Gary Darmstadt

Gary L. Darmstadt, MD, MS, is associate dean for maternal and child health and a professor in the department of pediatrics at the Stanford University School of Medicine. Previously, Dr. Darmstadt was senior fellow in the Global Development Program at the Bill & Melinda Gates Foundation (BMGF), where he led a cross-foundation initiative assessing how addressing gender inequalities and empowering women and girls leads to improved health and development outcomes. Prior to this role, he served as BMGF director of family health, leading strategy development across nutrition, family planning, and maternal, newborn, and child health.

Darmstadt was formerly associate professor and founding director of the International Center for Advancing Neonatal Health in the Department of International Health at the Johns Hopkins Bloomberg School of Public Health. He has trained in pediatrics at Johns Hopkins University, in dermatology at Stanford University, and in pediatric infectious disease as a fellow at the University of Washington, Seattle, where he was assistant professor in the Department of Pediatrics and the School of Medicine. Dr. Darmstadt left the University of Washington to serve as senior research advisor for the Saving Newborn Lives program of Save the Children-US, where he led the development and implementation of the global research strategy for newborn health and survival. He holds a BS from California Polytechnic State University, an MS from the University of Wisconsin, Madison, and an MD from the University of California, San Diego.

King Center Supported Research

2022 - 2023 Academic Year | Global Development Research Funding

Prediction of Maternal and Infant Outcomes in Bangladesh, Zimbabwe, and Kenya from Metabolomic Machine Learning Analyses

Preterm birth is the largest cause of under-five child mortality and a risk for adverse neonatal outcomes. 82% of under-five deaths occur in sub-Saharan Africa and southern Asia. Our goal is to develop prediction models for preterm birth and neonatal outcomes enabling better diagnosis and timely interventions. We measured metabolites in maternal and neonatal blood samples collected in cohorts in Bangladesh, Zimbabwe and Kenya. We next need to retrieve related non-electronic records stored in Zimbabwe and Bangladesh. By using machine learning we will then identify metabolites (10 or less) that accurately predict the risk for preterm birth and neonatal outcomes.