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Miguel O. Bernabeu completed a D.Phil. in Computational Biology at the University of Oxford in 2011, focusing on the development of computational methods for simulating ventricular cardiac electrophysiology. This work laid the foundation for multiple subsequent Ph.D. projects and was crucial to the success of the EU-FP7 grant VPH-preDiCT, which involved collaboration with pharmaceutical companies to integrate mathematical modelling into drug cardiotoxicity research. His research was presented at the Heart Rhythm 2011 conference, a significant event in cardiac science. In 2011, Bernabeu joined the Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX) at University College London, where he investigated the relationship between haemodynamics and vascular remodelling using both computational and experimental methods. His contributions were featured in the New Scientist magazine and published in various journals and conferences. During this period, he recognised the necessity of combining experimental and computational techniques to tackle critical questions regarding blood vessel responses to flow conditions. In 2015, Bernabeu was appointed to The University of Edinburgh as a Chancellor’s Fellow, establishing his first research group at the Centre for Medical Informatics, Usher Institute. He was promoted to Senior Lecturer in 2019. His research group has received funding from several prestigious organisations, including Fondation Leducq, the European Commission, EPSRC, MRC, the British Heart Foundation, The Alan Turing Institute, and Diabetes UK. In May 2021, Bernabeu became the Deputy Director of The Bayes Centre, the university's innovation hub for Data Science and Artificial Intelligence, where he oversees research strategy and delivery as Director of Research.
Miguel O. Bernabeu's research focuses on vascular structure and function, particularly the relationship between abnormal vascularisation and various diseases such as myocardial infarction, stroke, and tumourigenesis. Their work aims to advance the understanding of vascular biology and biotransport, translating findings into next-generation vascular normalisation therapies. Specific research interests include the development of automated methods for diagnosing eye and systemic diseases through retinal scans, studying the tumour microvascular environment's impact on treatment, and investigating vascular remodelling during angiogenesis. The approach combines theoretical mathematical modelling and machine learning, in collaboration with vascular and cancer biologists and clinicians.
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