Paxton Fitzpatrick
PhD Candidate
Lebanon, New Hampshire, United States
Summary
🤩
Rockstar🎓
Top SchoolPaxton Fitzpatrick is a PhD candidate in cognitive neuroscience at Dartmouth who builds computational models of human learning and memory and develops software tools to make research and education more reproducible and accessible. With nine years of research and lab-management experience at Dartmouth’s Contextual Dynamics Lab and Brain Imaging Center, he bridges experimental neuroscience and production-ready code. He is an active open-source contributor and has improved core tooling in high-profile projects like UMAP, tackling build, CI, and custom metric issues to make ML tooling more robust. Paxton focuses on practical technologies to improve classroom and online learning through personalized approaches, and he brings an uncommon combination of hands-on neuroimaging experience and MLOps-style engineering to education research.
9 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Cognitive Neuroscience, Doctor of Philosophy - PhD, Cognitive Neuroscience at Dartmouth College
Stackoverflow
545reputation
31kreached
22answers
1question
Github Skills (29)
data-visualizations10
integrate10
python10
dimensionality-reduction10
imap10
machine-learning10
data-visualisation10
integrations10
cicd10
numpy10
system-integration10
omap10
data-visualization10
data-integration10
package-management9
Programming languages (6)
Github contributions (5)
ContextLab/hypertools
Dec 2019 - Feb 2022
A Python toolbox for gaining geometric insights into high-dimensional data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 releases, 4 reviews, 111 commits in 2 years 2 months
Contributions summary:Paxton primarily contributed to the `hypertools` project by updating the `reduce.py` file to handle custom model parameters, adding checks and fixing order-of-checks issues. They also updated a depreciated method in the `df2mat.py` file. Furthermore, they incorporated upstream changes and merged upstream changes. These commits suggest a focus on improving existing functionalities and integrating updates within the project's code.
high-dimensional-datapythontoolboxdimensionalpython-toolbox
lmcinnes/umap
Dec 2019 - Mar 2020
Uniform Manifold Approximation and Projection
Role in this project:Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:44 commits, 2 PRs, 13 comments in 2 months
Contributions summary:Paxton primarily focused on improving the project's build process and addressing compatibility issues. Their contributions included fixing encoding issues in the setup script to enable proper package installation and modifying the CI scripts to handle Python 3.x environments. They also worked on integrating and utilizing black for code formatting and added functionality for handling custom distance metrics and related exceptions within the core UMAP library, further contributing to overall usability.
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.