My research investigates social inequality and its maintenance across time and generations. Current projects focus on wealth inequality and its consequences for the next generation, the institutional context of social mobility processes and educational inequality in the United States and other industrialized countries. I also help expand the social science data infrastructure and quantitative methods needed to address questions on inequality and mobility. I serve as Principal Investigator of the Wealth and Mobility (WAM) study as well as Co-Investigator of the Panel Study of Income Dynamics (PSID). As such, my research draws on and helps construct nationally representative survey data as well as full-population administrative data. My methodological work has been focused on causal inference, multiple imputation, and measurement error.
My research program uses epidemiologic methods to examine the interrelationships between mental and physical health over the lifespan. A core feature of my research is the integration of conceptual and analytical approaches, methods, and models from social science, including natural language processing, and clinical/health disciplines with the aim of arriving at a more nuanced and comprehensive understanding of the ways in which mental and physical health interrelate. The goal of this work is to inform interventions that reflect an integrative approach to health.
Broadly, I study legal decision making, including decisions related to crime and employment. I typically use large social science data bases, but also collect my own data using technology or surveys.
My research focuses on building infrastructure for public health and health science research organizations to take advantage of cloud computing, strong software engineering practices, and MLOps (machine learning operations). By equipping biomedical research groups with tools that facilitate automation, better documentation, and portable code, we can improve the reproducibility and rigor of science while scaling up the kind of data collection and analysis possible.
Research topics include:
1. Open source software and cloud infrastructure for research,
2. Software development practices and conventions that work for academic units, like labs or research centers, and
3. The organizational factors that encourage best practices in reproducibility, data management, and transparency
The practice of science is a tug of war between competing incentives: the drive to do a lot fast, and the need to generate reproducible work. As data grows in size, code increases in complexity and the number of collaborators and institutions involved goes up, it becomes harder to preserve all the “artifacts” needed to understand and recreate your own work. Technical AND cultural solutions will be needed to keep data-centric research rigorous, shareable, and transparent to the broader scientific community.
Our team leads research on the Ethical, Legal, and Social Implications (ELSI) of learning health systems and related enterprises. Our research uses mixed methods to understand policies and practices that make data science methods (data collection and curation, AI, computable algorithms) trustworthy for patients, providers, and the public. Our work engages multiple stakeholders including providers and health systems, as well as the general public and minoritized communities on issues such as AI-enabled clinical decision support, data sharing and privacy, and consent for data use in precision oncology.
As an environmental epidemiologist and in collaboration with government and community partners, I study how social, economic, health, and built environment characteristics and/or air quality affect vulnerability to extreme heat and extreme precipitation. This research will help cities understand how to adapt to heat, heat waves, higher pollen levels, and heavy rainfall in a changing climate.
I manage research activities for the College and Beyond II study at ICPSR, including survey development and data infrastructure planning. My research broadly focuses on issues of postsecondary access and success for undergraduate and graduate students and uses quantitative methodologies.
My methodological research focus on developing statistical methods for routinely collected healthcare databases such as electronic health records (EHR) and claims data. I aim to tackle the unique challenges that arise from the secondary use of real-world data for research purposes. Specifically, I develop novel causal inference methods and semiparametric efficiency theory that harness the full potential of EHR data to address comparative effectiveness and safety questions. I develop scalable and automated pipelines for curation and harmonization of EHR data across healthcare systems and coding systems.
Jeffrey D. Morenoff is a professor of sociology, a research professor at the Institute for Social Research (ISR), and a professor of public policy at the Ford School. He is also director of the ISR Population Studies Center. Professor Morenoff’s research interests include neighborhood environments, inequality, crime and criminal justice, the social determinants of health, racial/ethnic/immigrant disparities in health and antisocial behavior, and methods for analyzing multilevel and spatial data.