Robert Manduca

By |

Professor Manduca’s research focuses on urban and regional economic development, asking why some cities and regions prosper while others decline, how federal policy influences urban fortunes, and how neighborhood social and economic conditions shape life outcomes. He studies these topics using computer simulations, spatial clustering methods, network analysis, and data visualization.

In other work he explores the consequences of rising income inequality for various aspects of life in the United States, using descriptive methods and simulations applied to Census microdata. This research has shown how rising inequality has lead directly to lower rates of upward mobility and increases in the racial income gap.

9.9.2020 MIDAS Faculty Research Pitch Video.

MIDAS Faculty Research Pitch, Fall 2021

Screenshot from “Where Are The Jobs?” visualization mapping every job in the United States based on the unemployment insurance records from the Census LODES data. http://robertmanduca.com/projects/jobs.html

Jeffrey Morenoff

By |

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.

Andrei Boutyline

By |

Cultural systems are fundamentally structural phenomena, defined by patterns of relations between elements of public representations and individual behaviors and cognitions. However, because such systems are difficult to capture with traditional empirical approaches, they usually remain understudied. In my work, I draw on network analysis, statistics, and computer science to create novel approaches to such analyses, and on cognitive science to theorize the objects of these investigations. Broader questions that interest me are: how are different cultural elements interrelated with one another? What is the relationship between public cultural representations and individual cognition and behavior? And how can we capture the structure of these interrelationships across large social and time scales? Methodologically, I am currently focused no developing applications of word embeddings and other natural language processing methods to sociological questions about cultural change.

Changing gender connotations of intelligence and studiousness throughout the latter half of the 20th century measured using word embeddings. Intelligence gained a masculine gender coding just as studiousness gained a masculine one. Scores are z-scored average cosine similarities between sets of keywords and a gender dimension. Data source: Corpus of Historical American English.

Elizabeth Bruch

By |

I am a computational social scientist and sociologist. In addition to my appointments at Michigan, I am an external faculty member at the Santa Fe Institute and serve on their Science Steering Committee; I also serve on the advisory board for the Social Science Research Council’s Social Data Initiative.

The vast amounts of activity data from sources such as cell phones and the Internet make it possible to study human behavior with an unparalleled richness of detail. Such “big data” are interesting to me in large part because they are behavioral data that allow us to observe how people explore their environment, engage in novel or habitual behaviors, interact with others, and learn from past experiences. My work combines models of human behavior with network science techniques to explore how individuals navigate complex and/or socially structured environments. One line of work focuses on developing “cognitively plausible” models of human decision-making. A 2016 paper used data from a major online dating site to look at mate selection strategies (e.g., the extent to which people invoke ‘deal-breakers’ or ‘deal-makers’) and how these strategies differ by age, gender, and other attributes. A 2019 study examined the decision processes that guide neighborhood choice, and the implications of these strategies for segregation dynamics. My more recent work on decision-making looks at how college students navigate course-taking and major declaration in a large public university, and the implications of these decision strategies for broader patterns of inequality.

A second line of work uses network science techniques to examine the structure of social environments. In two recent papers (2018, 2019), I described the vertical and horizontal organization of online dating markets, e.g., the extent to which singles are concentrated in particular submarkets and/or pursue partners who are “out of their league.” More recent ongoing work includes one project that examines competition in online dating markets and the strategies that mate-seekers use to mitigate that competition, and another project that describes the interdependent structure of curriculums for different majors at a large public university, and the implications of this structure for the flow of students through those majors.

 

Jason Owen-Smith

By |

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.

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.

Gerald Davis

By |

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