香蕉视频

Sanjeena Dang, PhD

Assistant Professor, Department of Mathematics and Statistics, University of Guelph

New developments in model-based clustering and applications to biological data

ALL ARE WELCOME

Abstract:

With advances in high throughput technologies, massive amounts of data can be generated in an increasingly shorter period of time. Challenges and approaches to dealing with high-dimensional data will be discussed in a model-based clustering context. The talk will provide an overview of different frameworks for clustering increasingly complex biological data using both mixtures of Gaussian distributions and mixtures of non-Gaussian distributions. A number of families of mixture models will be considered along with several parameter estimation techniques and principles including the EM algorithm, the MM algorithm, and the variational Bayes algorithm. The talk will conclude with a discussion of future trends.

Bio:

Dr. Dang is Assistant Professor at the Department of Mathematics and Statistics at the University of Guelph. She completed her Ph.D. in Statistics at the University of Guelph in 2012 and received her masters in Statistics from University of Guelph in 2009. She did her undergraduate in Biological Sciences with courses and research focusing on Molecular Biology, Genetics, and Statistics at the University of Guelph in 2008.聽 Dr. Dang鈥檚 current research focuses on clustering and classification of high dimensional data with applications in bioinformatics, specifically 香蕉视频-seq data, microbiome data, and microarray data. Her research is currently supported by NSERC Discovery Grant and University of Guelph start-up research support. Her Ph.D. research was supported through a Post Graduate Scholarship (PGS-D) from the Natural Sciences and Engineering Council of Canada.

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