Nazanin Andalibi

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I use mixed methods to investigate social media’s use and roles in relation to self-disclosure, social support exchange, and other disclosure behavior outcomes and responses to them. I concentrate on experiences that can be distressing, traumatizing, isolating, or stigmatized, and contribute to poor wellbeing. Broadly, in these contexts, I address how we can design social computing systems that facilitate beneficial sensitive disclosures and desired disclosure outcomes such as (but not limited to) exchanging social support, meaningful interactions, reciprocal disclosures, and reduced stigma. Some contexts my work has focused on in the past include: mental health, sexual abuse, and pregnancy loss.

The research trajectory described above focuses on other social media users as information/disclosure recipients. I also investigate people’s attitudes and concerns when companies and algorithms are audiences or recipients of one’s sensitive information. This work goes beyond social media applications to include other types of social technologies. I critically examine the ways emerging technologies such as emotion artificial intelligence may engage with humans in times of distress or in otherwise private and personal settings. I explore the extent to which designing these technologies is appropriate in different contexts, and investigate what it would take for them to be sensitive to and foreground people’s values, needs, and desires.

Ivy F. Tso

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My lab researches how the human brain processes social and affective information and how these processes are affected in psychiatric disorders, especially schizophrenia and bipolar disorder. We use behavioral, electrophysiological (EEG), neuroimaging (functional MRI), eye tracking, brain stimulation (TMS, tACS), and computational methods in our studies. One main focus of our work is building and validating computational models based on intensive, high-dimensional subject-level behavior and brain data to explain clinical phenomena, parse mechanisms, and predict patient outcome. The goal is to improve diagnostic and prognostic assessment, and to develop personalized treatments.

Brain activation (in parcellated map) during social and face processing.

Elle O’Brien

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My research focuses on building infrastructure for public health and health science research organizations to take advantage of cloud computing, strong software engineering practices, and MLOps (machine learning operations). By equipping biomedical research groups with tools that facilitate automation, better documentation, and portable code, we can improve the reproducibility and rigor of science while scaling up the kind of data collection and analysis possible.

Research topics include:
1. Open source software and cloud infrastructure for research,
2. Software development practices and conventions that work for academic units, like labs or research centers, and
3. The organizational factors that encourage best practices in reproducibility, data management, and transparency

The practice of science is a tug of war between competing incentives: the drive to do a lot fast, and the need to generate reproducible work. As data grows in size, code increases in complexity and the number of collaborators and institutions involved goes up, it becomes harder to preserve all the “artifacts” needed to understand and recreate your own work. Technical AND cultural solutions will be needed to keep data-centric research rigorous, shareable, and transparent to the broader scientific community.

View MIDAS Faculty Research Pitch, Fall 2021

 

Lisa Levinson

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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.

William J. Gehring

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Williams is a Professor of Psychology, University of Michigan, Ann Arbor. His academic interests span two lines of teaching and research: his longest-running line of research concerns the brain processes involved in detecting errors, including how those processes affect anxiety disorders and children’s executive function. More recently, he has focused on higher education, teaching first-year undergraduate students evidence-based principles for learning and finding purpose in college. His research in this area uses institutional data to understand the factors within the college and the curriculum that promote or hinder academic success.

Andrew Krumm

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My research examines the ways in which individuals and organizations use data to improve. Quality improvement and data-intensive research approaches are central to my work along with forming equitable collaborations between researchers and frontline workers. Prior to joining the Department of Learning Health Sciences, I was the Director of Learning Analytics Research at Digital Promise and a Senior Education Researcher in the Center for Technology in Learning at SRI International. At both organizations, I developed data-intensive research-practice partnerships with educational organizations of all types. As a learning scientist working at the intersection of data-intensive research and quality improvement, my colleagues and I have developed tools and strategies (e.g., cloud-based, open source tools for engaging in collaborative exploratory data analyses) that partnerships between researchers and practitioners can use to measure learning and improve learning environments.

This is an image that my colleagues and I, over multiple projects, developed to communicate the multiple steps involved in collaborative data-intensive improvement. The “organize” and “understand” phases are about asking the right questions before the work of data analysis begins: “co-develop” and “test” are about taking action following an analysis. Along with identifying common phases, we have also observed the importance of the following supporting conditions: a trusting partnership, the use of formal improvement methods, common data workflows, and intentional efforts to support the learning of everyone involved in the project.

Christopher Fariss

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My core research focuses on the politics and measurement of human rights, discrimination, violence, and repression. I use computational methods to understand why governments around the world torture, maim, and kill individuals within their jurisdiction and the processes monitors use to observe and document these abuses. Other projects cover a broad array of themes but share a focus on computationally intensive methods and research design. These methodological tools, essential for analyzing data at massive scale, open up new insights into the micro-foundations of state repression and the politics of measurement.

People rely more on strong ties for job help in countries with greater inequality. Coefficients from 55 regressions of job transmission on tie strength are compared to measures of inequality (Gini coefficient), mean income per capita, and population, all measured in 2013. Gray lines indicate 95% confidence regions from 1000 simulated regressions that incorporate uncertainty in the country-level regressions (see below for more details). In each simulated regression we draw each country point from the distribution of regression coefficients implied by the estimate and standard error for that country and measure of tie strength. P values indicate the simulated probability that there is no relationship between tie strength and the other variable. Laura K. Gee, Jason J. Jones, Christopher J. Fariss, Moira Burke, and James H. Fowler. “The Paradox of Weak Ties in 55 Countries” Journal of Economic Behavior & Organization 133:362-372 (January 2017) DOI:10.1016/j.jebo.2016.12.004

Z. Tuba Suzer-Gurtekin

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Z. Tuba Suzer Gurtekin is an Assistant Research Scientist at the University of Michigan’s Institute for Social Research. Her research includes managing monthly surveys of consumer attitudes, expectations and behavior. Her published research focuses on methods to quantify nonresponse and measurement survey errors in probability and nonprobability sample surveys, and mixed-mode survey design and inference. Her research experience has included development of alternative sample, methodology and questionnaire designs, data collection and analysis methods for a general population in parallel survey modes. She also teaches Survey Sampling for Clinical Research at the University of Michigan’s Clinical Research Design and Statistical Analysis program (OJOC CRDSA).

Gongjun Xu

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Dr. Gongjun Xu is an assistant professor in the Department of Statistics at the University of Michigan. Dr. Xu’s research interests include latent variable models, psychometrics, cognitive diagnosis modeling, high-dimensional statistics, and semiparametric statistics.

Ginger Shultz

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The Shultz group uses data science methods in two primary ways 1) to investigate student placement in introductory chemistry courses and 2) to analyze student texts to provide instructors actionable intelligence about student learning. Using regression discontinuity we investigated the impact of taking general chemistry prior to organic chemistry on student performance and persistence in later chemistry courses and found that students who took general chemistry first benefitted by 1/4 of a letter grade but were no more likely to persist. A continued investigation using survey and interview methods indicated that this was related to academic skills rather than content preparation. Through the MWrite project we have collected a large corpus of student texts and are developing automated text analysis methods to glean information about student learning across disciplines, with specific focus on scientific reasoning.

Network representation of writing moves made by students in argumentative writing with relevant transition probabilities. The size of the node represents the relative frequency of operation use and the edge labels represent the transition probability with key transitions highlighted in orange.