Arman Eshaghi
National Institute For Health And Care Research (NIHR) Advanced Fellow
London, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolArman Eshaghi is an NIHR Advanced Fellow and computational neuroscientist at UCL combining an MD and a PhD to apply AI and causal inference to understand and halt disability progression in multiple sclerosis. He leads large-scale neuroimaging studies—having worked with cohorts exceeding 100,000 subjects—and develops robust image-processing tools, contributing to prominent open-source projects such as ANTs and nipype. As founder of Queen Square Analytics and an affiliate at the Montreal Neurological Institute, he translates methods into clinical trials and hospital-real-world data to personalise MS treatment. He supervises and teaches machine learning for neuroimaging at UCL and serves on the editorial board of Neurology, reflecting both academic leadership and clinical impact. Notably, his background spans hands-on algorithmic work (e.g., lesion-filling and cortical thickness interfaces) through to strategic AI deployment in healthcare.
12 years of coding experience
2 years of employment as a software developer
Doctor of Medicine - MD Medicine, Doctor of Medicine - MD Medicine at Tehran University of Medical SciencesUniversity College London
Github Skills (20)
algorithm10
algorithms10
c-language10
dataflow-programming10
dataflow10
python10
image-processing10
medical-image-processing10
brain-imaging10
workflow-engine10
neuroimaging10
itk10
cprogramming-language10
file-handling9
command-line-parser8
Programming languages (9)
Github contributions (5)
ANTsX/ANTs
Dec 2014 - Nov 2015
Advanced Normalization Tools (ANTs)
Role in this project:
Back-end Developer & Image Processing Specialist
Back-end Developer & Image Processing SpecialistContributions:1 release, 40 commits, 5 PRs in 10 months
Contributions summary:Arman primarily contributed to the `LesionFilling.cxx` example, focusing on algorithms for filling lesions in medical images. Their work involved implementing connected component analysis, binary thresholding, dilation and subtraction operations using ITK (Insight Toolkit) libraries. They also integrated command-line parsing and made iterative improvements to the lesion filling algorithm, including measures to exclude CSF voxels.
antsnormalizationimage-registrationimage-segmentationmedical-image-processing
nipy/nipype
May 2014 - Oct 2014
Workflows and interfaces for neuroimaging packages
Role in this project:Back-end Developer
Back-end DeveloperContributions:24 commits, 8 comments, 6 issues in 5 months
Contributions summary:Arman primarily contributed to the development of the `antsCorticalThickness` interface within the `nipype` project, which is designed for neuroimaging workflows. Their work involved creating and refining the interface, including specifying input and output parameters. The user also added test files and corrected typos within the codebase. Furthermore, they addressed a bug related to output prefixes.
workflowneuroimagingpythondata-scienceworkflow-engine
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