Adriene Beltz

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The goal of my research is to leverage network analysis techniques to uncover how the brain mediates sex hormone influences on gendered behavior across the lifespan. Specifically, my data science research concerns the creation and application of person-specific connectivity analyses, such as unified structural equation models, to time series data; these are intensive longitudinal data, including functional neuroimages, daily diaries, and observations. I then use these data science methods to investigate the links between androgens (e.g., testosterone) and estradiol at key developmental periods, such as puberty, and behaviors that typically show sex differences, including aspects of cognition and psychopathology.

A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

Kai S. Cortina

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Kai S. Cortina, PhD, is Professor of Psychology in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.

Prof. Cortina’s major research revolves around the understanding of children’s and adolescents’ pathways into adulthood and the role of the educational system in this process. The academic and psycho-social development is analyzed from a life-span perspective exclusively analyzing longitudinal data over longer periods of time (e.g., from middle school to young adulthood). The hierarchical structure of the school system (student/classroom/school/district/state/nations) requires the use of statistical tools that can handle these kind of nested data.

 

Joseph Ryan

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Joseph Ryan, PhD, is Associate Professor of Social Work, School of Social Work and Faculty Associate in the Center for Political Studies, ISR, at the University of Michigan, Ann Arbor.

Prof. Ryan’s research and teaching build upon his direct practice experiences with child welfare and juvenile justice populations. Dr. Ryan is the Co-Director of the Child and Adolescent Data, an applied research center focused on using big data to drive policy and practice decisions in the field. Dr. Ryan is currently involved with several studies including a randomized clinical trial of recovery coaches for substance abusing parents in Illinois (AODA Demonstration), a foster care placement prevention study for young children in Michigan (MiFamily Demonstration), a Pay for Success (social impact bonds) study focused on high risk adolescents involved with the Illinois child welfare and juvenile justice system and a study of the educational experiences of youth in foster care (Kellogg Foundation Education and Equity). Dr. Ryan is committed to building strong University and State partnerships that utilize big data and data visualization tools to advance knowledge and address critical questions in the fields of child welfare and juvenile justice.

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, PhD, is Professor of Statistics, College of Literature, Science, and the Arts, Professor of Biostatistics, School of Public Health, and Director of the Consulting for Statistics, Computing, and Analytics Research (CSCAR) center.

Kerby Shedden received his PhD in Statistics from UCLA in 1999 and joined the University of Michigan the same year.  His research interests include genomics, genetics, and other areas of life science where large and complex data arise. He also is interested in computational statistics and statistical software development. He participates in many collaborative research efforts including biomarker screening for cancer and kidney disease outcomes, cell-based screening for understanding the behavior of chemical probes in cells, and genetic association analysis for longitudinal traits.

Peter X. K. Song

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Peter Song, PhD, is Professor of Biostatistics, School of Public Health, at the University of Michigan, Ann Arbor.

Dr. Song is interested in methodological developments related to modelling, statistical inference and applications in biomedical sciences.  One of his current research areas concerns the development of statistical methodology and algorithm for fusion learning and homogeneity pursuit in data integration to address various analytic challenges from data heterogeneity.  Another focus of his current research is on the regression analysis of networked data, with applications to electroencephalogram data analysis for the understanding of human growth and development.