Joseph Burns
Software Engineer at Cloudflare
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolJoseph Burns is a software engineer with 12 years of professional experience who blends a creative background in writing with an MS in Computer Science from Northeastern to build data-driven systems and visualizations. He has transitioned from research and teaching roles—developing datasets and bias-analysis experiments at Northeastern’s IoT lab and TAing networking and software engineering courses—to production engineering roles at Mercari US and now Cloudflare. His open-source contributions include integrating the Families in the Wild dataset into the FiftyOne computer vision platform, highlighting a practical focus on dataset curation and kinship/vision analysis. Comfortable across data engineering, ML-infused workflows, and product engineering, he also ships creative side projects—a published novel, music albums, and an indie Unity game—that reflect a rare combination of technical rigor and storytelling sensibility.
12 years of coding experience
5 years of employment as a software developer
Masters of Science in Computer Science Computer Science, Masters of Science in Computer Science Computer Science at Northeastern UniversityUniversity of Connecticut
Github Skills (8)
computer-vision10
machine-learning10
python10
data-science10
image-classification9
unstructured-data8
data-cleaning8
active-learning6
Programming languages (9)
Github contributions (5)
voxel51/fiftyone
Oct 2021 - Nov 2021
Refine high-quality datasets and visual AI models
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:13 commits, 1 PR, 4 comments in 1 month
Contributions summary:Joseph primarily worked on integrating the "Families in the Wild" (FIW) dataset into the FiftyOne platform. Their contributions involved modifying the `fiftyone/utils/fiw.py` and `fiftyone/zoo/datasets/base.py` files to support dataset loading, metadata parsing, and relationship analysis. They also updated the documentation and user guide to include information about the FIW dataset. The changes focused on preparing the data for use within the FiftyOne framework, enhancing its utility for computer vision and kinship analysis tasks.
pytorchpythonvisiondata-sciencedeep-learning
visionjo/pykinship
Jan 2020 - Oct 2021
SW components and demos for visual kinship recognition. An emphasis is put on the FIW dataset-- data loaders, benchmarks, results in summary.
Contributions:1 release, 264 commits, 40 PRs in 1 year 9 months
pythondata-sciencedeep-learningloadersrecognition
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