Greg’s research primarily investigates information flow in financial markets and the actions of agents in those markets – both consumers and producers of that information. His approach draws on theory from the social sciences (economics, psychology and sociology) combined with large data sets from diverse sources and a variety of data science approaches. Most projects combine data from across multiple sources, including commercial data bases, experimentally created data and extracting data from sources designed for other uses (commercial media, web scrapping, cellphone data etc.). In addition to a wide range of econometric and statistical methods, his work has included applying machine learning , textual analysis, mining social media, processes for missing data and combining mixed media.
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.
Dr. Fleischer’s research focuses on how the broader socioeconomic and policy environments impact health disparities and the health of vulnerable populations, in the U.S. and around the world. Through this research, her group employs various analytic techniques to examine data at multiple levels (country-level, state-level, and neighborhood-level), emphasizing the role of structural influences on individual health. Her group applies advanced epidemiologic, statistical, and econometric methods to this research, including survey methodology, longitudinal data analysis, hierarchical modeling, causal inference, systems science, and difference-in-difference analysis. Dr. Fleischer leads two NCI-funded projects focused on the impact of tobacco control policies on health equity in the U.S.
My research at ICPSR is developing ingest and curation workflows for new data types (including EEG) to ensure these data are Findable, Accessible, Interoperable, and Reusable (FAIR) within data repositories.
My funded projects and programs:
National Addiction and HIV Data Archive Program (NAHDAP) funded by the National Institute on Drug Abuse (NIDA)
Health and Medical Care Archive funded by Robert Wood Johnson Foundation (RWJF)
Archive of Data on Disability to Enhance Policy and research (ADDEP) funded by NIH
My work falls into three general application areas. I am an applied (accredited) biostatistician with a strong team science motivation and I collaborate with scientists in primarily the biomedical sciences, contributing expertise in experimental design, statistical analysis/modeling, and data visualization. I have held faculty appointments in Schools of Medicine and Nursing, and also worked as a senior scientist in the Human Research Program at the NASA Johnson Space Center. I currently direct an Applied Biostatistics Laboratory and Data Management Core within the UM School of Nursing, and maintain several collaborative research programs within the School, at NASA, and with collaborators elsewhere.
My research examines the ways in which individuals and organizations use data to improve. Quality improvement and data-intensive research approaches are central to my work along with forming equitable collaborations between researchers and frontline workers. Prior to joining the Department of Learning Health Sciences, I was the Director of Learning Analytics Research at Digital Promise and a Senior Education Researcher in the Center for Technology in Learning at SRI International. At both organizations, I developed data-intensive research-practice partnerships with educational organizations of all types. As a learning scientist working at the intersection of data-intensive research and quality improvement, my colleagues and I have developed tools and strategies (e.g., cloud-based, open source tools for engaging in collaborative exploratory data analyses) that partnerships between researchers and practitioners can use to measure learning and improve learning environments.
This is an image that my colleagues and I, over multiple projects, developed to communicate the multiple steps involved in collaborative data-intensive improvement. The “organize” and “understand” phases are about asking the right questions before the work of data analysis begins: “co-develop” and “test” are about taking action following an analysis. Along with identifying common phases, we have also observed the importance of the following supporting conditions: a trusting partnership, the use of formal improvement methods, common data workflows, and intentional efforts to support the learning of everyone involved in the project.
My research focuses on issues in data collection with hard-to-reach populations. In particular, she examines 1) nontraditional sampling approaches for minority or stigmatized populations and their statistical properties and 2) measurement error and comparability issues for racial, ethnic and linguistic minorities, which also have implications for cross-cultural research/survey methodology. Most recently, my research has been dedicated to respondent driven sampling that uses existing social networks to recruit participants in both face-to-face and Web data collection settings. I plan to expand my research scope in examining representation issues focusing on the racial/ethnic minority groups in the U.S. in the era of big data.
My research involves developing novel data collection strategies and image reconstruction techniques for Magnetic Resonance Imaging. In order to accelerate data collection, we take advantage of features of MRI data, including sparsity, spatiotemporal correlations, and adherence to underlying physics; each of these properties can be leveraged to reduce the amount of data required to generate an image and thus speed up imaging time. We also seek to understand what image information is essential for radiologists in order to optimize MRI data collection and personalize the imaging protocol for each patient. We deploy machine learning algorithms and optimization techniques in each of these projects. In some of our work, we can generate the data that we need to train and test our algorithms using numerical simulations. In other portions, we seek to utilize clinical images, prospectively collected MRI data, or MRI protocol information in order to refine our techniques.
We seek to develop technologies like cardiac Magnetic Resonance Fingerprinting (cMRF), which can be used to efficiently collect multiple forms of information to distinguish healthy and diseased tissue using MRI. By using rapid methods like cMRF, quantitative data describing disease processes can be gathered quickly, enabling more and sicker patients can be assessed via MRI. These data, collected from many patients over time, can also be used to further refine MRI technologies for the assessment of specific diseases in a tailored, patient-specific manner.