ÐÓ°ÉÔ­´´

Sanjeena Dang

Associate Professor

• B.Sc. (Biological Sciences, Guelph), M.Sc., Ph.D.(Statistics, Guelph)
• Email Sanjeena Dang
• 613-520-2600 ext 1014

• Visit the Website

Dr. Dang is an Associate Professor and Canada Research Chair in Data Science and Analytics at the School of Mathematics and Statistics (100% appointment) and the Department of Biology (0% appointment) at ÐÓ°ÉÔ­´´ University. She was the recipient of the 2019 Chikio Hayashi Award by the International Federation of the Classification Societies, an award that recognizes promising young researchers in the field of clustering and classification. Dr. Dang’s undergraduate training was in biological sciences followed by graduate and postdoctoral training in Statistics. Her research focuses on clustering and classification of high dimensional data with applications in bioinformatics, specifically developing novel clustering models for complex biological datasets such as RNA-seq data, microbiome data, and microarray data. Her research is currently supported by NSERC Discovery Grant and the Canada Research Chair Program.

Select Publications:

•  Tu, W. and Subedi, S., `A family of mixture models for biclustering’. Statistical Analysis and Data mining. To Appear.
•  Subedi, S. and McNicholas, P. D. (2021), `A variational approximations-DIC rubric for parameter estimation and mixture model selection within a family setting. Journal of Classification. 38 89–108
• Subedi, S. and Browne, R. P. (2020) A parsimonious family of multivariate Poisson-lognormal distributions for clustering multivariate count data. Stat. 9 (1) e310
• Subedi, S., Neish, D., Bak, S., and Feng, Z. (2020)`Cluster analysis of microbiome data via mixtures of Dirichlet-multinomial regression models’. Journal of Royal Statistical Society: Series C. 69 (5) 1163-1187
• Silva, A., Rothstein, S. J., McNicholas, P. D. Subedi, S. (2019) A Multivariate Poisson-Log Normal Mixture Model for Clustering Transcriptome Sequencing Data. BMC Bioinformatics. 20(1) 394.
•  Dang, S. and Vialaneix, N. (2018) Cutting Edge Bioinformatics and Biostatistics Approaches Are Bringing Precision Medicine and Nutrition to a New Era. Lifestyle Genomics 1–4.