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Jowei Chen

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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 secondary appointments in the Center for Political Studies and 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.

 

 

9/18/14 2014 Polical Science Department faculty and staff.

Brian Min

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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).

 

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Michael Cafarella

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My 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.

A peek inside a Michigan data center! My students and I visit whenever I am teaching EECS485, which teaches many modern data-intensive methods and their application to the Web.

A peek inside a Michigan data center! My students and I visit whenever I am teaching EECS485, which teaches many modern data-intensive methods and their application to the Web.

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Jason Owen-Smith

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Our data architecture combines naturally-occurring data from research grant inputs with scientific outputs including publications, citations, dissertations, and patents, as well as with biographic data on researchers scraped from the web and in databases. These data integrate with STAR METRICS administrative data on grant purchases and employment, which can in turn be linked to Longitudinal Employer-Household Dynamics (LEHD) Census data enabling individuals to be traced as they move across employers and start businesses. These data are then linked using cutting edge disambiguation/name-entity resolution, web scraping and entity extraction. This IRIS methodology is advancing the underlying computational sciences and creating more useful data for broader applications.

One year snapshot of the collaboration network of a single large research university campus. Nodes are individuals employed on sponsored project grants, ties represent copayment on the same grant account in the same year. Ties are valued to reflect the number of grants in common. Node size is proportional to a simple measure of betweenness centrality and node color represents the results of a simple (walktrip) community finding algorithm. The image was created in Gephi.

One year snapshot of the collaboration network of a single large research university campus. Nodes are individuals employed on sponsored project grants, ties represent copayment on the same grant account in the same year. Ties are valued to reflect the number of grants in common. Node size is proportional to a simple measure of betweenness centrality and node color represents the results of a simple (walktrip) community finding algorithm. The image was created in Gephi.

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Gerald Davis

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My research is broadly concerned with corporate governance and the effects of finance on society. Recent writings examine how ideas about corporate social responsibility have evolved to meet changes in the structures and geographic footprint of multinational corporations; whether “shareholder capitalism” is still a viable model for economic development; how income inequality in an economy is related to corporate size and structure; why theories about organizations do (or do not) progress; how architecture shapes social networks and innovation in organizations; why stock markets spread to some countries and not others; and whether there exist viable organizational alternatives to shareholder-owned corporations in the United States. Recent publications are available at http://webuser.bus.umich.edu/gfdavis/articles.htm.

Ties Among the Fortune 1000 Corporate Boards

Ties Among the Fortune 1000 Corporate Boards

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Brady West

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My current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, survey nonresponse, interviewer variance, and multilevel regression models for clustered and longitudinal data. I also conduct research in statistical software.

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Pamela Davis-Kean

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Davis-Kean is the Director of the Population, Neurodevelopment, and Genetics program at the Institute for Social Research. This group examines the complex transactions of brain, biology, and behavior as children and families develop across time. She is interested in both micro (brain and biology) and macro (family and socioeconomic conditions) aspects of development to understand the full developmental story of individuals.  Her primary focus in this area is how stress relates to family socioeconomic status and how that translates to parenting beliefs and behaviors that influence the development of children.

Susan Murphy, 2013 MacArthur Fellow

Susan A. Murphy

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I develop clinical trial designs and data analytic methods for informing sequential decision making in health. In particular  I focus on methods for constructing real time individualized sequences of treatments (a.k.a., treatment policies or Just-in-Time Adaptive Interventions) delivered by mobile devices.  This is an area of Precision Medicine. I develop new clinical trial designs (designs in which each person is randomized 100s or 1000s of times) and generalize reinforcement learning algorithms to analyze the data and construct treatment policies.  I also generalize data analytic method from causal inference for use in analyzing mobile health data.

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Margaret Hedstrom

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My research centers on the methods, costs, incentives, and implementation of scalable digital curation and archiving services as a core element of the underlying infrastructure for research data management, reproducible research, and data analysis.  I study the social and technical dimensions digital curation including data sharing behaviors among scientists in different research domains, techniques for automated metadata extraction and user-contributed metadata, requirements for meaningful reuse of numeric, image, and textual data, and long-term preservation of digital information.  My current research projects span projects involving researchers in environmental science and sustainability, social science, bioinformatics, and materials science.