John Healy
Research Mathematician And Data Scientist
Ottawa, Ontario, Canada
Summary
🤩
RockstarJohn Healy is a research mathematician and data scientist with over two decades of experience translating foundational mathematical problems into practical machine learning solutions. Based in Ottawa, he specializes in unsupervised learning—clustering, outlier detection, and dimension reduction—and has contributed to high-profile open-source projects including HDBSCAN and UMAP. He combines rigorous algorithm design with hands-on engineering, implementing robust features (like duplicate-handling in UMAP) and improving cluster stability metrics in HDBSCAN. His career spans government research, startup AutoML work, and current applied research at the Tutte Institute, where he closes the loop by deploying methods and guiding clients on their use. Comfortable with both theory and production code, he often focuses on the mathematical core of problems that unlock broadly applicable solutions.
11 years of coding experience
6 years of employment as a software developer
Github Skills (18)
scipy10
python10
dimensionality-reduction10
imap10
scikit10
sparse-matrix10
machine-learning10
machine-learning-algorithms10
numpy10
omap10
scikit-learn10
clustering10
data-science9
testing9
pandas9
Programming languages (4)
Github contributions (5)
scikit-learn-contrib/hdbscan
Apr 2015 - Jan 2022
A high performance implementation of HDBSCAN clustering.
Role in this project:
Data Scientist
Data ScientistContributions:14 commits, 3 PRs, 8 pushes in 6 years 9 months
Contributions summary:John contributed code related to cluster analysis and cluster stability, specifically within the context of an HDBSCAN implementation. The commits involved modifying and extending code related to the Condense Tree structure and related stability calculations. They also worked on flattening the tree and calculating stability metrics, indicating a focus on improving the analysis capabilities of the clustering algorithm.
clustering-evaluationk-meansmachine-learning-algorithmsmachine-learningclustering
lmcinnes/umap
Aug 2019 - Oct 2021
Uniform Manifold Approximation and Projection
Role in this project:ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:11 commits, 6 PRs, 28 comments in 2 years 2 months
Contributions summary:John implemented and tested the "unique" functionality for the UMAP algorithm, enhancing its ability to handle duplicate data points and improving the robustness of the embedding process. This involved modifying the `fit` function to identify and handle unique rows in both dense and sparse matrices, ensuring correct behavior and preventing potential issues. The changes also included adding unit tests to verify the functionality across various data types and scenarios, demonstrating a focus on code quality and reliability. They also corrected and clarified the `target_weight` docstring in `UMAP` class.
projectiondimensionality-reductionmachine-learningtopological-data-analysisapproximation
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.