My research focuses on flood hazard modelling and flood risk assessment, with expertise in developing high-performance hydrodynamic models as the core engine for simulating large-scale flood dynamics. Building on this, I develop integrated modelling frameworks that combine hydrodynamic models with hydrological, ocean, and climate models, leveraging diverse Earth observation datasets including SWOT satellite water surface elevations, to simulate compound flooding in data-sparse coastal regions, project how flood hazards evolve under future climate conditions, and support to deliver actionable tools for early warning and climate adaptation.

I am currently a researcher on the REPRESA project (Resilience and Preparedness to Tropical Cyclones across Southern Africa), a CAD 8 million initiative aimed at improving early warnings and building resilience to tropical cyclones in southern Africa. The project is co-led by the Global Change Institute at the University of the Witwatersrand (WITS), Eduardo Mondlane University (UEM), and the University of Bristol (UoB), in partnership with the UK Met Office, ECMWF, the University of Reading, and other organisations. My role centres on assessing tropical cyclone flood hazards, both present-day and future, by integrating surface water, river, and coastal flooding to support impact-based early warning systems for vulnerable communities.

Prior to REPRESA, I was a Research Associate on the UKRI GCRF Living Deltas Hub, one of twelve major transdisciplinary projects funded under the £1.5 billion UK Government Global Challenges Research Fund. My work involved developing Python-based data processing pipelines integrated with hydrodynamic models to simulate large-scale fluvial flood dynamics in the Vietnamese Mekong Delta, contributing to flood risk assessments under future climate scenarios.

My doctoral research took a different but related direction, focusing on mass movement hazards. I developed a GPU-accelerated model based on the discrete element method (DEM) for high-performance simulation of flow-like landslides, and subsequently coupled it with a depth-averaged model (DAM) to capture the fully dynamic behaviour of landslide events efficiently and accurately. The coupled model was successfully applied to simulate the 2019 Shuicheng landslide in China, demonstrating its potential for real-world hazard and risk assessment.