Bhramar Mukherjee is a Professor in the Department of Biostatistics, joining the department in Fall, 2006. Bhramar is also a Professor in the Department of Epidemiology. Bhramar completed her Ph.D. in 2001 from Purdue University. Bhramar’s principal research interests lie in Bayesian methods in epidemiology and studies of gene-environment interaction. She is also interested in modeling missingness in exposure, categorical data models, Bayesian nonparametrics, and the general area of statistical inference under outcome/exposure dependent sampling schemes. Bhramar’s methodological research is funded by NSF and NIH. Bhramar is involved as a co-investigator in several R01s led by faculty in Internal Medicine, Epidemiology and Environment Health sciences at UM. Her collaborative interests focus on genetic and environmental epidemiology, ranging from investigating the genetic architecture of colorectal cancer in relation to environmental exposures to studies of air pollution on pediatric Asthma events in Detroit. She is actively engaged in Global Health Research.
Kevin Dombkowski, DrPH, is Research Associate Professor in the department of Pediatrics, Medical School, and holds a secondary appointment in the School of Public Health at the University of Michigan, Ann Arbor.
Kevin’s primary research focus is conducting population-based interventions aimed at improving the health of children, especially those with chronic conditions. Much of his work has focused on evaluating the feasibility and accuracy of using administrative claims data to identify children with chronic conditions by linking these data with clinical and public health systems. Many of these projects have linked claims, immunization registries, newborn screening, birth records and death records to conduct population-based evaluations of health services. He has also applied these approaches to assess the statewide prevalence of chronic conditions such as asthma, sickle cell disease, and inflammatory bowel disease in Michigan as well as other states.
Further, his research interests also include registry-based interventions to improve the timeliness of vaccinations through automated reminder and recall systems. He has led numerous collaborations with the Michigan Department of Health and Human Services, including several CDC-funded initiatives using the Michigan Care Improvement Registry (MCIR). Through this collaboration, Kevin tested a statewide intervention aimed at increasing influenza vaccination among children with chronic conditions during the 2009 influenza pandemic.
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 research 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.
Naisyin Wang, PhD, is Professor of Statistics, College of Literature, Science, and the Arts, at the University of Michigan, Ann Arbor.
Prof. Wang’s main research interests involve developing models and methodologies for complex biomedical data. She has developed approaches in information extraction from imperfect data due to measurement errors and incompleteness. Her other methodology developments include model-based mixture modeling, non- and semiparametric modeling of longitudinal, dynamic and high dimensional data. She developed approaches that first gauge the effects of measurement errors on non-linear mixed effects models and provided statistical methods to analyze such data. Most methods she has developed are so called semi-parametric based. One strength of such approaches is that one does not need to make certain structure assumptions about part of the model. This modeling strategy enables data integration from measurements collected from sources that might not be completely homogeneous. Her recently developed statistical methods focus on regularized approach and model building, selection and evaluation for high dimensional, dynamic or functional data.