Simon Dahan



Enrichment Student

Cohort year


Partner Institution


Simon is a PhD student in Deep Learning for Neuroimaging at King’s College London. He is part of the CDT in Smart Medical Imaging under the supervision of Dr Emma Robinson and Daniel Rueckert.

After graduating in computer science engineering at Telecom Paris, he completed an MSc. in Artificial Intelligence & Machine Learning at Imperial College London. Throughout his MSc, he developed a keen interest for deep learning in medical imaging and the promise it holds to improve clinical outcomes. Following this, a research internship focusing on video understanding sparked a passion for spatio-temporal deep learning – incorporating both spatial and temporal information into predictive models. Together, these experiences motivated Simon to investigate how the application of spatio-temporal deep learning to medical imaging data can enhance the modelling of complex medical conditions.

Today, he draws upon his multidisciplinary background to support research at the intersection of medical imaging, neuroscience, and spatio-temporal deep learning.

Research interests

Modelling the cerebral cortex (outermost tissue of the brain) as a surface is critical for capturing the structural and functional heterogeneity of cortical organisation and function. Simon’s PhD research focuses on developing deep learning methods that can extract biologically meaningful spatio-temporal information from magnetic resonance images of the cerebral cortex. His research involves translating geometric deep learning techniques and developing new surface deep learning methodologies, with the ultimate goal of characterising subtle features of the spatio-temporal dynamics of brain activity for vulnerable preterm babies. He welcomes further experience on how to develop and interpret spatio-temporal deep learning models for studying non-Euclidian surfaces and is interested in collaborating toward achieving these goals.