Senior Research Officer
Ahmed has 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 few. He mostly uses Python and R to perform statistical calculations, with seven years of experience in Python and one-year experience with R. During his PhD degree and postdocs, he has been managing extremely large dataset (terabyte scales) and performing extensive analysis and visualization of this dataset.
Ahmed is currently interested in applying his computational experience in medical imaging & research and has recently joined the MAP team as a senior research scientist. His role involves mapping the burdens of Plasmodium falciparum and Plasmodium 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.