I develop methods for the analysis of complex high-dimensional data. These methods are usually motivated by applications in molecular biology, especially in "omics" fields such as genomics, metabolomics, and proteomics. My focus is in the integrated analysis of data that are from multiple sources (e.g., gene expression, metabolomics, imaging) or measured in multiple dimensions (e.g., multiple tissue types or body regions), which are often necessary to capture every facet of a complex biological system. I also have an interest in exploratory factorization and clustering methods, and Bayesian nonparametric inference.

I joined UMN in 2014, after getting my PhD in Statistics from the University of North Carolina and doing a postdoc at Duke University.

News:
New integrated data analysis method to improve cancer cell research and treatments (06/2019)
New methods will simplify biological data analysis (04/2019)
Developing Methods that Provide a More Comprehensive Analysis in Medical Studies (04/2019)
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