Rohini Mathur

Professor Rohini Mathur is the Chair of Health Data Science at Queen Mary University of London (QMUL), within the Wolfson Institute of Population Health. She previously served as an Associate Professor at the London School of Hygiene & Tropical Medicine (LSHTM), where she conducted Wellcome Trust-funded research on ethnic inequalities in type 2 diabetes care and outcomes. Her work emphasizes precision medicine applied to globally diverse datasets to address health disparities, particularly in cardiometabolic diseases and pandemic-related studies. At QMUL, she leads a workstream in the Genes & Health Executive, developing phenotypes for electronic health records linked to the G&H cohort, and collaborates across the Wolfson Institute’s (WIPH) centres on health inequality projects.

Her research expertise includes causal approaches, observational and pharmacoepidemiology, and leveraging large-scale health data. She is actively involved in the OpenSAFELY collaborative, focusing on projects like risk prediction during the pandemic and long-term health impacts of conditions such as diabetes and mental illness. External roles include Chair of the Membership Committee for the International Society of Pharmacoepidemiology, membership in Diabetes UK Research Steering Groups, and the South Asian Health Foundation.

Professor Mathur’s grants include the Barts Charity-funded Precision Medicine Part 2 program and Wellcome Trust support for her students’ projects. She supervises three PhD/Master’s students addressing topics like ethnic variations in diabetes risk, AI-driven multimorbidity analysis in underrepresented groups, and mental health comorbidity in South Asian populations. Her research also spans genetic studies, including polygenic risk scores in multiethnic populations, and the effects of social determinants like household composition on health outcomes.

She collaborates closely with the Clinical Effectiveness Group and contributes to initiatives such as improving hypertension management in socioeconomically deprived areas and analyzing pandemic-induced healthcare disruptions. Her work highlights the importance of equitable data collection and utilization to bridge gaps in healthcare access and outcomes for marginalized communities.