Nicole White is a Principal Software Engineer with 12 years of experience building reliable, production-grade systems across startups and enterprise teams, now based in Washington, D.C. She progressed from data science roles at Neo4j and Infer to senior engineering and founding-engineer responsibilities at ClearMetal/project44 and Autoblocks, and now leads engineering work at Microsoft. Her background in data science and machine learning (MS, UT Austin) informs pragmatic choices around model explainability and testability, reflected in contributions to LIME and Keras where she improved usability and robustness. She’s also an active open-source back-end contributor, enhancing algebraic equation solving (including cubic solutions) in algebra.js and refining core evaluation logic. Known for blending deep technical rigor with product-minded delivery, she routinely converts complex algorithms into maintainable, well-tested code. Colleagues rely on her to bridge data science, QA, and backend engineering to ship dependable systems at scale.
12 years of coding experience
10 years of employment as a software developer
Master's Degree, Data Science & Machine Learning, Master's Degree, Data Science & Machine Learning at The University of Texas at Austin
Bachelor's Degree, Economics & Mathematics, Bachelor's Degree, Economics & Mathematics at Louisiana State University
Contributions:4 releases, 115 commits, 19 PRs in 1 year 10 months
Contributions summary:Nicole focused on implementing and refining algebraic equation-solving capabilities within the `algebra.js` library. Their work involved significant changes to the core `expressions.js`, `terms.js`, and `equations.js` files, implementing expression and term sorting algorithms. Additionally, they refactored and improved the evaluation process of expressions, enabling the handling of various inputs, including other expressions. The user also added the ability to solve cubic equations with real solutions.
Lime: Explaining the predictions of any machine learning classifier
Role in this project:
Data Scientist
Contributions:7 commits, 5 PRs, 3 comments in 23 days
Contributions summary:Nicole primarily focused on improving the `lime` library, which is designed to explain the predictions of machine learning models. Their contributions involved enhancing the usability and functionality of the library, specifically in relation to feature names, categorical features, and feature values. They also addressed code quality by fixing flake8 issues and handling potential issues with division in Python 2.7. Their work demonstrates a focus on making the library more flexible and robust for various data types and use cases.
classifiermachine-learning
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