Work that bridges deep tissue biology, modern clinical-trial methodology, and pragmatic clinical AI — anchored in IgA nephropathy, extending across glomerular disease, and oriented towards kinder, more precise care for the patients who come next.
Phenotyping glomerular disease by defining omics-based signatures predictive of disease trajectory and therapeutic response. The programme is designed to feed into companion-diagnostic and patient-stratification strategies for IgAN therapies and trials.
ERA Clinical Trials Fellow involved with the delivery of several clinical trials related to glomerular diseases.
NHS Digital-funded ML models for AKI prognosis; KRUK-funded LLM-powered patient assistant for IgAN. The work bridges clinical pragmatism with modern machine-learning practice and rigorous evaluation in real-world patient cohorts.
The clinical anchor is glomerular disease — and especially IgA nephropathy — but the methods reach across nephrology and beyond.
The IgAN programme moves between bench and bedside: characterising disease at sub-cellular resolution with DSP, identifying druggable pathways (including endothelial and complement targets), and translating findings into stratified trial designs and pragmatic patient-facing tools.
A second methodological thread is clinical AI — most prominently the NHS Digital-funded recurrent neural network model that predicts the need for renal-replacement therapy in inpatients with AKI in real time, and a KRUK-funded LLM assistant designed to canvas concerns from IgAN patients.
Together these strands form a portfolio that is unusually well-suited to industry partnership: a clinically deep view of a single disease, a methodological toolkit fit for modern trial design, and an AI-aware approach to digital health.
Particularly interested in stratified-trial design, biomarker strategy, spatial-omics integration, and clinically deployed ML.