Yi Wang

Assistant Professor

Indiana University Richard M. Fairbanks School of Public Health

Dr. Yi Wang is an assistant professor at Indiana University Richard M. Fairbanks School of Public Health. Rooted in his training in biomedical bench research, pharmacy, and public health statistics, Yi approaches healthcare issues through both individual and population lens. He has expertise in population health data analytics such as epidemiological study and causal inference (natural experiment and randomized control trial). Trained in toxicology and biopharmaceuticals, he also has expertise in the biological mechanisms/pathways behind statistical inference drawn in epidemiological research.

Yi received postdoctoral training in Epidemiology from Brown University after obtaining his PhD in environmental epidemiology from the University of Michigan, Ann Arbor. Prior to becoming a population health scientist, he spent two years doing bench science in toxicology after earning his master’s degree in toxicology, and worked in the pharmaceutical industry as a business development associate after his undergraduate training in biopharmaceuticals.

BOLD SOLUTION: Marion County in Indiana has a long history of high-polluting industries, yet no information is available for health burden of hazards across communities. Yi created the Multilayer Data Community Action Tool (MDCAT), a data platform showing a clear picture of health burden and vulnerability related to environmental and social determinants of health in Indianapolis, and led a team in collaboration with the City of Indianapolis and community organizations to advance the inclusion of health equity in the Indy2020 Urban Plan. The tool accounts for social and health disadvantages, providing critical information to assess vulnerability for communities. It utilizes data extracted from publicly available local governmental websites and can be easily replicated to other U.S. counties.
The health of a nation relies on population health prevention as much as improving the health of individuals. We are better off if we improve both simultaneously and don’t only advance one at the expense of the other.