Dr. Feng’s research involves conducting and using naturalistic observational studies to better understand the interactions between motorists and other road users including bicyclists and pedestrians. The goal is to use an evidence-based, data-driven approach that improves bicycling and walking safety and ultimately makes them viable mobility options. A naturalistic study is a valuable and unique research method that provides continuous, high-time-resolution, rich, and objective data about how people drive/ride/walk for their everyday trips in the real world. It also faces challenges from the sheer volume of the data, and as with all observational studies, there are potential confounding factors compared to a randomized laboratory experiment. Data analytic methods can be developed to interpret the behavioral data, make meaningful inferences, and get actionable insights.
My research interests are to improve safety associated with motor-vehicle transportation by addressing both active safety (increased crash avoidance) and passive safety (increased crash protection) issues through the development and application of a wide range of research methodologies. These methodologies are targeted at developing a better understanding and modeling of driver behavior, including physical and cognitive attributes, driver decision-making processes and human intention prediction. I am currently interested in applying data science to study the following topics:
*Driver state detection and prediction;
*Improve user intersection with automated vehicle technologies;
*Communication and interaction between vehicle and vulnerable road users
*Driving style classification
*Human factors issues associated with connected and automated vehicle technologies
Antonios M. Koumpias, Ph.D., is Assistant Professor of Economics in the department of Social Sciences at the University of Michigan, Dearborn. Prof. Koumpias is an applied microeconomist with research interests in public economics, with an emphasis on behavioral tax compliance, and health economics. In his research, he employs quasi-experimental methods to disentangle the causal impact of policy interventions that occur at the aggregate (e.g. states) or the individual (e.g. taxpayers) level in a comparative case study setting. Namely, he relies on regression discontinuity designs, regression kink designs, matching methods, and synthetic control methods to perform program evaluation that estimates the causal treatment effect of the policy in question. Examples include the use of a regression discontinuity design to estimate the impact of a tax compliance reminders on payments of overdue income tax liabilities in Greece, matching methods to measure the influence of mass media campaigns in Pakistan on income tax filing and the synthetic control method to evaluate the long-term effect of state Medicaid expansions on mortality.
Prof. Titiunik’s research interests lie primarily in quantitative methodology for the social sciences, with emphasis on quasi-experimental methods for causal inference and political methodology. She is particularly interested in the application and development of non-experimental methods for the study of political institutions, a methodological agenda that is motivated by her substantive interests on democratic accountability and the role of party systems in developing democracies. Some of her current projects include the application of web scraping and text analysis tools to measure political phenomena.
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.
The goal of my research is to leverage network analysis techniques to uncover how the brain mediates sex hormone influences on gendered behavior across the lifespan. Specifically, my data science research concerns the creation and application of person-specific connectivity analyses, such as unified structural equation models, to time series data; these are intensive longitudinal data, including functional neuroimages, daily diaries, and observations. I then use these data science methods to investigate the links between androgens (e.g., testosterone) and estradiol at key developmental periods, such as puberty, and behaviors that typically show sex differences, including aspects of cognition and psychopathology.
Kai S. Cortina, PhD, is Professor of Psychology in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.
Prof. Cortina’s major research revolves around the understanding of children’s and adolescents’ pathways into adulthood and the role of the educational system in this process. The academic and psycho-social development is analyzed from a life-span perspective exclusively analyzing longitudinal data over longer periods of time (e.g., from middle school to young adulthood). The hierarchical structure of the school system (student/classroom/school/district/state/nations) requires the use of statistical tools that can handle these kind of nested data.
Matthew Kay, PhD, is Assistant Professor of Information, School of Information and Assistant Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.
Prof. Kay’s research includes work on communicating uncertainty, usable statistics, and personal informatics. People are increasingly exposed to sensing and prediction in their daily lives (“how many steps did I take today?”, “how long until my bus shows up?”, “how much do I weigh?”). Uncertainty is both inherent to these systems and usually poorly communicated. To build understandable data presentations, we must study how people interpret their data and what goals they have for it, which informs the way that we should communicate results from our models, which in turn determines what models we must use in the first place. Prof. Kay tackles these problems using a multi-faceted approach, including qualitative and quantitative analysis of behavior, building and evaluating interactive systems, and designing and testing visualization techniques. His work draws on approaches from human-computer interaction, information visualization, and statistics to build information visualizations that people can more easily understand along with the models to back those visualizations.
People’s behavior is often contingent on what other people are doing or have done. In dating and job markets, for example, each person’s choices limit what opportunities are available to others. A classic problem in sociology is explaining the relationship between individuals’ actions and larger-scale social patterns. My strategy is to use computer models of how people’s choices co-evolve with aspects of their environment—known as agent-based models (ABMs)—to determine what behavioral or demographic features are important for understanding social processes. I then use statistical models to assess to what degree these features exist in the real world. Substantively, most of my work examines the drivers of neighborhood segregation. More recently, I embarked on a study of how mate choice strategies shape (and are shaped by) dating, marriage, and affair markets.
With Fred Feinberg (UM Marketing and Statistics), I am also exploring how new data sources can be combined with choice models. 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 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. In ongoing work, we show how decision processes regarding mate choice can be extracted from online dating activity data.