Meredith Franklin

Papers

Total Citations

131

H-Index

About

Meredith Franklin is a leading environmental epidemiologist and biostatistician whose research lies at the intersection of air pollution exposure assessment, remote sensing, and machine learning. Her work focuses on developing advanced spatiotemporal models to estimate fine particulate matter (PM₂.₅) concentrations, enabling more accurate health effects studies. Her most cited paper, "Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling" (2019, 121 citations), pioneered the use of deep learning to fill gaps in satellite-derived aerosol optical depth (AOD) data, significantly improving the resolution and completeness of PM exposure estimates. Another key study, "Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley" (2020), demonstrated robust calibration models linking AOD to ground-level PM, critical for generating reliable exposure metrics in regions with sparse monitoring. Franklin’s contributions have advanced the integration of big data and artificial intelligence in environmental health, providing tools that support policy-relevant research on the health impacts of air pollution. Her work is widely cited by epidemiologists, atmospheric scientists, and public health researchers seeking to reduce exposure misclassification in large-scale studies.