Alexandra Lee
Staff ML Scientist at Mulholland
Philadelphia, Pennsylvania, United States
Summary
👤
Senior🎓
Top SchoolAlexandra Lee is a Staff ML Scientist in Philadelphia with over a decade of computational research experience and 4+ years applying machine learning to biological problems. She specializes in unsupervised generative models for large-scale omics data, having developed influential tools like SOPHIE and the widely adopted ponyo package for simulating gene expression compendia. Her work spans end-to-end solutions—from engineering scalable ML workflows on UK Biobank multi-omic data to deploying NLP and LLM-driven tooling for product prioritization and user feedback analysis. Alexandra blends rigorous academic training (PhD in Genomics and Computational Biology, MS in Applied Mathematics) with practical product impact, enabling thousands of researchers through documentation, QA, and automated data-quality pipelines. Colleagues rely on her ability to surface subtle transcriptional signals that standard methods miss and to translate complex biological data into actionable insights.
10 years of coding experience
7 years of employment as a software developer
Master of Science (MS) Applied Mathematics, Master of Science (MS) Applied Mathematics at University of WashingtonBachelor of Arts (B.A.) Mathematics and Computer Science, Bachelor of Arts (B.A.) Mathematics and Computer Science at Bryn Mawr CollegeDoctor of Philosophy - PhD Genomics and Computational Biology, Doctor of Philosophy - PhD Genomics and Computational Biology at University of Pennsylvania
Github Skills (88)
virus10
variants10
lda10
immunology10
chat10
gene-expression10
genomics10
computational-biology10
variational-autoencoder10
autoencoder10
biology10
bioinformatics10
simulate9
genes9
javascript9
Programming languages (9)
Github contributions (5)
greenelab/ponyo
Jun 2020 - Jun 2022
Software to simulate compendium-wide gene expression data using a VAE.
Contributions:27 reviews, 35 commits, 51 PRs in 2 years
autoencoderdata-simulationcompendiumgene-expressionmachine-learning
ajlee21/core-accessory-interactome
Apr 2020 - Aug 2022
Investigating the functional relationship between P. aeruginosa core and accessory genes.
Contributions:485 pushes, 108 branches in 2 years 4 months
relationshipbioinformaticsgenesaccessory
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.