Mike Nsubuga is an MRC-funded PhD student under GW4 BioMed2 MRC DTP (Population Health theme). His research, supervised by Dr. Sion Bayliss, Prof Kristen Reyher, Prof Andrew Dowsey, and Dr. Lauren Cowley is conducted in collaboration with the UK Health Security Agency (UKHSA).

His work focuses on developing machine learning approaches to forecast foodborne disease outbreaks and identify genomic mechanisms of antimicrobial resistance (AMR), with the aim of supporting timely public health decision-making and outbreak response.

Mike’s broader research interests lie at the intersection of bioinformatics, machine learning, and infectious disease genomics, with a particular focus on antimicrobial resistance, pathogen evolution, and global health applications in low- and middle-income countries (LMICs).

He is currently a Visiting Researcher at Imperial College London through the GW4 MRC Broadening Horizons programme, where he is working with Prof Leonid Chindelevitch to benchmark machine learning methods for AMR prediction using large-scale genotype–phenotype datasets.

Prior to his PhD, Mike completed an MSc in Bioinformatics at Makerere University under the NIH Fogarty International Center–funded EANBIT programme. His MSc research focused on evaluating the cross-geographical generalisability of AMR predictive models by leveraging transfer learning to adapt UK-based datasets for clinical application in low income countries.