Adriene Beltz

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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.

A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

Kai S. Cortina

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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

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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.

 

Elizabeth Bruch

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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.

 

 

Anna Kratz

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Anna Kratz, PhD, is Assistant Professor of Physical Medicine and Rehabilitation and the Center for Clinical Outcomes Development and Application (CODA) at the University of Michigan, Ann Arbor.

Dr. Kratz’s clinical research is focused on the characteristics and mechanisms of common symptoms (e.g. pain, fatigue, cognitive dysfunction) and functional outcomes in those with chronic clinical conditions.  Using a combination of ambulatory measurement methods of physical activity (actigraphy), heart rate variability, galvanic skin response, and self-reported experiences, her research aims to overlay the patient’s day-to-day experience with physiological markers of stress, sleep quality, and physical activity. She utilizes a number of computational approaches, including multilevel statistical modeling, signal processing, and machine learning to analyze these data. The ultimate goal is to use insights from these data to design better clinical interventions to help patients better manage symptoms and optimize functioning and quality of life.

Rie Suzuki

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Dr. Suzuki is a behavioral scientist and has major research interests in examining and intervening mediational social determinants factors of health behaviors and health outcomes across lifespan. She analyzes the National Health Interview Survey, Medical Expenditure Panel Survey, National Health and Nutrition Examination Survey as well as the Flint regional medical records to understand the factors associating with poor health outcomes among people with disabilities including children and aging.

Hyun-soo Ahn

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Hyun-soo Ahn is an Associate Professor of Operations and Management Science at the Michigan Business School. He joined Michigan in 2003 from the department of Industrial Engineering and Operations Research at UC Berkeley. In his research, Hyun-soo develops and analyzes mathematical models related to supply chain management, dynamic pricing and revenue management, workforce agility, and resource allocation. He is also interested in modeling the customer’s behavior (such as subscription) and how it affects the firm’s profit. He has worked with more than 20 companies and his research has been funded by several organizations including National Science Foundation. His papers appear in leading journals in the field, including Operations Research, M&SOM, and Journal of Applied Probability.

At Ross, he teaches supply chain analytics and business statistics to MBAs, Exec. MBAs, MSCM, and BBAs. He has won a number of teaching awards voted by students, including 2012 Exec MBA teaching excellence award, 2012 Global MBA teaching excellence award, and 2006 BBA teaching excellence award.

Jessica K. Camp

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Jessica K. Camp, PhD, is Assistant Professor of social work in the Department of Health and Health Services at the University of Michigan, Dearborn.

Her research focuses on using large nationally representative data from the United States and internationally (SIPP, ACS, GSOEP) to explore trends in poverty and inequality. Specifically, I examine ways that marginalized and hyper-marginalized groups experience economic disparity and labor market exclusion. My most recent completed study showed how welfare reform can have a powerful impact on the well-being of working women, especially women with vulnerabilities. My area of expertise as a data analyst is in complex samples, regression, and longitudinal models. I am hoping my future work will inform ways that “Big Data” can be used in social work research.

Josh Pasek

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Josh Pasek is Assistant Professor of Communication Studies and Faculty Associate in the Center for Political Studies at the University of Michigan.  His substantive research explores how new media and psychological processes each shape political attitudes, public opinion, and political behaviors.  Josh also examines issues in the measurement of public opinion including techniques for incorporating social trace data as a means of tracking attitudes and behaviors.  Current research evaluates whether the use of online social networking sites such as Facebook and Twitter might be changing the political information environment, and assesses the conditions under which nonprobability samples, such as those obtained from big data methods or samples of Internet volunteers can lead to conclusions similar to those of traditional probability samples.  His work has been published in Public Opinion Quarterly, Political Communication, Communication Research, and the Journal of Communication among other outlets.  He also maintains two R packages for producing survey weights (anesrake) and analyzing weighted survey data (weights).