PeopleofSuperintelligence

Researcher

Marc Modat

Marc Modat is a medical-imaging researcher whose work brings image registration and deep learning into clinical research. He coauthored GPU-based registration studies, led the UCL team behind NiftyReg, and contributed to MONAI, an open-source framework for deep learning in healthcare. At UCL, he earned a PhD in 2012 and coauthored a symmetric registration method; in 2026, he coauthored radiomics research on glioblastoma. As of October 2026, he is a Reader at King's College London.

Timeline

  1. 2008

    GPU registration in 2008

    In 2008, he coauthors a conference paper on commodity graphics cards for image registration, biomechanical modelling and cardiac imaging.

  2. 2010

    GPU free-form deformation

    His 2010 paper sets out a parallel-friendly free-form deformation method for graphics processing units. It registers T1-weighted MR images in under a minute with the same segmentation-propagation accuracy as a classical serial implementation.

  3. 2012

    A PhD and NiftyReg

    At UCL, he completes a PhD at the Centre for Medical Image Computing in 2012 under professors Sebastien Ourselin and Nick Fox. The NiftyReg team he leads works to integrate its GPU-based registration package into ITK, and he co-organizes the Medical Image Open Source Workshop at ISBI in 2012.

  4. 2014

    Symmetric block matching

    In 2014, he coauthors a global image-registration method that pairs symmetric block matching with least-trimmed square regression. At UCL's Centre for Medical Image Computing and Dementia Research Centre, he is a senior research associate who translates methods toward clinical use.

  5. 2017

    Lecturer in Translational Imaging

    In 2017, he is a Lecturer in UCL's Translational Imaging Group within the Centre for Medical Image Computing.

  6. 2022

    Contributing to MONAI

    In 2022, he coauthors MONAI, a community-supported PyTorch framework for deep learning in healthcare that supplies tools and model architectures for medical imaging.

  7. May 2026

    Glioblastoma radiomics

    In May 2026, he coauthors the PRECISE-GBM study, which uses MRI-based radiomic features and machine-learning models to predict macrophage immune signatures in IDH-wildtype glioblastoma.

  8. As of October 2026

    Reader at King's College London

    As of October 2026, he is a Reader in the School of Biomedical Engineering & Imaging Sciences at King's College London. He is the main developer of NiftyReg, an open-source medical image-registration package used in large-scale clinical research and clinical trials.

Career

  1. NowKing's College LondonReader
  2. From 2017Centre for Medical Image ComputingLecturer
  3. From 2014University College LondonSenior Research Associate

Education

  1. 2012University College LondonPhD

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes King's College London. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes King's College London. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.
  3. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes King's College London. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  4. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes King's College London. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Commodity Graphics Cards for Image Registration, Biomechanical Modelling and Cardiac Imaging - Aarhus University pure.au.dk. Checked 21 October 2026.
  2. Fast free-form deformation using graphics processing units | Sciweavers sciweavers.org. Checked 21 October 2026.
  3. Global image registration using a symmetric block-matching approach - PMC pmc.ncbi.nlm.nih.gov. Checked 21 October 2026.
  4. ITK meets NiftyReg and Osirix in Barcelona kitware.com. Checked 21 October 2026.
  5. Organisers and Speakers 2017 – MedICSS medicss.cs.ucl.ac.uk. Checked 21 October 2026.
  6. MONAI: An open-source framework for deep learning in healthcare arxiv.org. Checked 21 October 2026.
  7. Predictive radiomics for evaluation of cancer immune signature in glioblastoma: The PRECISE-GBM study - King's College London kclpure.kcl.ac.uk. Checked 21 October 2026.
  8. Marc Modat | King's College London kcl.ac.uk. Checked 21 October 2026.