The primary tools currently in use are variations of linear models (regression, MLM, SEM, and so on) as we pursue the initial aims of the NICHD funded work. We are expanding into new areas that require new tools. Our adolescent sample is diverse, selected through quota sampling of high schools close enough to UM to afford the use of neuroimaging tools, but it is not population representative. To overcome this, we have begun work to calibrate our sample with the nationally representative Monitoring the Future study, implementing pseudo-weighting and multilevel regression and post-stratification. To enable much more powerful analyses, we are aiming toward the harmonization of multiple, high quality longitudinal databases from adolescence through early adulthood. This would benefit traditional analyses by allowing cross-validation with high power, but also provide opportunities for newer data science tools such as computational modeling and machine learning approaches.
My research focuses on the development and evaluation of novel interventions that leverage emerging technologies to train members of the healthcare workforce around adhering to guidelines. I study how to scale custom designed teaching and learning platforms and evaluate their use to motivate effective communication and dissemination of evidence based practice. Other emphases of my work include health policy literacy, translation and communication of health services research, and improving health system literacy in urban communities. I have developed and evaluated numerous web based educational interventions that employ the “flipped classroom” design with an emphasis on understanding the data and analytics that guide successful implementation and promote high fidelity for members of the healthcare workforce. As an implementation scientist, I rely on the integration of data and analytics to understand what motivates successful program implementation.
In addition to the development of these platforms, I have extensive experience developing and evaluating online, hybrid residential, residential courses, and MOOCs related to healthcare management, non-profit management, healthcare finance, and health economics that employ engaging lessons and modules, interactive graphics, and a blended learning format to aid health professions students, and both undergraduate and graduate public health students in understanding the healthcare system. My MOOC entitled “Understanding and Improving the U.S. Health Care System” has been taken by over 5,000 learners and is characterized by the use of “big data” to understand how future healthcare providers learn health policy.
I am a Research Fellow in the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. My research is currently supported by a NSF project, Developing Evidence-based Data Sharing and Archiving Policies, where I am analyzing curation activities, automatically detecting data citations, and contributing to metrics for tracking the impact of data reuse. I hold a Ph.D. in Geography from UC Santa Barbara and I have expertise in GIScience, spatial information science, and urban planning. My interests also include the Semantic Web, innovative GIS education, and the science of science. I have experience deploying geospatial applications, designing linked data models, and developing visualizations to support data discovery.
J. Trent Alexander is the Associate Director and a Research Professor at ICPSR in the Institute for Social Research at the University of Michigan. Alexander is a historical demographer and builds social science data infrastructure. He is currently leading the Decennial Census Digitization and Linkage Project (joint with Raj Chetty and Katie Genadek) and ResearchDataGov (joint with Lynette Hoelter). Prior to coming to ICPSR in 2017, Alexander initiated the Census Longitudinal Infrastructure Project at the Census Bureau and managed the Integrated Public Use Microdata Series (IPUMS) at the University of Minnesota.
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.
Dr. Hemphill studies conversations in social media and aims to promote just access to social media spaces and their data. She uses computational approaches to modeling political topics, predicting and addressing toxicity in online discussions, and tracing linguistic adaptations among extremists. She also studies digital data curation and is especially interested in ways to measure and model data reuse so that we can make informed decisions about how to allocate data resources.
I am interested in the intersection of big data, data science, privacy, security, public policy, and law. At U-M, this includes co-convening the Dissonance Event Series, a multi-disciplinary collaboration of faculty and graduate students that explore the confluence of technology, policy, privacy, security, and law. I frequently guest lecture on these subject across campus, including at the School of Information, Ford School of Public Policy, and the Law School.
I am interested in the evolutionary processes that originate “mega-diverse” biotic assemblages and the role of ecology in shaping the evolution of diversity. My program studies the evolution of Neotropical freshwater fishes, the most diverse freshwater fish fauna on earth, with an estimate exceeding 7,000 species. My lab combines molecular phylogenetics and phylogeny-based comparative methods to integrate ecology, functional morphology, life histories and geography into analyses of macroevolutionary patterns of freshwater fish diversification. We are also comparing patterns of diversification across major Neotropical fish clades. Relying on fieldwork and natural history collections, we use methods that span
Andrea Thomer is an assistant professor of information at the University of Michigan School of Information. She conducts research in the areas of data curation, museum informatics, earth science and biodiversity informatics, information organization, and computer supported cooperative work. She is especially interested in how people use and create data and metadata; the impact of information organization on information use; issues of data provenance, reproducibility, and integration; and long-term data curation and infrastructure sustainability. She is studying a number of these issues through the “Migrating Research Data Collections” project – a recently awarded Laura Bush 21st Century Librarianship Early Career Research Grant from the Institute of Museum and Library Services. Dr. Thomer received her doctorate in Library and Information Science from the School of Information Sciences at the University of Illinois at Urbana‐Champaign in 2017.