Rezvan Farahibozorg

Rezvan Farahibozorg is a Royal Academy of Engineering Research Fellow and Principal Investigator in the FMRIB Analysis Group at the Nuffield Department of Clinical Neuroscience, University of Oxford, within the Medical Sciences Division. Her research develops machine learning tools for non-invasive functional brain imaging to predict personalized traits and diseases.

She earned her PhD at Cambridge University through the Cambridge International Scholarship Scheme, developing magnetoencephalography (MEG) connectivity methods for semantic memory research. Her academic journey bridges computational neuroscience and clinical applications.

Her research integrates machine learning with multimodal neuroimaging (fMRI, MEG, EEG) to model brain connectivity at population and individual levels. Key applications include dementia prediction, personalized trait estimation (age/IQ), and understanding semantic memory networks. She specializes in scalable methods for big datasets like UK Biobank.

Analysis of her publications reveals a strong trajectory in population neuroscience and methodological innovation. Recent work focuses on hierarchical modeling (2021-2022), subject variability (2020), and cross-modal connectivity (2017-2018), consistently advancing tools for large-scale brain network analysis with clinical relevance.

Key recognitions include:

• Royal Academy of Engineering Research Fellowship
• Cambridge International Scholarship

Funded by the Royal Academy of Engineering, she leads collaborative projects within Oxford's FMRIB centre. She actively recruits PhD/MSc students through co-supervised projects and provides application mentoring for underrepresented STEM groups. Her work integrates with FSL software development, including sPROFUMO lectures for the neuroscience community.