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Thomas Finholt

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Thomas Finholt is the Dean and Professor of Information at the School of Information. His current research focuses on: the energy costs of forming and maintaining social ties; computational mediation of trust in virtual organizations; and use of ultra-resolution collaboration environments.

Edward G. Happ

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Edward G. Happ is an Executive Fellow at the University of Michigan School of Information, where he is teaching and conducting research. He is also the Co-Founder and former Chairman of NetHope (www.nethope.org), a U.S. based consortium of 50+ leading international relief, development and conservation nonprofits focused on information and communications technology (ICT) and collaboration.

 

V. G. Vinod Vydiswaran

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V.G.Vinod Vydiswaran, PhD, is Assistant Professor in the Department of Learning Health Sciences with a secondary appointment in the School of Information at the University of Michigan, Ann Arbor.

Dr. Vydiswaran’s research focuses on developing and applying text mining, natural language processing, and machine learning methodologies for extracting relevant information from health-related text corpora. This includes medically relevant information from clinical notes and biomedical literature, and studying the information quality and credibility of online health communication (via health forums and tweets). His previous work includes developing novel information retrieval models to assist clinical decision making, modeling information trustworthiness, and addressing the vocabulary gap between health professionals and  laypersons.

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).

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.

 

Stephanie Teasley

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Dr. Teasley’s research has focused on issues of collaboration and learning, looking specifically at how sociotechnical systems can be used to support effective collaborative processes and successful learning outcomes. As Director of the LED lab, she leads learning analytics-based research to investigate how instructional technologies and digital media are used to innovate teaching, learning, and collaboration. The LED Lab is committed to providing a significant contribution to scholarship about learning at Michigan and in the broader field as well, by building an empirical evidentiary base for the design and support of technology rich learning environments.

Perry Samson

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The capacity to predict student success depends in part on our ability to understand “educationally purposeful” student behaviors and motivations and the relationship between behaviors and motivations and academic achievement. My research focuses on how to collect student behaviors germane to learning at a higher granularity and analyze the relationships between student performance and behaviors.

Ultimately this research is aimed at designing and constructing an “earlier warning system” wherein student guidance is quasi-automated and informed by motivation, background and behaviors and delivered within weeks of the beginning of classes.

Daniel Romero

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My research focuses on the empirical and theoretical analysis of social and information networks. I am particularly interested in understanding the mechanisms involved in network evolution, information diffusion, and interactions among people on the Web and in complex organizations.

Steven Abney

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Dr. Abney has pursued research in natural language understanding and natural language learning, including information extraction, biomedical text processing, integrating text analysis into web search, robust and rapid partial parsing, stochastic grammars, spoken-language information systems, extraction of linguistic information from scanned page images, dependency-grammar induction for low-resource languages, and semisupervised learning.