Mar 02
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Aditi Misra

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Transportation is the backbone of the urban mobility system and is one of the greatest sources of environmental emissions and pollutions. Making urban transportation efficient, equitable and sustainable is the main focus of my research. My students and I analyze small scale survey data as well as large scale spatiotemporal data to identify travel behavior trends and patterns at a disaggregate level using econometric methods, which we then scale up to the population level through predictive and statistical modeling. We also design our own data collection methods and instruments, be it a network of smart devices or stated preference experiments. Our expertise lies in identifying latent constructs that influence decisions and choices, which in turn dictate demands on the systems and subsystems. We use our expertise to design incentives and policy suggestions that can help promote sustainable and equitable multimodal transportation systems. Our team also uses data analytics, particularly classification and pattern recognition algorithms, to analyze crash context data and develop safety-critical scenarios for automated and connected vehicle (CAV) deployment. We have developed an online game based on such scenarios to promote safe shared mobility among teenagers and young adults and plan to expand research in that area. We are also currently expanding our research to explore the use of NN in context information synthesis.

This is a project where we used classification and Bayesian models to identify scenarios that are risky for pedestrians and bicyclists. We then developed an online game based on those scenarios for middle schoolers so that they are better prepared for shared road conflicts.

Mar 02
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Sunghee Lee

Sunghee Lee

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My research focuses on issues in data collection with hard-to-reach populations. In particular, she examines 1) nontraditional sampling approaches for minority or stigmatized populations and their statistical properties and 2) measurement error and comparability issues for racial, ethnic and linguistic minorities, which also have implications for cross-cultural research/survey methodology. Most recently, my research has been dedicated to respondent driven sampling that uses existing social networks to recruit participants in both face-to-face and Web data collection settings. I plan to expand my research scope in examining representation issues focusing on the racial/ethnic minority groups in the U.S. in the era of big data.

Feb 13
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Todd I Herrenkohl

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Before joining the faculty at the University of Michigan in 2018 as Professor and Marion Elizabeth Blue Chair of Children and Families, I was Co-Director of the 3DL Partnership at the University of Washington, where I collaborated with academic colleagues, students, and service providers throughout the state to conduct and translate research on social emotional learning (SEL) and trauma-informed practices. I am now pursuing a similar line of research in Michigan, where I am collaborating with state partners and to identify, develop, and refine new approaches to disseminate research for schools and early childhood settings engaged in SEL and trauma work. As a scholar, I am committed to increasing the visibility, application, and sustainability of evidence-based programs and practices relevant to these topics and have worked extensively in the U.S. and internationally to advance goals for prevention and the promotion of child well-being.

Jan 08
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Andrei Boutyline

Andrei Boutyline

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Cultural systems are fundamentally structural phenomena, defined by patterns of relations between elements of public representations and individual behaviors and cognitions. However, because such systems are difficult to capture with traditional empirical approaches, they usually remain understudied. In my work, I draw on network analysis, statistics, and computer science to create novel approaches to such analyses, and on cognitive science to theorize the objects of these investigations. Broader questions that interest me are: how are different cultural elements interrelated with one another? What is the relationship between public cultural representations and individual cognition and behavior? And how can we capture the structure of these interrelationships across large social and time scales? Methodologically, I am currently focused no developing applications of word embeddings and other natural language processing methods to sociological questions about cultural change.

Changing gender connotations of intelligence and studiousness throughout the latter half of the 20th century measured using word embeddings. Intelligence gained a masculine gender coding just as studiousness gained a masculine one. Scores are z-scored average cosine similarities between sets of keywords and a gender dimension. Data source: Corpus of Historical American English.

Nov 20
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Yajuan Si

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My research lies in cutting-edge methodology development in streams of Bayesian statistics, complex survey inference, missing data imputation, causal inference, and data confidentiality protection. I have extensive collaboration experiences with health services researchers and epidemiologists to improve healthcare and public health practice, and have been providing statistical support to solve sampling and analysis issues on health and social science surveys.

Nov 15
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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).

Sep 23
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John E Marcotte

John E Marcotte

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John E. Marcotte, PhD is a statistician and data security expert. His research concerns data sharing, data security, data management, disclosure, health policy, nursing staffing and patient outcomes. He has over 25 years of experience implementing computing systems and performing quantitative analysis. During his career, Marcotte has served as a quantitative researcher, biostatistician, data archivist, data security officer and computing director. Among Marcotte’s statistical fortes are linear and logistic regression, survival analysis and sampling while his computing specialties include secure systems, high performance systems and numerical methods. He has collaborated with social and natural scientists as well as nurses and physicians. Marcotte regularly presents at professional conferences and contributes to invited panels on data security and disclosure. He has formal training in Demography, Statistics and Computer Science.

Research Data Security Options

Apr 15
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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.

Jun 24
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Amy M. Yorke

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Amy M. Yorke, PT, PhD, NCS, is Assistant Professor of Physical Therapy at the University of Michigan, Flint.

 

Mar 08
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Steven J. Katz

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Dr. Katz’s research addresses cancer treatment communication, decision-making, and quality of care. His work aims to examine the dynamics of how precision medicine presents itself in the exam room via provider and patient communication and shared decision-making. Dr. Katz leads the Cancer Surveillance and Outcomes Research Team (CanSORT), an interdisciplinary research program centered at the University of Michigan and focused on population and intervention studies of the quality of care and outcomes of cancer detection and treatment in diverse populations.  Dr. Katz and CanSORT have been collaborating with Surveillance, Epidemiology, and End Results (SEER) cancer registries since 2002 to study breast cancer treatment decision making at the population level. We obtain patient clinical and demographic information from SEER and combine this with surveys of patients and physicians to create comprehensive data sets that enable us to study testing and treatment trends and the challenges of individualizing treatments for breast cancer patients. In 2015 we added a new dimension to our research by partnering with evaluative testing firms to obtain tumor genomic and germline genetic test results for over 30,000 breast and ovarian cancer patients in the states of California and Georgia. We are also pursuing insurance claims data to assist with our analysis of physician network effects.

Steven Katz, MD discusses BRCA and multigene sequence testing at the labs of Ambry Genetics.

Steven Katz, MD discusses BRCA and multigene sequence testing at the labs of Ambry Genetics.