A multi-disciplinary consortium spanning neuroimaging, machine learning, federated analytics, data governance, and public engagement.
Data scientist with expertise in federated analytics and responsible AI. Secured £16.5M in funding from UKRI/NIHR/Innovate-UK/DARE-UK. Produced open-source, FAIR-aligned software for ontology mapping, provenance tracking, and explainable machine learning. Associate Director of HDR-UK Midlands and Training Lead for the HDR-UK Federated Learning programme.
International leader in computational neuroimaging and biophysical modelling. Developer of the FSL software suite, used by over 1,000 labs worldwide. His diffusion-MRI methodologies are embedded in "Connectome-capable" clinical scanners. Serves on multiple international advisory boards and consults for Johnson&Johnson and GE Healthcare.
Clinical translation expert in neurodegenerative and cerebrovascular imaging. Director of the Precision Imaging Beacon and Theme Lead at the NIHR Nottingham Biomedical Research Centre. Her integration of quantitative MRI, pathology, and AI has produced validated biomarkers for dementia and stroke.
Leading public-engagement specialist in data-intensive health research. Manages the SAIL Consumer Panel. Her hybrid workshops and multimedia consultations have been adopted as best practice across DARE-UK and HDR-UK. Champions ethical and public-engagement training, mentoring researchers in communicating data-intensive science to lay audiences.
Advances statistics and deep learning for medical imaging, with award-winning algorithms for MRI reconstruction and motion correction (MICCAI 2022). His publicly released diagnostic tools enable early, non-invasive disease detection. Leads initiatives on research integrity and scientific communication for early-career researchers.
Academic neuroradiologist and Chair of the Academic Committee of the British Society of Neuroradiologists. Leads large-scale imaging trials TICH-2/TICH-3/RECAST-3 and the first AI-based segmentation implementation for outcome measures in a multicentre intracerebral haemorrhage RCT. Co-Director of clinical academic training.
Chairs the UK Synthetic Data Community Group and the Statistical Disclosure Control Group, leading national policy on safe synthetic data generation and evaluation. Co-developed the SynthOpt open-source toolkit. Advances governance and safe-analytics frameworks, ensuring community tools meet technical and ethical benchmarks.
Expert in AI risk assessment and statistical disclosure control. Developed semi-automated disclosure-risk toolkits for SACRO, SACRO-ML, and TREvolution. Maintains several public GitHub repositories adopted internationally. Guides BRAID on privacy-risk evaluation and new egress requirements for the TREvolution stack.
Head of Programme Delivery for DPUK and UKSeRP, managing a mature infrastructure supporting over 1,200 users. Co-developed TRE-FX and Teleport — core components of TREvolution. Ensures technical innovation aligns with governance, disclosure control, and interoperability across federated environments.
Research software engineer specialising in secure analytics systems. Experienced in developing FAIR software for HDR-UK Federated Analytics, NHS Secure Data Environment, and TREvolution. Leads software engineering teams embedding version control, documentation, and sustainability across NUH and UoN.
Member of the Computational Neuroimaging Lab, focused on developing methods and tools for the analysis of brain imaging datasets. Currently developing multi-modal brain imaging pre-processing pipelines. His imaging-genetics research investigates associations between idiopathic pulmonary fibrosis (IPF) and anatomical, structural, and functional brain imaging-derived features in large cohorts including UK Biobank. Research interests span multi-modal data fusion, multivariate data analysis, and neuroimaging.
Research Software Engineer at the University of Nottingham with expertise in image analysis, scientific computing, and research software development. He develops computational tools and scalable analysis pipelines for large imaging datasets, supporting advanced image processing, machine learning, and quantitative imaging research. His work enables researchers to perform reproducible and efficient analysis of complex imaging data across multidisciplinary research programmes.