Patrick Fowler

Patrick J. Fowler’s research aims to prevent homelessness and its deleterious effects on child, family, and community well-being. Trained in child clinical-community psychology, Fowler uses innovative methods that rigorously investigate policies and programs intended to promote housing and family stability. Recent research focuses on cross system collaborations to prevent child maltreatment associated with family homelessness, as well as youth homelessness in the transition from foster care to adulthood.

Fowler also designs and tests big data applications that improve fair and efficient delivery of homeless services; the approaches leverage linked administrative data to target prevention for households most likely to benefit.  His work applies a complex systems perspective to inform developmentally and culturally tailored responses to homelessness. He collaborates with experts in the areas of prevention science, artificial intelligence, social system dynamics, as well as network and systems science.

Fowler’s federally funded research has been supported by the National Institute of Child Health and Human Development, the U.S. Administration for Children and Families, and the U.. Department of Housing and Urban Development.  Fowler teaches courses in public health and social work focused on prevention science, program and systems evaluation, and developmental psychopathology.

Patrick Fowler

  • Associate Professor
  • Director, Doctoral Program in Public Health Sciences
  • PhD, Wayne State University
  • Office Phone: 314-935-5859
  • Email:
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Areas of Focus:

  • Preventing homelessness and the associated impact on children, families, and communities
  • Promoting family stability and healthy child development by investigating complex systems
  • Informing policies and programs through community-engaged computational methods
  • Developing responsible data science applications that promote social and health equity
  • Prevention science

Featured Publications

Allocating interventions based on counterfactual predictions: A case study on homelessness services