Thuy Le

Thuy Le

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Dr. Le is an assistant research scientist at the University of Michigan Department of Health Management and Policy. Dr Le is also a member of the UM/Georgetown TCORS Center for the Assessment of Tobacco Regulations (CAsToR). Dr. Le is interested in mathematical modeling for cancer- and tobacco-related problems, and machine-learning applications in tobacco regulatory science. Dr. Le has developed mathematical models to evaluate the benefits and harms of breast cancer mammography and predict the number of white blood cells during acute lymphoblastic maintenance therapy in children. Dr. Le’s recent work focuses on employing mathematical models to quantify the burden of menthol cigarettes on public health and estimate the smoking cessation rate. Dr. Le is working on applying machine learning techniques to predict and understand smoking behaviors.

Tanya Rosenblat

Tanya Rosenblat

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My main research interest lies in experimental economics, social networks and social learning. I am particularly interested in how people aggregate information from social networks and news sources and form posterior beliefs. I use regression techniques to uncover causal relationships as well as classification to reduce the dimensionality of data.

Some of my recent research looks at how people update beliefs when they derive direct utility from beliefs. This occurs, for example, when people receive feedback on their ability. They often seem to weigh positive information more strongly than negative information. I am also interested in understanding differences between statistical and anecdotal reasoning. Under statistical reasoning, people have known objectives and they update beliefs through Bayes’ rule. Under anecdotal reasoning, people recall anecdotes that are relevant for forming a belief about a new objective that has not been encountered before. In these situations, memory recall and recognition are important to understand the formation of beliefs.

Mean absolute belief revisions by prior belief in response to positive/negative information. Prior deciles are ordered in increasing (decreasing) order for positive (negative) information. Bayesian should have equal responses.

Sabine Loos

Sabine Loos

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My research focuses on natural hazards and disaster information, everything from understanding where disaster data comes from, how it’s used, and its implications to design improved disaster information systems that prioritize the human experience and lead to more effective and equitable outcomes.

My lab takes a user-centered and data-driven approach. We aim to understand user needs and the effect of data on users’ decisions through qualitative research, such as focus groups or workshops. We then design new information systems through geospatial/GIS analysis, risk analysis, and statistical modeling techniques. We often work with earth observation, sensor, and survey data. We consider various aspects of disaster information, whether it be the hazard, its physical impacts, its social impacts, or a combination of the three.

I also focus on the communication of information, through data visualization techniques, and host a Risk and Resilience DAT/Artathon to build data visualization capacity for early career professionals.

Geospatial model for predicting inequities in recovery from the 2015 Nepal earthquake

Xiaoquan William Wen

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Xiaoquan (William) Wen is an Associate Professor of Biostatistics. He received his PhD in Statistics from the University of Chicago in 2011 and joined the faculty at the University of Michigan in the same year. His research centers on developing Bayesian and computational statistical methods to answer interesting scientific questions arising from genetics and genomics.

In the applied field,  he is  particularly interested in seeking statistically sound and computationally efficient solutions to scientific problems in the areas of genetics and functional genomics.
Quantifying tissue-specific expression quantitative trait loci (eQTLs) via Bayesian model comparison

Quantifying tissue-specific expression quantitative trait loci (eQTLs) via Bayesian model comparison

Cong Shi

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Cong Shi is an associate professor in the Department of Industrial and Operations Engineering at the University of Michigan College of Engineering. His primary research interest lies in developing efficient and provably-good data-driven algorithms for operations management models, including supply chain management, revenue management, service operations, and human-robot interactions. He received his Ph.D. in Operations Research at MIT in 2012, and his B.S. in Mathematics from the National University of Singapore in 2007.

Olga Yakusheva

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My research interests are in health economics and health services research; specifically econometric methods for causal inference, data architecture, and secondary analyses of big data. My primary focus is the study the work of nurses. I led the development of a new method for outcomes-based clinician performance productivity measurement using the electronic medical records. With this work, I was able to measure, for the first time, the value-added contributions of individual nurses to patient outcomes. This work has won her national recognition earning her the Best of AcademyHealth Research Meeting Award in 2014. I am is currently working to uncover traits and success strategies of highly-effective nurses, including education, experience, and expertise—and most recently smart clinician staffing approaches and innovation in the healthcare setting. I am a team scientist and contributed methodological expertise to many interdisciplinary projects including hospital readmissions, primary care providers, obesity, pregnancy and birth, and peer effects on health behaviors and outcomes. I am the Director of the Healthcare Innovation and Impact Program (HiiP) at the School of Nursing.

Using big data analytics to measure value-added contributions of nurses

Elizabeth F. S. Roberts

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“Neighborhood Environments as Socio-Techno-bio Systems: Water Quality, Public Trust, and Health in Mexico City (NESTSMX)” is an NSF-funded multi-year collaborative interdisciplinary project that brings together experts in environmental engineering, anthropology, and environmental health from the University of Michigan and the Instituto Nacional de Salud Pública. The PI is Elizabeth Roberts (anthropology), and the co-PIs are Brisa N. Sánchez (biostatistics), Martha M Téllez-Rojo (public health), Branko Kerkez (environmental engineering), and Krista Rule Wigginton (civil and environmental engineering). Our overarching goal for NESTSMX is to develop methods for understanding neighborhoods as “socio-techno-bio systems” and to understand how these systems relate to people’s trust in (or distrust of) their water. In the process, we will collectively contribute to our respective fields of study while we learn how to merge efforts from different disciplinary backgrounds.
NESTSMX works with families living in Mexico City, that participate in an ongoing longitudinal birth-cohort chemical-exposure study (ELEMENT (Early Life Exposures in Mexico to ENvironmental Toxicants, U-M School of Public Health). Our research involves ethnography and environmental engineering fieldwork which we will combine with biomarker data previously gathered by ELEMENT. Our focus will be on the infrastructures and social structures that move water in and out of neighborhoods, households, and bodies.

Testing Real-Time Domestic Water Sensors in Mexico City

Testing Real-Time Domestic Water Sensors in Mexico City

Fabian Pfeffer

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My research investigates social inequality and its maintenance across time and generations. Current projects focus on wealth inequality and its consequences for the next generation, the institutional context of social mobility processes and educational inequality in the United States and other industrialized countries. I also help expand the social science data infrastructure and quantitative methods needed to address questions on inequality and mobility. I serve as Principal Investigator of the Wealth and Mobility (WAM) study as well as Co-Investigator of the Panel Study of Income Dynamics (PSID). As such, my research draws on and helps construct nationally representative survey data as well as full-population administrative data. My methodological work has been focused on causal inference, multiple imputation, and measurement error.

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.

Meha Jain

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​I am an Assistant Professor in the School for Environment and Sustainability at the University of Michigan and am part of the Sustainable Food Systems Initiative. My research examines the impacts of environmental change on agricultural production, and how farmers may adapt to reduce negative impacts. I also examine ways that we can sustainably enhance agricultural production. To do this work, I combine remote sensing and geospatial analyses with household-level and census datasets to examine farmer decision-making and agricultural production across large spatial and temporal scales.

Conducting wheat crop cuts to measure yield in India, which we use to train algorithms that map yield using satellite data