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I develop, analyze, and apply statistical methods for genetics, genomics, and other high-dimensional biomedical data. Primarily, I use linear algebra tools to address statistical and computational hurdles that prevent important scientific studies.

As a postdoc at UCSF I focused on studying phenotypic heterogeneity, including developing RGWAS for subtyping, GxEMM for heritability due to genetic interactions, and mixed models to partition variance in single cell data. I focused on multi-trait mixed models in my PhD at Oxford, including (penalized) MPMMs for heritability and genetic correlation estimation and phenix for phenotype imputation. I worked on a variant of STRUCTURE in undergrad at UChicago. I have also done theoretical work on heritability estimation.

Contact

andywdahl@gmail.com