Trivellore E. Raghunathan

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Dr.¬†Raghunathan’s primary research interest is in developing methods for dealing with missing data in sample surveys and in epidemiological studies. The methods are motivated from a Bayesian perspective but with desirable frequency or repeated sampling properties. The analysis of incomplete data from practical sample surveys poses additional problems due to extensive stratification, clustering of units and unequal probabilities of selection. The model-based approach provides a framework to incorporate all the relevant sampling design features in dealing with unit and item nonresponse in sample surveys. There are important computational challenges in implementing these methods in practical surveys. He has developed SAS based software, IVEware, for performing multiple imputation analysis and the analysis of complex survey data. Raghunathan’s other research interests include Bayesian methods, methods for small area estimation, combining information from multiple surveys, measurement error models, longitudinal data analysis, privacy, confidentiality and disclosure limitations and statistical methods for epidemiological studies. His applied interests include cardiovascular epidemiology, social epidemiology, health disparity, health care utilization, and social and economic sciences. Raghunathan is also involved in the Survey Methodology Program at the Institute for Social Research, a multidisciplinary team of sociologists, statisticians and psychologists, provides an opportunity to address methodological issues in: nonresponse, interviewer behavior and its impact on the results, response or measurement bias and errors, noncoverage, respondent cognition, privacy and confidentiality issues and data archiving. The Survey Methodology Program has a graduate program offering masters and doctoral degrees in survey methodology.

Ivo D. Dinov

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Dr. Ivo Dinov directs the Statistics Online Computational Resource (SOCR), co-directs the multi-institutional Probability Distributome Project, and is an associate director for education of the Michigan Institute for Data Science (MIDAS).

Dr. Dinov is an expert in mathematical modeling, statistical analysis, computational processing and visualization of Big Data. He is involved in longitudinal morphometric studies of human development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s and Parkinson’s diseases). Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for scientific education and active learning.

Analyzing Big observational data including thousands of Parkinson's disease patients based on tens-of-thousands signature biomarkers derived from multi-source imaging, genetics, clinical, physiologic, phenomics and demographic data elements is challenging. We are developing Big Data representation strategies, implementing efficient algorithms and introducing software tools for managing, analyzing, modeling and visualizing large, complex, incongruent and heterogeneous data. Such service-oriented platforms and methodological advances enable Big Data Discovery Science and present existing opportunities for learners, educators, researchers, practitioners and policy makers.

Analyzing Big observational data including thousands of Parkinson’s disease patients based on tens-of-thousands signature biomarkers derived from multi-source imaging, genetics, clinical, physiologic, phenomics and demographic data elements is challenging. We are developing Big Data representation strategies, implementing efficient algorithms and introducing software tools for managing, analyzing, modeling and visualizing large, complex, incongruent and heterogeneous data. Such service-oriented platforms and methodological advances enable Big Data Discovery Science and present existing opportunities for learners, educators, researchers, practitioners and policy makers.