Ahmed developed strong machine learning expertise throughout his undergraduate, Masters and PhD degrees. He has expertise in supervised and unsupervised statistical modelling, linear regression, maximum likelihood estimators, bootstrap sampling and Bayesian statistics, to mention a few. He primarily uses Python and R to perform statistical calculations, with seven years of experience in Python and one-year of experience with R. Before joining MAP, in his previous PhD and postdoc positions, Ahmed managed an extremely large dataset (terabyte scale) and performed extensive analysis and visualizations of this data.
Ahmed is currently interested in applying his computational experience in medical imaging and research and joined the MAP team as a senior research scientist in August 2020. His role involves mapping the burden of P. falciparum and P. vivax malaria through spatiotemporal and statistical modelling infrastructure to predict malaria transmission and burden sub-nationally, nationally and globally and across a wide range of epidemiological settings.