Bilal Butt is an Associate Professor in the School for Environment and Sustainability at the University of Michigan. His research is concerned with understanding the drivers and effects of violent conflicts over natural resources. He places a large emphasis on empirical fieldwork to understand the lived geographies of the interactions between people and the environment in ecologically heterogeneous regions. He combines various geospatial technologies with historical and ecological dynamics of dryland environments to understand how differential power relations between agencies, states, and other actors come together to influence the etiology of resource conflicts. He is also interested in the ways that scientific and technical appraisals of indigenous peoples and environments have misread the landscape, leading to orientalist approaches to development programs. He has also had a long history of engagement on questions of environmental conflicts, particularly around wildlife poaching, land grabs, and green energy. Dr. Butt received the National Science Foundations Career Award and is a recipient of the Superior Teaching Award from the University of Michigan. He has published in diverse journals such as the Annals of the Association of American Geographers, Remote Sensing of Environment, Journal of Applied Ecology and Humanity. He teaches courses on Conservation and Development, Political Ecology, Environmental Security and Conflict, Environmental Governance, and Preparing for International Fieldwork.
Wildlife-Livestock Relationships in Kenya
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.
Cyber-security is a complex and multi-dimensional research field. My research style comprises an inter-disciplinary (primarily rooted in economics, econometrics, data science (AI/ML/Bayesian and Frequentist Statistics), game theory, and network science) investigation of major socially pressing issues impacting the quality of cyber-risk management in modern networked and distributed engineering systems such as IoT-driven critical infrastructures, cloud-based service networks, and app-based systems (e.g., mobile commerce, smart homes) to name a few. I take delight in proposing data-driven, rigorous, and interdisciplinary solutions to both, existing fundamental challenges that pose a practical bottleneck to (cost) effective cyber-risk management, and futuristic cyber-security and privacy issues that might plague modern (networked) engineering systems. I strongly strive for originality, practical significance, and mathematical rigor in my solutions. One of my primary end goals is to conceptually get arms around complex, multi-dimensional information security and privacy problems in a way that helps, informs, and empowers practitioners and policy makers to take the right steps in making the cyber-space more secure.
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.
Fred Conrad’s research concerns the development of new methods and data sources for conducting social research. His work is largely focused on survey methodology, but he also explores the use of social media content as a complement to survey data and as a source of large-scale qualitative insights. His focus is on data quality and reducing measurement error. For example, live video interviews promote more thoughtful responses, e.g., less straightlining – the tendency to give the same answer to a battery of survey questions, but they also promote less candor when answering questions on sensitive topics. Measurement error in social media include misclassification in the automated interpretation of content using methods such as sentiment analysis and topic modeling, as well as selective self-presentation (only posting flattering content). Equally challenging is not knowing the extent to which users differ from the population to which one might wish to generalize results.
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.
I am a social epidemiologist with expertise in data collection, analysis, and translation. My research is focused on quantifying health inequities at the individual, community, and national level and examining how policy and social factors impact these inequities. My experience has spanned academic, clinical, and community settings, providing me with a unique perspective on the value and need for epidemiologic research and dissemination in multiple contexts. My current work focuses on the health equity impact of tobacco product use as part of the University of Michigan Tobacco Center of Regulatory Science, the Center for the Assessment of Tobacco Regulations (CAsToR). I am examining sociodemographic inequities in polytobacco use (the use of multiple tobacco products) across multiple nationally representative datasets. I am also an active member of CAsToR’s Data Analysis and Dissemination (DAD) Core. Additionally, I am collaborating with colleagues in Chicago to disseminate findings from a community-level probability survey of 10 Chicago communities, of which I served as Co-PI while working at a hospital system in Chicago. We continue to publish on the unique survey process, sharing our community-driven approach to conducting research and disseminating findings in partnership with surveyed communities.
