“The use of machine learning to analyze big data: Opportunities and Challenges for Epidemiology”
November 14, 2018, 12pm EST
Stephen Mooney
University of Washington, Seattle
Nicholas Tatonetti
Columbia University, New York
SERdigital 2018
Live Student Presentations
Julie Petersen is a 3rd year doctoral student at the Boston University School of Public Health (BUSPH). Her areas of interest are pregnancy and early human development, including the etiology of birth defects. She also does work in epidemiologic methods, namely quantitative bias analysis. Her dissertation research focuses on the novel application of contemporary analytic techniques (e.g., machine learning) to gain new insights in well-studied areas of perinatal epidemiology, including the consequences of birth spacing and gestational weight gain and predictors of intrauterine growth restriction. Julie completed her MPH at BUSPH with dual concentration in epidemiology and biostatistics.
Justin Rodgers is a social epidemiologist interested in the interconnections between social environments, psychosocial factors, and chronic health conditions. He recently completed his Doctor of Science in Social and Behavioral Sciences at the Harvard T.H. Chan School of Public Health, where his dissertation research examined the biopsychosocial mechanisms connecting psychosocial stress to chronic disease and mortality. Dr. Rodgers currently works with Dr. Daniel Kim as a postdoctoral research fellow at Northeastern University’s Department of Health Sciences to investigate the effectiveness of targeting various social determinants on reducing the burden of cardiovascular disease in the United States.