Summary
Fani Deligianni is an Associate Professor and senior lecturer at the University of Glasgow with a PhD in Medical Image Computing from Imperial College London and over a decade of research experience bridging machine learning, neuroscience and medical imaging. Her work focuses on statistically rigorous mappings between brain structure and function, exploiting sparsity, multivariate inference and non-parametric methods to reduce false positives in connectivity models. She is proficient in C++, Python, R and MATLAB, and has translated research into usable software packages and project-managed EU/UK innovation efforts and commercial medical imaging development. Her research blends theory and practice—using MCMC, randomized Lasso and manifold transport for covariance analysis—rather than purely black-box ML, enabling interpretable neuroimaging models. Based in Glasgow, she combines academic leadership with hands-on algorithm and software development, including contributions to tools like TractoR and research code distributions. An underappreciated strength is her cross-disciplinary training (advanced computing, neuroscience and engineering), which lets her navigate and integrate diverse methodological perspectives.
8 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Medical Image Computing, NA, Doctor of Philosophy (Ph.D.), Medical Image Computing, NA at Imperial College London
Engineer’s Degree, Electrical and Computer Engineering, 7.2/10, Engineer’s Degree, Electrical and Computer Engineering, 7.2/10 at Aristotle University of Thessaloniki (AUTH)
University College London