Andrew Tritt
Data Engineer
Oakland, California, United States
Summary
👤
Senior🎓
Top SchoolAndrew Tritt is a data engineer with 16 years of technical experience and over a decade specializing in genomic sequence analysis, now applying that domain expertise to scalable data systems at Berkeley Lab. He leads architecture for Neurodata Without Borders and serves as machine learning lead on ExaBiome, blending deep learning with biomedical informatics to turn messy clinical and microbiome sequencing data into production-ready models and pipelines. Earlier roles at Joint Genome Institute and UC Davis show a track record of automating high-throughput sequencing workflows, boosting throughput and reliability for multi-terabyte archives and metatranscriptome projects. A contributor to the popular UMAP project, he has improved embedding documentation and fixed nuanced metric and code-quality issues, reflecting both practical data-science instincts and attention to software engineering detail. Based in Oakland, he describes himself as a bioinformatician-turned-data-engineer who is "better at writing code than a statistician and better at analyzing data than a software engineer," a concise distillation of his hybrid skill set.
16 years of coding experience
Bachelors, Genetics, Computer Science, Mathematics, Bachelors, Genetics, Computer Science, Mathematics at University of Wisconsin-Madison
English
Github Skills (8)
machine-learning10
python10
omap10
imap10
numpy9
matplotlib8
scikit7
scikit-learn7
Programming languages (6)
Github contributions (5)
lmcinnes/umap
Mar 2020 - Mar 2020
Uniform Manifold Approximation and Projection
Role in this project:
Data Scientist
Data ScientistContributions:9 commits, 3 PRs, 2 comments in 1 day
Contributions summary:Andrew primarily contributed to the `umap` repository by addressing bugs and improving code quality. Their work involved fixing precomputed metric issues, resolving pep8 errors, and cleaning up the code. They also integrated documentation improvements related to embedding spaces, specifically spherical and torus embeddings, demonstrating an understanding of UMAP's capabilities.
projectiondimensionality-reductionmachine-learningtopological-data-analysisapproximation
ajtritt/ajtritt
Nov 2017 - Mar 2024
A sandbox repository
Contributions:2 PRs, 53 pushes, 2 branches in 6 years 4 months
netboxsandbox
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