Collaborations
How I work with academic and industry partners.
The most useful collaborations connect a clinically important question with the methods, data, and expertise needed to answer it well. I work across disciplinary boundaries to address biological and clinical questions grounded in real-world problems, and to help translate research methods into tools and devices that can be evaluated and implemented in clinical care.
Collaborative themes
Clinical imaging and cohorts
I collaborate with clinicians and cohort researchers to develop imaging phenotypes and study ophthalmic images collected in prospective studies or routine care.
Genetics and multi-omic discovery
I work with academics in statistical genetics, metabolomics, causal inference, and biological interpretation to investigate the mechanisms underlying eye disease and imaging-derived traits.
AI methods and validation
I am interested in collaborations around representation learning, multimodal modelling, external validation, and prospective evaluation, with clinical relevance shaping the technical question from the outset.
Clinical translation and industry
I welcome partnerships focused on clinically meaningful use cases, robust evaluation, and potential integration into care pathways. My role is to help frame and evaluate research, rather than to act as a route to data access.
Supervision and academic community
I am happy to discuss research supervision, collaborative grants, speaking, and opportunities to connect researchers across ophthalmology, AI, genetics, and population health.
Current key collaborators
Alejandro Frangi
University of Manchester
We work on computational ophthalmic imaging and clinically grounded AI, including image-derived phenotypes, cardiovascular applications of ophthalmic imaging, and the integration of imaging with multi-omic data.
Panagiotis Sergouniotis
University of Manchester
Our work brings together retinal genetics, image phenotyping, and causal inference to investigate foveal morphology, retinal pigment epithelium features, glaucoma, and age-related macular degeneration.
Ewan Birney
EMBL-EBI
We collaborate on genomic and multi-omic interpretation of image-derived traits, including work investigating the genetic and biological basis of retinal pigment epithelium features learned by deep-learning models.
Pearse Keane
UCL Institute of Ophthalmology and Moorfields Eye Hospital
Our collaboration focuses on oculomics and the clinical interpretation of AI-derived retinal phenotypes, including their relationships with cardiovascular and neurological health.
Johnathan Cooper-Knock
University of Sheffield and SITraN
We apply causal inference and multi-omic methods across neurodegenerative, ophthalmic, and systemic disease, with collaborative work spanning motor neurone disease, glaucoma, macular degeneration, and retinal phenotypes.
Bernard Keavney
University of Manchester
We work together on the cardiovascular interpretation of ophthalmic image phenotypes, bringing clinical and genetic perspectives to multi-omic studies of retinal structure and disease risk.
Start a conversation
If your work overlaps with these areas and you are interested in exploring working together, I would be very happy to hear from you. Please feel free to contact me by email.