Vicki Johnson-Lawrence

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Vicki Johnson-Lawrence, PhD, is Assistant Professor in the department of Public Health and Health Sciences at the University of Michigan, Flint.

Dr. Johnson-Lawrence is a social epidemiologist interested in the application of epidemiologic methods that capture the dynamic nature of psychosocial factors over the life course, and how these factors contribute to chronic disease risk.  Further, she is interested in racial/ethnic patterns of comorbid mental and physical health outcomes, and how these patterns vary throughout the life course.

Pamela Davis-Kean

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Pamela Davis-Kean, PhD, is Professor of Psychology, College of Literature, Science, and the Arts, and Research Professor, Survey Research Center and Research Center for Group Dynamics, Institute for Social Research, at the University of Michigan, Ann Arbor.

Prof. Davis-Kean is the Director of the Population, Neurodevelopment, and Genetics program at the Institute for Social Research. This group examines the complex transactions of brain, biology, and behavior as children and families develop across time. She is interested in both micro (brain and biology) and macro (family and socioeconomic conditions) aspects of development to understand the full developmental story of individuals.  Her primary focus in this area is how stress relates to family socioeconomic status and how that translates to parenting beliefs and behaviors that influence the development of children.

Kerby Shedden

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Kerby Shedden has broad interests involving applied statistics, data science and computing with data.  Through his work directing the data science consulting service he has worked in a wide variety of application domains including numerous areas within health science, social science, and transportation research.  A current major focus is development of software tools that exploit high performance computing infrastructure for statistical analysis of health records, and sensor data from vehicles and road networks.

Peter X. K. Song

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Dr. Song interested in the development and application of theories and methodologies from Data Science to solve scientific problems arising from medical and public health sciences, in particular from the fields of environmental health sciences and nutritional sciences. People from his lab are strongly interested in interdisciplinary research in the areas of statistics, operation research, and machine learning, with the core interest in the statistical foundation of big data analytics, and with target applications in processing and analyzing big data from various applied sciences, including asthma, environmental health sciences, nephrology, and nutritional sciences. His research projects have been funded by NIH, NSF and DARPA funding agencies. Visit Song Lab webpage for detail: