Peter Abeles
Founder at NINOX 360 LLC
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolPeter Abeles is a founder and hands-on computer vision and robotics engineer with 15 years of experience turning R&D into production systems for customers including DARPA, NASA, and major defense and autonomous vehicle companies. He has led sensor fusion, localization, and embedded vision efforts at Tesla and Argo AI and now builds commercial-grade 3D vision and photogrammetry products at NINOX 360. A prolific open-source maintainer, he is the primary developer of BoofCV and a major contributor to the EJML linear algebra library, optimizing core algorithms and adding concurrency for real-world performance. His work spans the full stack from pixel-level algorithms for embedded systems to distributed sensor networks and productionized quality-control systems in manufacturing. Trained in physics at Carnegie Mellon, he combines rigorous analytical methods with practical engineering and a knack for publishing robust solutions even while in industry.
15 years of coding experience
17 years of employment as a software developer
Physics Physics art Minor, Physics Physics art Minor at Carnegie Mellon University
Github Skills (15)
decompose10
matrix-multiplication10
data-structures10
deco10
javas10
matrix-decomposition10
computer-vision10
algorithms10
sparse-matrix10
linear-algebra10
image-processing10
data-structure10
java10
decomposition10
concurrency9
Programming languages (8)
Github contributions (5)
lessthanoptimal/BoofCV
Apr 2011 - Jan 2023
Fast computer vision library for SFM, calibration, fiducials, tracking, image processing, and more.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:13 releases, 9 reviews, 5077 commits in 11 years 10 months
Contributions summary:Peter's commits primarily focus on implementing and optimizing core functionalities within the BoofCV library, a computer vision project. The user has worked on adding support for new image data types and geometric algorithms. The changes suggest efforts to improve the core processing engine of the library and extend its capabilities to support different types of images.
structure-from-motionqrcode-generatorintrinsicsmicro-qr-codeqr-code
lessthanoptimal/ejml
Mar 2014 - Jan 2023
A fast and easy to use linear algebra library written in Java for dense, sparse, real, and complex matrices.
Role in this project:Back-end Developer
Back-end DeveloperContributions:2 releases, 89 reviews, 807 commits in 8 years 11 months
Contributions summary:Peter contributed to the sparse linear algebra library, specifically focusing on the implementation and optimization of algorithms within this domain. They made significant modifications to the Cholesky decomposition and sparse matrix multiplication routines, including adding concurrency support. Furthermore, the user added functions for core mathematical operations, and improvements to the structure of the code by renaming and refactoring existing components.
eigenvaluesmathmatriceslinear-algebra-librarylinear-algebra
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