My research develops advanced AI methodologies to quantify brain structure, function, and health from large-scale neuroimaging and multi-modal biomedical data. I work at the intersection of artificial intelligence, neuroscience, and computational biology, designing scalable models that integrate structural MRI (3T/7T), functional imaging, biomarkers, and clinical variables to move beyond binary diagnosis toward continuous, individualised health trajectories. My work spans:
- Robust multi-site MRI segmentation and large-scale morphometric modelling;
- Rapid cortical thickness estimation using deep and graph neural networks;
- Foundation models and normative frameworks for precision neuroimaging;
- Generative AI for synthetic medical imaging and fairness-aware training;
- Cognitive resilience modelling integrating imaging, biomarkers, and genetics;
- Longitudinal modelling of disease progression and treatment response.
In short, I build AI systems that transform brain imaging into quantitative, scalable measures of individual health, risk, and resilience.
A central theme of my lab is building reproducible, deployable AI infrastructure, from open-source tools to web-based analysis platforms, ensuring that methodological innovation translates into real-world clinical impact. I lead the Brain Imaging & Artificial Intelligence Research Lab at the University of Glasgow and collaborate across Europe with clinicians, neuroscientists, computational biologists, and industry partners. For more insights into my research, please visit the research page.
Currently a Lecturer in Artificial Intelligence (Assistant Professor) @Glasgow University (UK) and Senior Machine Learning Scientist @Yonder (Italy). I’m pleased to consider applications from prospective PhD students (see blog for the latest news).
I welcome collaborations across academia, healthcare, and industry.