Todd I Herrenkohl

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Before joining the faculty at the University of Michigan in 2018 as Professor and Marion Elizabeth Blue Chair of Children and Families, I was Co-Director of the 3DL Partnership at the University of Washington, where I collaborated with academic colleagues, students, and service providers throughout the state to conduct and translate research on social emotional learning (SEL) and trauma-informed practices. I am now pursuing a similar line of research in Michigan, where I am collaborating with state partners and to identify, develop, and refine new approaches to disseminate research for schools and early childhood settings engaged in SEL and trauma work. As a scholar, I am committed to increasing the visibility, application, and sustainability of evidence-based programs and practices relevant to these topics and have worked extensively in the U.S. and internationally to advance goals for prevention and the promotion of child well-being.

Andrew Grogan-Kaylor

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My core intellectual interest is the way in which parenting behaviors, like the use of physical punishment, or parental expressions of emotional warmth, have an effect on child outcomes like aggression, antisocial behavior, anxiety and depression, and how these dynamics play out across contexts, neighborhoods, and cultures.  A lot of my work is done with international samples. In my work I use statistical models, like multilevel models and some econometric models, and software like Stata, R, HLM and ArcGIS, to examine things like growth and change over time, or community, school or parent effects on children and families.  I have emerging interests in text-mining and natural language processing.

Visualization of multilevel modeling using High School and Beyond data set.

Visualization of multilevel modeling using High School and Beyond data set.

Joseph Ryan

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.

Joseph Himle

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Joseph A. Himle, PhD, is the Associate Dean for Research and the Howard V. Brabson Collegiate Professor of Social Work, School of Social Work, and Professor of Psychiatry, Medical School, at the University of Michigan, Ann Arbor.

The goal of Prof. Himle’s research is to design, develop and test a inconspicuous, awareness-enhancement and monitoring device (AEMD) which will assist the treatment of trichotillomania (TTM), a disorder involving recurrent pulling of one’s hair resulting in noticeable hair loss. TTM is associated with significant impairments in social functioning and often has a profound negative impact on self-esteem and well being. Best practice treatment for TTM involves a form of behavioral therapy known as habit reversal therapy (HRT). HRT requires persons with trichotillomania to be aware of their hair pulling behaviors, yet the majority of persons with TTM pull most of their hair outside of their awareness . HRT also requires TTM sufferers to record the frequency and duration of their hair pulling behaviors yet it is obviously impossible for a person to monitor behaviors that they are unaware of. Our Phase I efforts have produced a prototype device (AEMD) that solves these two problems. The prototype AEMD signals the TTM sufferer if their hand approaches their hair, thereby bringing pulling-related behavior into awareness. The prototype AEMD also logs the time, date, duration, and user classification of hair pulling related events and can later transfer the logged data to a personal computer for analysis and data presentation. He continues to refine this device and seek to integrate it with smart-phones to better understand activities and locations associated with hair pulling or other body-focused repetitive behaviors (e.g., skin picking). In the future, he seeks to pool data from users to get a better sense of common situations and other factors associated with elevated pulling rates. He intends to develop other electronic tools to detect, monitor and intervene with other mental disorders in the future.

Brian Perron

Brian Perron

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Brian E. Perron, Ph.D., is an Associate Professor at the University of Michigan’s School of Social Work. Dr. Perron received his Ph.D. from Washington University in St. Louis and a specialization in Data Science from Johns Hopkins University. Dr. Perron has extensive experience in services research for persons with mental health and substance use disorders. His research (NCBI, Google Scholar) has been supported by the National Institutes of Health, Department of Veterans Affairs, and the State of Michigan. He recently published books on the topics of measurement (Oxford University Press) and social work practice (Sage Publications). Dr. Perron’s recent work focuses on helping community-based organizations more effectively use administrative data to improve service delivery and other business processes.This includes developing user-friendly and sustainable data management systems; using data visualizations to facilitate interpretation of data, especially for non-technical users; and building organizational capacity to promote data-driven decision making.