Reuben Dunn
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolReuben Dunn is a junior software engineer based in San Francisco with a decade of hands-on experience and a strong appetite for machine learning, network security, and graphics rendering. He learns new languages and stacks quickly and has proven that versatility by contributing full-stack improvements to the high-profile PyTorch Captum project—adding multi-model comparison in Captum Insights, shrinking package size, and fixing frontend widget and data-loading issues. Comfortable working across React frontends and Flask backends, he focuses on practical usability and performance gains rather than theoretical tinkering. Actively seeking internships or contract roles, he brings production-minded open-source experience and a pragmatic engineering mindset ready to accelerate ML- and security-focused teams.
10 years of coding experience
University of California, Davis
Github Skills (14)
interpretation10
front-end-development10
python10
react10
flask-ask9
flask9
backend9
back-end-development9
javascript9
pytorch9
typescripts8
machine-learning8
typescript8
typescript-types8
Programming languages (7)
Github contributions (5)
pytorch/captum
Dec 2020 - Aug 2021
Model interpretability and understanding for PyTorch
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:2 reviews, 7 commits, 3 PRs in 8 months
Contributions summary:Reuben primarily contributed to the Captum Insights module, implementing features for model comparison and addressing widget-related issues. They added the ability to compare multiple models in the Insights application, modifying both the frontend (React) and backend components. Furthermore, the user reduced package size by replacing a graphing library, compressing the Flask server, and excluding unused dependencies. They also fixed widget errors related to target class selection and addressed data loading issues, improving overall usability.
pytorchinterpretable-aifeature-importanceunderstandinginterpretability
Reubend/captum
Dec 2020 - Aug 2021
Model interpretability and understanding for PyTorch
Contributions:15 pushes in 7 months
pytorchunderstandinginterpretabilitydeep-learningmachine-learning
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