Over the last century, new technologies have brought about the study of large datasets in multiple disciplines, both research-based and in industry. In this research we consider large datasets that are common in biomedical research, typically these datasets contain observations of genes (genomics), mRNA (transcriptomics), proteins (proteomics) and metabolites (metabolomics) and collectively are known as ‘Omics’ datasets. My research is in the development of feature selection methods for large structured data and the application of these methods for Omics datasets.
Research Interests:
Bayesian computational algorithms
Convex Optimization
High-dimensional data analysis
Nonconvex Optimization
Omics datasets
Operations Research
Penalized Likelihood Methods
Variable selection
Qualifications:
Bachelor of Science / Arts (Mathematics and Statistics)
In this project we consider the challenging task of developing fully Bayesian sparse analyses for the situations when the numbers of predictors is larger than observations for complex responses and covariates grouped by blocks with the sparsity for blocks and cases.
