diff --git a/content/03.pilist.md b/content/03.pilist.md
index 2e2c9a8..5b9bda5 100644
--- a/content/03.pilist.md
+++ b/content/03.pilist.md
@@ -66,4 +66,4 @@
 4. Casey Greene
 5. Tom Hampton is Director of Bioinformatic Training for two program projects at the Geisel School of Medicine at Dartmouth. In that role, he has a long collaboration with co-PI Casey Greene, with whom he has collaborated in the development of short courses taught at Mount Desert Island Biological Laboratory and at Dartmouth. Dr Hampton’s  bioinformatic research is focused on using data from multiple independent studies to identify concordant patterns of gene express in response to stressors such as infection and environmental stress.
 6. Michael Love is an Assistant Professor of Biostatistics and Genetics at the University of North Carolina at Chapel Hill. He is a leading developer of statistical software for RNA-seq analysis in the Bioconductor Project, maintaining the widely used DESeq2 [@doi:10.1186/s13059-014-0550-8] and tximport [@doi:10.12688/f1000research.7563.1] packages. He is a close collaborator with Dr. Rob Patro on bias-aware estimation of transcript abundance from RNA-seq and estimation of uncertainty during transcript quantification [@doi:10.1038/nmeth.4197]. Dr. Love will work with co-PIs to disseminate versioned reference cell type catalogs through widely used frameworks for genomic data analysis including R/Bioconductor and Python.
-7. Rob Patro is an Assistant Professor of Computer Science at Stony Brook University.  He leads the COMBINE-lab, that [develops and maintains numerous open-source genomics tools and methods](https://github.com/COMBINE-lab).  He is the primary developer of the popular transcript quantification tools Sailfish [@doi:10.1038/nbt.2862] and Salmon [@doi:10.1038/nmeth.4197], having collaborated closely with Dr. Love on the latter. He and Dr. Love are actively collaborating on improved methods for transcript quantification, differential testing, and also on reproducible analysis via [tximeta](https://github.com/mikelove/tximeta). He has recently been focused on developing improved methods for gene-level quantification from tagged-end single-cell RNA-seq data, as implemented in the alevin tool [@doi:10.1101/335000]. Dr. Patro will worth with co-PIs to develop improved single-cell quantification tools that can account for gene-ambiguous reads and provide uncertainty information about the quantification estimates (base enabling technologies) --- which is important for accurate and robust creation of reduced-dimensionality latent spaces.  He will also work with the co-PIs to develop efficient algorithms and data structures, and robust implementations, to enable efficient expression search over low-dimensional representations of HCA data (Aim 1).
+7. Rob Patro is an Assistant Professor of Computer Science at Stony Brook University.  He leads the COMBINE-lab, that [develops and maintains numerous open-source genomics tools and methods](https://github.com/COMBINE-lab).  He is the primary developer of the popular transcript quantification tools Sailfish [@doi:10.1038/nbt.2862] and Salmon [@doi:10.1038/nmeth.4197], having collaborated closely with Dr. Love on the latter.  Dr. Love and he are actively collaborating on improved methods for transcript quantification, differential testing, and also on reproducible analysis via [tximeta](https://github.com/mikelove/tximeta) [@doi:10.18129/B9.bioc.tximeta]. He has recently focused on developing improved methods for gene-level quantification from tagged-end single-cell RNA-seq data, as implemented in the tool alevin [@doi:10.1101/335000]. He will work with co-PIs to develop improved single-cell quantification tools that account for gene-ambiguous reads and provide quantification uncertainty estimates (base enabling technologies) --- which is important for accurate and robust creation of reduced-dimensionality representations.  He will work with the co-PIs to develop efficient algorithms and data structures to enable efficient expression and sample search over low-dimensional representations of HCA data (Aim 1).