Mariam Girguis

Papers

Total Citations

121

H-Index

About

Mariam Girguis is a leading environmental data scientist whose work bridges atmospheric science and machine learning to address critical gaps in air quality monitoring. Her research focuses on spatiotemporal modeling, satellite remote sensing, and deep learning, with a particular emphasis on improving the resolution and accuracy of aerosol optical depth (AOD) data. Girguis’s most influential contribution is her 2019 paper, "Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling," which has garnered 121 citations. In this work, she pioneered a deep learning framework that imputes missing satellite AOD measurements and downscales them to finer spatial scales, enabling more precise estimates of particulate matter exposure. This innovation has profound implications for environmental epidemiology, allowing researchers to better assess health risks in underserved regions with sparse monitoring networks. Girguis’s work is notable for its methodological rigor and practical impact, advancing the use of AI in environmental science. Her achievements highlight her as a rising figure in data-driven atmospheric research, with her methods increasingly adopted in studies linking air pollution to respiratory and cardiovascular outcomes.