Professor Fernando Bello is a computer scientist and engineer working at the intersection of medicine, education and technology. He obtained his PhD in Biomedical Systems from Imperial College in 1996 and worked as a postdoctoral Research Fellow on brain image analysis and Computer Aided Surgery from 1996-1998 at Guy's Hospital, and at the University of Kent, where in 1998 he became a Lecturer in Electronic Engineering.
He re-joined Imperial in September 2000 and is currently Professor of Surgical Computing and Simulation Science within the Department of Surgery and Cancer, where he is Director of the Centre for Engagement and Simulation Science, leading the SiMMS - Simulation and Modelling in Medicine and Surgery research group. A multi-disciplinary research group aiming at building suitable models and simulations of clinical processes, including clinical examination, clinical diagnosis, interventional procedures and care pathways.
Prof Bello is particularly interested in the use of virtual / mixed reality environments and haptics in the context of education and training, pioneering advanced patient specific simulation of a number of surgical procedures and clinical examinations, online and mobile simulation tools, as well as innovative approaches to contextualised simulation. He has published widely in technological, medical and educational journals, is involved in several simulation-based training programmes in the UK and abroad and is Academic Co-director of Imperial’s MSc in Surgical Innovation.
et al., 2019, 'How to help your unwell child': A sequential simulation project, Bmj Simulation and Technology Enhanced Learning, ISSN:2056-6697
Cox W, Cavenagh P, Bello F, 2019, Is the diagnostic radiological image an underutilised resource? Exploring the literature, Insights Into Imaging, Vol:10, ISSN:1869-4101
et al., 2019, A novel haptic interface for the simulation of endovascular interventions, International AsiaHaptics conference, Springer, Pages:178-182, ISSN:1876-1100
et al., 2019, Robust adaptive synchronisation of a single-master multi-slave teleoperation system over delayed communication, Pages:193-198
et al., 2019, A new tensioning method using deep reinforcement learning for surgical pattern cutting, Pages:1339-1344