Dr. Jeanette Stingone
Dr. Jeanette Stingone
  • February 12, 2021
    11:30 am - 12:30 pm

Please join NYU CUSP for our Research Seminar Series, featuring leading voices in the growing field of Urban Informatics.

Our next virtual seminar will feature Dr. Jeanette Stingone, Assistant Professor in the Department of Epidemiology at the Columbia University Mailman School of Public Health, for a discussion on “Using Data Science to Address Inequities in Urban Environmental Health.”

Friday, February 12th, 2021
11:30 am to 12:30 pm EST

CUSP’s research seminars are open to the public!

Using Data Science to Address Inequities in Urban Environmental Health

There is a large focus in biomedical research to integrate data science into the fields of medicine and healthcare to promote precision medicine and improve outcomes for individuals. While given less focus, there is a parallel challenge in public health to harness advanced computational and analytic technologies to collect and use more accurate, extensive and equitable data to improve the health of communities. In this talk, Dr. Stingone will discuss three projects that aim to use techniques from data science to enhance how we use existing data for research into urban health inequities. Within the context of research focusing on interactions between chemical and non-chemical stressors in the urban environment, she will present work that integrates data science into multiple aspects of the research pipeline, including data compilation, analytic approaches to multiple exposures and facilitating data sharing.

Dr. Jeanette Stingone

Dr. Jeanette Stingone is an environmental epidemiologist with a focus on pregnancy as a vulnerable exposure period for pregnant people and their offspring. Supported by an NIEHS-funded career development award, her current research couples data science techniques with more traditional epidemiologic methods to investigate health effects associated with complex environmental mixtures and multiple exposures. This includes advancing the reuse of existing data through novel data linkages and harmonization, adapting machine learning analytic techniques to account for the specific challenges of multiple exposures and promoting data sharing and interoperability through the use of ontologies and semantic science.