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
17 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Medical Image Computing, Doctor of Philosophy (Ph.D.) Medical Image Computing at Imperial College London
Engineer’s Degree Electrical and Computer Engineering, Engineer’s Degree Electrical and Computer Engineering at Aristotle University of Thessaloniki (AUTH)
University College London