I am currently a Visiting Researcher at Imperial through the GW4 MRC Broadening Horizons programme, working with Professor Leonid Chindelevitch to benchmark machine learning methods for antimicrobial resistance (AMR) prediction using large-scale, curated genotype–phenotype datasets. This work focuses on improving the robustness and generalisability of predictive models for real-world clinical and public health applications.

My research centres on antimicrobial resistance (AMR), where I apply bioinformatics and machine learning to understand the genomic determinants of resistance, virulence, and pathogen evolution. I am particularly interested in translating these approaches into practical tools for surveillance and decision-making, especially in low-resource settings.

I am also an MRC-funded PhD student at the University of Bristol under the GW4 BioMed2 Doctoral Training Partnership (Population Health theme). My PhD research is conducted in collaboration with the UK Health Security Agency, where I develop machine learning approaches to forecast foodborne disease outbreaks and identify genomic mechanisms of AMR, supporting timely public health response. My work is supervised by Professor Kristen Reyher, Dr Sion Bayliss, Professor Andrew Dowsey, and Dr Lauren Cowley.

Previously, I completed an MSc in Bioinformatics at Makerere University (Fogarty International Center, NIH-funded EANBIT programme), where I evaluated the cross-geographical generalisability of AMR predictive models by leveraging transfer learning to adapt UK-based datasets for clinical application in low- and middle-income countries. I have also worked as a Research Data Scientist at Bristol's Jean Golding Institute (UK) and the Infectious Diseases Institute’s ACE (Uganda).