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Dr. Acar’s research program focuses on methodological computational developments in high-dimensional data analysis and integration, aiming to contribute novel multivariate modeling strategies that successfully account for the complexities of biomedical health applications. She deals with high-dimensional dependence modeling problems in data science, which involves analyzing multivariate data characterized by its complex nature and massive amounts of information. Dr. Acar's research is pivotal with advancements in computing and storage technologies that have enabled automated statistical analyses of large datasets. Her work emphasizes the development of multivariate modeling strategies that accurately capture and account for statistical dependencies in complicated data scenarios. Dr. Acar's specific research interests include flexible modeling of incomplete data types and the automation of multivariate analysis techniques. Her efforts extend to developing meta-analysis methods for synthesizing statistical evidence from high-throughput studies in interrelated fields such as genome-wide association studies, microbiome studies, and high-throughput phenotyping experiments. Dr. Acar is currently accepting postdoctoral fellows and PhD/MSc students, encouraging interested candidates to reach out with CV and cover letters.
University of Guelph • Guelph, ON, Canada
Teaching and conducting research in Statistical Modeling and related fields.
Department of Clinical Studies. Offers MSc by thesis (2 years) and MSc by coursework (1 year).