Ethan Armand is a bioinformatics PhD student at UC San Diego's Ren lab with nine years of experience developing computational methods for single-cell epigenomic, transcriptomic, and multi-omic data. Prior roles in the Mukamel Lab included building analysis pipelines and custom algorithms to extract cell-type specific insights from complex sequencing datasets. With a BS in Cognitive Science focused on machine learning and neural computation, he blends machine learning intuition with hands-on genomic data engineering. Based in San Diego, he contributes to the mlpc-ucsd community on GitHub and is active in academic publishing, bringing a research-forward approach to reproducible, scalable bioinformatics.
Contributions:12 pushes, 1 branch in 1 year 3 months
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