral position (February 2006 - August 2012) at the Universitat Pompeu Fabra (Barcelona, Spain) and a Research Fellow position at the University of Sheffield (Department of Mechanical Engineering, September 2012 - January 2015). Since 2006 she participated in several large European and Spanish projects. In the FP6 European project @neurIST (Integrated Biomedical Informatics for the Management of Cerebral Aneurysms), she joined the Virtual Physiological Human research community. Since then, her involvement and collaboration in this field continued in other European projects (the VPH-NoE Network of Excellence, the DISCIPULUS Coordinated Support Action, and the VPH-Share and euHeart Integrated Projects). Her recent work in VPH-Share was centred on sharing image-based computational workflows with the VPH community, on studying and developing strategies to tackle the presence of missing of data in clinical databases, and on using machine learning techniques to unravel correlations between image-based personalised biomechanic descriptors, elements in the patient’s clinical record and clinically relevant events for cerebral aneurysm patients.
Her main research interests are the personalisation of models using computational imaging and modelling techniques, the composition of scientific workflows, and the use and development of data-driven decision-making strategies to support clinical decisions using heterogeneous data sources such as personalised VPH models, clinical databases, and mobile sensors capturing a wide variety of variables describing an individual and his/her environment. Her primary area of interest is in the cardiovascular domain with an emphasis in clinical translation.