Profile
Project Title:
Unravelling human-pathogen co-evolution with genomics and deep learning
Project Summary:
Infectious diseases have been powerful drivers of human evolution, exerting strong selective pressures on the genomes of modern populations. Genomic studies of historical epidemics—including the Black Death, the Spanish flu, smallpox, and malaria — have revealed enduring genetic imprints left by these demographic events. However, detecting rapid and short-lived selective processes remains a major methodological challenge. This project aims to overcome this limitation by developing a Deep Learning framework that integrates ancient and modern genomic data. Designed to identify signatures of host–pathogen coevolution, this approach will be applied and validated using Plasmodium vivax, the most widespread malaria parasite outside sub-Saharan Africa and the leading cause of malaria in the Americas. This research will increase our understanding of human–pathogen evolutionary dynamics and contribute to the development of predictive tools for assessing genetic susceptibility to future infectious disease outbreaks
Supervisors: