Michael I. Love, PhD

Assistant Professor, Biostatistics

Research Interests

Keywords: Statistical modeling of genomic data, high-throughput sequencing, RNA sequencing (RNA-seq), empirical Bayes methods

Dr. Love’s research concerns statistical and computational methods for the analysis of high-throughput sequencing assays to facilitate biomedical and biological research. He has developed a number of open source software packages for the analysis of RNA sequencing (RNA-seq) data, including the DESeq2 package for differential gene expression analysis. In addition, he studies the effect of lab-to-lab variation on computational estimation of gene isoform abundances from RNA-seq, and has developed statistical methods for accurate estimation of isoform abundance in the presence of common technical biases.