2023 Awardees

John Goldsmith Award

Francine Laden, ScD

Francine Laden, ScD, is an environmental epidemiologist, specializing in the study of associations of chronic exposures to air pollution and neighborhood contextual variables with various disease and health outcomes. She has almost 30 years of experience working in the nationwide Harvard cohort studies (Nurses’ Health Study, Nurses’ Health Studies II and 3, Health Professionals’ Follow-up Study and the Growing Up Today Study) where, as the PI of several previous EPA, Komen, and NIH-funded grants, she laid the groundwork for studying geographic-based variables. She also has lead studies of air pollution in the Harvard Six Cities Study and designed and implemented a study of occupational exposures to diesel exhaust in the Trucking Industry Particle Study. She was co-director of an Environmental Health Disparities Center Center for Research on Environmental and Social Stressors in Housing Across the Life Course (CRESSH). She is currently the Director of the NIEHS funded Environmental Epidemiology T32 and am Associate Chair of the Department of Environmental Health and Program Director in the Exposure, Epidemiology and Risk Program in the Department of Environmental Health at the Harvard Chan School.

Tony McMichael Award

Marianthi-Anna Kioumourtzoglou, ScD

Marianthi-Anna Kioumourtzoglou, is an environmental engineer with an ScD in Environmental Health, with minors in Epidemiology and Biostatistics. She has conducted multiple large-scale studies assessing the impact of air pollution exposures on adverse health, including both single- and multi-pollutant approaches and has extensive experience with advanced statistical methods related to air pollution epidemiology, including source apportionment, assessing exposure to air pollution mixtures, and quantifying and correcting for exposure measurement error. Marianthi-Anna also has conducted several air pollution epidemiologic studies using very large administrative/register-based datasets, such as claims of Medicare enrollees across the entire US and the Danish National Registers system, as well as cohort data, such as the Nurses’ Health Study cohorts and the Strong Heart Study. Marianthi-Anna has specific expertise with data science methods to assess the effects of combinations of high-dimensional exposures. As the PI of a PRIME R01 (ES028805), in collaboration with Columbia University’s Data Science Institute, during which she developed novel robust methods for pattern recognition and dimensionality reduction when assessing high-dimensional exposures in health studies. She is the Contact PI of another R01 (ES030616) to develop a novel Bayesian machine learning algorithm to ensemble predictions from existing air pollution models to maximize spatio-temporal predictive accuracy and fully characterize spatio-temporal uncertainty in these predictions.