Todd Bell
CVML Engineer at Apple
Capitola, California, United States
Summary
👤
Senior🎓
Top SchoolTodd Bell is a CVML engineer with a decade of experience turning vision science and academic research into production-ready visual intelligence systems, currently applying that expertise at Apple in Capitola, CA. He blends deep academic credentials—a PhD and Master's from UT Austin and a history of funded research—with practical product work from co-founding a cargo-dimensioning startup to shipping vision solutions for AR/VR and HMDs. At Facebook Reality Labs he led perceptual quality and foveated rendering research (co-authoring DeepFovea) and built a virtual eye model used for spatio-temporal sensitivity analysis. His open-source contributions to scikit-video show a focus on robust tooling and metrics, adding benchmark tests and quality assessment measures like MS-SSIM and NIQE that improve video-processing reliability. Known for bridging theory and systems, he frequently moves ideas from papers into testable, performant pipelines. A pragmatic problem-solver, he pairs hands-on backend and testing work with a strong research orientation toward perceptual quality.
10 years of coding experience
12 years of employment as a software developer
Bachelor's Degree, Computer Engineering, Bachelor's Degree, Computer Engineering at Clemson UniversityMaster's Degree, Electrical and Computer Engineering, Master's Degree, Electrical and Computer Engineering at The University of Texas at Austin
German, English
Github Skills (6)
video-processing10
python10
testing10
ffmpeg9
numpy9
unit-testing9
Programming languages (4)
Github contributions (5)
scikit-video/scikit-video
Dec 2015 - Jan 2020
Video Processing in Python
Role in this project:
Back-end Developer & Testing Engineer
Back-end Developer & Testing EngineerContributions:9 releases, 441 commits, 43 PRs in 4 years 1 month
Contributions summary:Todd's contributions primarily involved adding benchmark tests and fixing issues related to the underlying video processing libraries. The user also focused on addressing bugs related to the handling of raw video formats and various performance improvements through the implementation of different testing suites. Moreover, the user added supporting quality assessment tools, including ST-RRED, MS-SSIM, SSIM, NIQE, and Video Oracle, which are integral to improving the library's reliability.
pythonscientific-computingffmpeg-wrappervideo-processingvideo
scikit-video/scikit-video.github.io
Dec 2015 - Sep 2018
Contributions:101 pushes, 1 branch in 2 years 9 months
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