Nigel Delaney
Senior Director, Computational Biology
San Francisco, California, United States
Summary
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Senior🎓
Top SchoolNigel Delaney is a senior computational biology leader with 12+ years of experience translating statistical inference and machine learning into production-ready biology software and teams. As Senior Director at 10x Genomics he has led cross-disciplinary groups solving problems from molecules to whole organisms, pairing a PhD in evolutionary genetics and an MA in statistics with a pragmatic software engineering mindset. Nigel has a track record of shipping performant, widely used tools—evidenced by his backend and performance work on the high-profile Seurat single-cell toolkit—where he optimized memory and speed-critical routines. He thrives on mentoring and scaling technical teams, taking personal satisfaction in building durable code, reproducible analyses, and operational practices. Unusually for a leader at his level, he combines hands-on low-level optimization skills with a broad, multi-omic perspective spanning DNA, RNA, proteins and metabolites.
12 years of coding experience
15 years of employment as a software developer
University of California, San DiegoMarine Biological LabsMaster of Arts (MA), Statistics, Master of Arts (MA), Statistics at Harvard University
Github Skills (8)
single-cell-genomics10
r-programming10
cpp-programming-language10
performance-optimization10
rcpp9
biom8
bioinformatics8
data-manipulation8
Programming languages (11)
Github contributions (5)
satijalab/seurat
May 2018 - Jan 2020
R toolkit for single cell genomics
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:22 commits, 13 PRs, 37 comments in 1 year 8 months
Contributions summary:Nigel primarily focused on optimizing the `RegressOutResid` function within the Seurat toolkit, targeting memory usage and performance. They implemented several code changes to reduce memory allocations and reuse existing components, leading to significant performance improvements. These optimizations included reusing QR decompositions, avoiding closures, and creating/reusing empty vectors. Additionally, the user addressed inefficiencies in the `Standardize` and `SampleUMI` functions, further contributing to the overall efficiency of the library.
single-cell-genomicshuman-cell-atlassingle-cell-rna-seqcran
evolvedmicrobe/Bio.VCF
Mar 2014 - Sep 2015
Contributions:2 commits, 1 push, 1 comment in 1 year 6 months
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