Fred Conrad’s research concerns the development of new methods and data sources for conducting social research. His work is largely focused on survey methodology, but he also explores the use of social media content as a complement to survey data and as a source of large-scale qualitative insights. His focus is on data quality and reducing measurement error. For example, live video interviews promote more thoughtful responses, e.g., less straightlining – the tendency to give the same answer to a battery of survey questions, but they also promote less candor when answering questions on sensitive topics. Measurement error in social media include misclassification in the automated interpretation of content using methods such as sentiment analysis and topic modeling, as well as selective self-presentation (only posting flattering content). Equally challenging is not knowing the extent to which users differ from the population to which one might wish to generalize results.
My research interests are in natural language semantics and psycholinguistics, focusing on verbs. I conduct behavioral psycholinguistic experiments with methodologies such as self-paced reading and maze tasks, as well as surveys of linguistic and semantic judgments. I also study semantic variation using corpora and datasets such as the Twitter Decahose, to better understand how words have developed diverging meanings in different communities, age groups, or regions. I use primarily R and Python to collect, manage, and analyze data. I direct the UM WordLab in the linguistics department, working with students (especially undergraduates) on experimental and computational research focusing on lexical representations.
Anthony Vanky develops and applies data science and computational methods to design, plan, evaluate cities, emphasizing their applications to urban planning and design. Broadly, his work focuses on the domains of transportation and human mobility; social behaviors and urban space; policy evaluation; quantitative social sciences; and the evaluation of urban form. Through this work, he has extensively collaborated with public and private partners. In addition, he considers creative approaches toward data visualization, public engagement and advocacy, and research methods.
Anthony Vanky’s Cityways project analyzed 2.2 million trips from 135,000 people over one year to understand the factors that influence outdoor pedestrian path choice. Factors considered included weather, urban morphology, businesses, topography, traffic, the presence of green spaces, among others.
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.
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
My research focuses on methods, applications, and ethics of Computational Modeling in Human-Computer Interaction (HCI). Understanding and modeling human behavior supports innovative information technology that will change how we study and design interactive user experiences. I envision modeling the human accurately across domains as a theoretical foundation for work in HCI in which computational models provide a foundation to study, describe, and understand complex human behaviors and support optimization and evaluation of user interfaces. I create technology that automatically reasons about and acts in response to people’s behavior to help them be productive, healthy, and safe.
Societal control tends to be implemented from the top-down, whether that is a private corporation or a communist state. How can data science empower from the bottom-up? Computational technologies can be designed to replace extractive economies with generative cycles. My research includes AI for the artisanal economy; computational modeling of Indigenous practices; and other means for putting the power of data science in the service of generative justice.
Student moving from her knowledge of braiding algorithms, to her program for braiding patterns, to a mannequin head for installation in adult braider’s shops. https://csdt.org/culture/cornrowcurves/index.html
My research focuses on the causes, dynamics and outcomes of conflict, at the international and local levels. My methodological areas of interest include spatial statistics, mathematical/computational modeling and text analysis.
Map/time-series/network plot, showing the flow of information across battles in World War II. Z axis is time, X and Y axes are longitude and latitude, polygons are locations of battles, red lines are network edges linking battles involving the same combatants. Source: https://doi.org/10.1017/S0020818318000358
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.
My research explores the interplay between corporate decisions and employee actions. I currently use anonymized mobile device data to observe individual behaviors, and employ both unsupervised and supervised machine learning techniques.
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.