Data Science Seminar Series: Peter James, Sc.D.
The Data Science Seminar Series presents bi-weekly talks from innovators in the data science and cancer research communities both within and outside of NCI.
Spatial Factors and Health: The Impact of Natural Environments on Health and Future Directions in Mobile Health Technology and Deep Learning for Epidemiologic Research
In this talk, Dr. James will discuss his team's work in deploying applications and wearable devices within a subsample of the Nurses' Health Study 3 (NHS3) prospective cohort. He will also discuss approaches they are developing to use deep learning algorithms to process ground-level street images to create novel metrics of exposure to natural environments. The approaches discussed will aid in advancing epidemiologic research to better evaluate causal mechanisms between spatial factors and behavioral risk factors for cancer.
Individuals with disabilities who need sign language interpreters and/or reasonable accommodation to participate in this event should contact (Eve Shalley (240-276-5104), and/or the Federal TTY Relay number (1-800-877-8339), or firstname.lastname@example.org). Requests should be made at least five days in advance of the event.
Assistant Professor, Harvard Medical School and Harvard Pilgrim Health Care Institute
Trained in environmental health and epidemiology, Dr. Peter James has focused his research on estimating the influence of geographic contextual factors, including exposure to nature, the built environment, the food environment, air pollution, light pollution, noise, and socioeconomic factors, on health behaviors and chronic disease. Dr. James has almost a decade of experience working with large prospective cohort studies, including the Nurses’ Health Studies, the Framingham Heart Study, and the Southern Community Cohort Study, where he has aided in the creation of many geographic-based variables and linked them to health data. More recently, Dr. James is developing methodologies to assess real-time, high spatio-temporal resolution objective measures of location and behavior by linking smartphone-based global positioning systems (GPS) and wearable device accelerometry data to understand how contextual factors influence health behaviors.