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Chris Coxen

Research Data Scientist


MS, Analytics, North Carolina State University
MS, Wildlife Science, New Mexico State University
BS, Fisheries and Wildlife Sciences, North Carolina State University


Chris Coxen is a research data scientist who uses machine learning and data visualization techniques to solve problems in the environmental and public health fields. His technical expertise has supported investigations from the National Institutes of Health (NIH) and the Centers for Disease Control and Prevention (CDC), along with state and local partners. 

Currently, Chris is the lead data scientist on a project that analyzes aquatic community responses to nature-based climate solution projects as part of the Louisiana Watershed Initiative. Previously, he led a team that created a spatial analysis tool that quantified how underserved communities in the Raleigh-Durham, North Carolina region may benefit from greenspace investments that mitigate urban heat island effects. Using his insights into machine learning methods, he collaborated with RTI public health experts to help develop a framework to proactively identify and describe emerging electronic nicotine delivery system brands for the Bureau of Tobacco Free Florida. Additionally, Chris worked with RTI epidemiologists on the NIH-funded RECOVER Initiative: Researching COVID to Enhance Recovery. Through that work, he used machine learning and descriptive statistics to explore Long COVID symptom patterns in patient survey data.  

Chris has also developed Tableau and PowerBI dashboards for the Georgia Department of Behavioral Health and Developmental Disabilities and the CDC National Center for Health Statistics to better communicate and disseminate key priority health topics to the public.

Before joining RTI, Chris worked as a wildlife biologist for nonprofit conservation groups, and he was a graduate researcher within the United States Geological Survey's Cooperative Fish and Wildlife Research Units Program in New Mexico. His research experience incorporated geospatial data and machine learning techniques to model habitat suitability for an avian species of conservation concern across various climate change scenarios in the Southwest.

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