Elena Naumova Division Chair
Big Data and advanced analytics provide new dimensions and strategies to better understand the role of nutrition in a modern society, inform and monitor behavioral change, and to build an integrative inter-sectoral knowledge platform in nutrition science and policy. Untangling specific relationships between dietary intake, nutritional status, and health effects requires an understanding of the complex interactions among dietary, lifestyle, environmental, sociocultural, metabolic, and genetic exposures and the critical-thinking skills for quantifying them in large datasets.
The Division of Nutrition Data Science aims to prepare future practitioners and researchers to address emergent complex problems using novel information technologies, systems science, and other quantitative methods to design, implement, and evaluate relevant studies in nutrition science and policy. Faculty in the Division harness data derived from diverse disciplines and fields including epidemiology, demography, genetics, and metabolomics; incorporate various instruments including cohorts, surveys, surveillance, and individual and remote sensors; and utilize a range of quantitative methods including regression analysis, meta-analysis, cost-effectiveness analyses, modeling, and simulations.