Nicholas Bernstein is a Principal Data Scientist based in the San Francisco Bay Area with 11 years of experience applying genetics, statistics, and software engineering to deliver clinically actionable insights. He builds and scales bioinformatics methods and web apps for diagnostics and research, having led deep learning solutions for doublet detection in scRNA-seq and multimodal meta-analyses across UKBB, TCGA, GTEx and other large consortia. Nicholas has driven scientific OKRs and partnered closely with wet labs to design experiments whose results are integrated into production-ready ML models. An active contributor to scvi-tools, he improved the SOLO model’s training and doublet-scoring capabilities, reflecting a blend of open-source rigor and production focus. He brings a rare mix of hands-on algorithm development, pipeline automation for CLIA assays, and cross-disciplinary leadership that accelerates translation from data to patient-facing content.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Arts (B.A.) Neuroscience with minors in Mathematics and Chemistry, Bachelor of Arts (B.A.) Neuroscience with minors in Mathematics and Chemistry at Colorado College
Deep probabilistic analysis of single-cell and spatial omics data
Role in this project:
ML Engineer
Contributions:12 reviews, 19 commits, 4 PRs in 3 months
Contributions summary:Nicholas primarily contributed to the SOLO model, adding features such as the ability to get scores for simulated doublets and modifying the handling of doublet ratios. They also implemented early stopping in the SOLO model, and incorporated changes related to training plans. Furthermore, the user made code formatting adjustments and incorporated updates from another repository.
Contributions:4 releases, 15 reviews, 169 commits in 2 years 9 months
detectdoubletspython
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