Jon Zelner

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Jon Zelner, PhD, is Assistant Professor in the department of Epidemiology in the University of Michigan School of Public Health. Dr. Zelner holds a second appointment in the Center for Social Epidemiology and Population Health.

Dr. Zelner’s┬áresearch is focused on using spatial analysis, social network analyisis and dynamic modeling to prevent infectious diseases, with a focus on tuberculosis and diarrheal disease. Jon is also interested in understanding how the social and biological causes of illness interact to generate observable patterns of disease in space and in social networks, across outcomes ranging from infection to mental illness.

 

A large spatial cluster of multi-drug resistant tuberculosis (MDR-TB) cases in Lima, Peru is highlighted in red. A key challenge in my work is understanding why these cases cluster in space: can social, spatial, and genetic data tell us where transmission is occurring and how to interrupt it?

A large spatial cluster of multi-drug resistant tuberculosis (MDR-TB) cases in Lima, Peru is highlighted in red. A key challenge in my work is understanding why these cases cluster in space: can social, spatial, and genetic data tell us where transmission is occurring and how to interrupt it?

 

 

Johann Gagnon-Bartsch

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My research currently focuses on the analysis of high-throughput biological data as well as other types of high-dimensional data. More specifically, I am working with collaborators on developing methods that can be used when the data are corrupted by systematic measurement errors of unknown origin, or when the data suffer from the effects of unobserved confounders. For example, gene expression data suffer from both systematic measurement errors of unknown origin (due to uncontrolled variations in laboratory conditions) and the effects of unobserved confounders (such as whether a patient had just eaten before a tissue sample was taken). We are developing methodology that is able to correct for these systematic errors using “negative controls.” Negative controls are variables that (1) are known to have no true association with the biological signal of interest, and (2) are corrupted by the systematic errors, just like the variables that are of interest. The negative controls allow us to learn about the structure of the errors, so that we may then remove the errors from the other variables.

Microarray data from tissue samples taken from three different regions of the brain (anterior cingulate cortex, dorsolateral prefrontal cortex, and cerebellum) of ten individuals. The 30 tissue samples were separately analyzed in three different laboratories (UC Davis, UC Irvine, U of Michigan). The left plot shows the first two principal components of the data. The data cluster by laboratory, indicating that most of the variation in the data is systematic error that arises due to uncontrolled variation in laboratory conditions. The second plot shows the data after adjustment. The data now cluster by brain region (cortex vs. cerebellum). The data is from GEO (GSE2164).

Microarray data from tissue samples taken from three different regions of the brain (anterior cingulate cortex, dorsolateral prefrontal cortex, and cerebellum) of ten individuals. The 30 tissue samples were separately analyzed in three different laboratories (UC Davis, UC Irvine, U of Michigan). The left plot shows the first two principal components of the data. The data cluster by laboratory, indicating that most of the variation in the data is systematic error that arises due to uncontrolled variation in laboratory conditions. The second plot shows the data after adjustment. The data now cluster by brain region (cortex vs. cerebellum). The data is from GEO (GSE2164).

Fred Feinberg

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My research examines how people make choices in uncertain environments. The general focus is on using statistical models to explain complex decision patterns, particularly involving sequential choices among related items (e.g., brands in the same category) and dyads (e.g., people choosing one another in online dating), as well as a variety of applications to problems in the marketing domain (e.g., models relating advertising exposures to awareness and sales). The main methods used lie primarily in discrete choice models, ordinarily estimated using Bayesian methods, dynamic programming, and nonparametrics. I’m particularly interested in extending Bayesian analysis to very large databases, especially in terms of ‘fusing’ data sets with only partly overlapping covariates to enable strong statistical identification of models across them.

Applying Bayesian Methods to Problems in Dynamic Choice

Applying Bayesian Methods to Problems in Dynamic Choice