This workshop, organised by the Department of Mathematics at Imperial College London, the Machine Learning and Global Health Network, and AIMS South Africa, took place at AIMS from 24 to 28 March 2025. It brought together 34 AIMS students and 15 local and international participants from universities and industry. The course was presented by Alexandra Blenkinsop, Tristan Naidoo, Josh Corneck, Shozen Dan, and Michael Whitehouse from Imperial College London; Juliette Unwin from the University of Bristol; and Sahoko Ishida from the University of Oxford.

One of the groundbreaking advances in machine learning research in the past decade is surrounding the emergence of increasingly sophisticated, robust, and easily usable probabilistic programming languages. These new tools, including Stan or numpyro, hide tedious calculations involving automatic differentiation and gradient-based optimisation from the end-user, making modern statistical methods widely available to data scientists in Africa who wish to address some of the most urgent challenges on the continent, ranging from habitat degradation, air pollution, extreme weather events, disease outbreaks, and population health in general.

The workshop focused on integrating these advanced statistical techniques and machine learning to address diverse population health issues using epidemiological, spatial, and genomic data. Participants honed their skills in Bayesian workflows, probabilistic programming, Gaussian processes, and infectious disease modelling.

We extend our gratitude to our partners at Imperial College London and the Machine Learning and Global Health Network for the fruitful collaboration. The future of data-driven health solutions in Africa is bright!