Dr. VanEseltine is a sociologist and data scientist working with large-scale administrative data for causal and policy analysis. His interests include studying the effects of scientific infrastructure, training, and initiatives, as well as the development of open, sustainable, and replicable systems for data construction, curation, and dissemination. As part of the Institute for Research on Innovation and Science (IRIS), he contributes to record linkage and data improvements in the research community releases of UMETRICS, a data system built from integrated records on federal award funding and spending from dozens of American universities. Dr. VanEseltine’s recent work includes studying the impacts of COVID-19 on academic research activity.
Inbal (Billie) Nahum-Shani is a Research Associate Professor in the Institute for Social Research, and a founding member of the Data-science for Dynamic Decision-making lab (d3lab) at the University of Michigan. Her research focuses on conceptual and methodological issues pertaining to the construction of effective Adaptive Interventions — a treatment design in which ongoing information from the person is used to individualize the type/dose/modality of support (or treatment); and Just-In-Time Adaptive Interventions (JITAIs) — a special form of adaptive interventions in which mobile devices are used to provide support in a timely and ecological manner.
My research lies in cutting-edge methodology development in streams of Bayesian statistics, complex survey inference, missing data imputation, causal inference, and data confidentiality protection. I have extensive collaboration experiences with health services researchers and epidemiologists to improve healthcare and public health practice, and have been providing statistical support to solve sampling and analysis issues on health and social science surveys.
Michael Traugott, PhD, is Professor Emeritus of Communication Studies, Professor Emeritus of Political Science, College of Literature, Science, and the Arts, Research Professor Emeritus, Center for Political Studies and Adjunct Research Professor, Center for Political Studies, Institute for Social Research.
Professor Traugott studies the mass media and their impact on American politics. This includes research on the use of the media by candidates in their campaigns and its impact on voters, as well as the ways that campaigns are covered and the impact of this coverage on candidates. He has a particular interest in the use of surveys and polls and the way news organizations employ them to cover campaigns and elections.
Prof. Shapiro is the Lawrence R. Klein Collegiate Professor of Economics, College of Literature, Science, and the Arts and Research Professor, Survey Research Center, Institute for Social Research, at the University of Michigan, Ann Arbor.
Prof. Shapiro’s general area of research is macroeconomics. He has studied investment and capital utilization, business-cycle fluctuations, consumption and saving, financial markets, monetary policy, fiscal policy, and time-series econometrics. Among his current research interests are consumption, saving, retirement, and portfolio choices of households, the effects of tax policy on investment, using surveys in macroeconomics, and improving the quality of national economic statistics.
Dr. Mitchell’s research focuses on the causes and consequences of family formation behavior. He examines how social context such as neighborhood resources and values influence family processes and how those processes interplay with an individual’s genetic and epigenetic makeup to influence behavior, wellbeing, and health. His research also includes the development of new methods for integrating the collection and analysis of biological and social data.
Jowei Chen, PhD, is Associate Professor of Political Science in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor. Prof. Chen holds a secondary appointment in the Center for Political Studies in the Institute for Social Research.
Prof. Chen’s research focuses on political geography and political institutions in the United States. His work on legislative districts examines how the geography of Democrat and Republican voters, as well as the political manipulation of district boundaries, affects voters’ political representation in legislatures. This work uses individual-level and precinct-level data about elections, combined with computer simulations of the district-drawing process. Other research projects analyze the political composition of the federal workforce by analyzing the campaign contributions and partisanship of bureaucratic employees, linking employee records with voter registration records and campaign finance data.
Brian Min, PhD, is Associate Professor of Political Science in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor. Prof. Min holds secondary appointments as Research Associate Professor in the Center for Political Studies and the Institute for Social Research.
Prof. Min studies the political economy of development with an emphasis on distributive politics, public goods provision, and energy politics. His research uses high-resolution satellite imagery to study the distribution of electricity across and within the developing world. He has collaborated closely with the World Bank using satellite technologies and statistical algorithms to monitor electricity access in India and Africa, including the creation of a web platform to visualize twenty years of change in light output for every village in India (http://nightlights.io).
Michael Cafarella, PhD, is Associate Professor of Electrical Engineering and Computer Science, College of Engineering and Faculty Associate, Survey Research Center, Institute for Social Research, at the University of Michigan, Ann Arbor.
Prof. Cafarella’s research focuses on data management problems that arise from extreme diversity in large data collections. Big data is not just big in terms of bytes, but also type (e.g., a single hard disk likely contains relations, text, images, and spreadsheets) and structure (e.g., a large corpus of relational databases may have millions of unique schemas). As a result, certain long-held assumptions — e.g., that the database schema is always known before writing a query — are no longer useful guides for building data management systems. As a result, my work focuses heavily on information extraction and data mining methods that can either improve the quality of existing information or work in spite of lower-quality information.
Professor Owen-Smith conducts research on the collective dynamics of large scale networks and their implications for scientific and technological innovation and surgical care. He is the executive director of the Institution for Research on Innovation and Science (IRIS, http://iris.isr.umich.edu). IRIS is a national consortium of research universities who share data and support infrastructure designed to support research to understand, explain, and eventually improve the public value of academic research and research training.