My research interests revolve around the problem of federated learning in hospital environments and, more precisely, medical imaging AI. Now, I aim to explore differential privacy, homomorphic encryption, and distributed governance models for federated learning systems. Much of my previous work was focused on the integration of blockchain technologies in different application areas such as cryptocurrencies, IoT devices, fog computing, 5G, and distributed oracles. I had introduced dynamic and adaptive device-centric application execution models in edge-cloud-enabled IoT systems. I had also explored several problems related to distributed computing, big data reduction, reputation systems, federated learning, big/mobile/IoT data analytics, and IoT security.
Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study
Nguyen, L.H., Drew, D.A., Graham, M.S., Joshi, A.D., Guo, C.G., Ma, W., Mehta, R.S., Warner, E.T., Sikavi, D.R., Lo, C.H. and Kwon, S., 2020. The Lancet Public Health.
Tudosiu, P.D., Varsavsky, T., Shaw, R., Graham, M., Nachev, P., Ourselin, S., Sudre, C.H. and Cardoso, M.J., 2020. arXiv preprint arXiv:2002.05692.
Chen, X., Diaz-Pinto, A., Ravikumar, N. and Frangi, A., 2020. Deep learning in medical image registration. Progress in Biomedical Engineering.
Wood, D.A., Lynch, J., Kafiabadi, S., Guilhem, E., Busaidi, A.A., Montvila, A., Varsavsky, T., Siddiqui, J., Gadapa, N., Townend, M. and Kiik, M., 2020. arXiv preprint arXiv:2002.06588.
A k-Space Model of Movement Artefacts: Application to Segmentation Augmentation and Artefact Removal
Shaw, R., Sudre, C.H., Varsavsky, T., Ourselin, S. and Cardoso, M.J., 2020. IEEE Transactions on Medical Imaging.