Chris Sweeney
Research Scientist Manager (Reality Labs Research) at Meta
Redwood City, California, United States
Summary
👤
Senior🎓
Top SchoolChris Sweeney is a research scientist manager at Meta Reality Labs with 12 years of experience building real-time 3D scene understanding systems for augmented reality, spanning computer vision, SLAM, sensor fusion, and large-scale optimization. He leads and grows teams translating cutting-edge perception research into production-grade systems, recruiting both new grads and experienced engineers to tackle AR’s hard problems. His background includes research roles at Facebook, Geomagical Labs, and the University of Washington, grounded in a PhD in Computer Science from UCSB and a BS from the University of Virginia. Chris brings hands-on performance engineering experience to his research leadership—he has contributed optimizations to the widely used Ceres Solver (including a Schur specialization for bundle adjustment and Jet/Eigen integrations). Based in Redwood City, he blends deep academic training with pragmatic engineering to push AR toward ubiquitous deployment.
12 years of coding experience
5 years of employment as a software developer
PhD, Computer Sciece, PhD, Computer Sciece at University of California, Santa BarbaraBS, Computer Science, BS, Computer Science at University of Virginia
Github Skills (8)
numerical-optimization10
ceres-solver10
c-language10
eigen10
cprogramming-language10
performance-optimization9
computer-vision9
multithreading8
Programming languages (3)
Github contributions (5)
ceres-solver/ceres-solver
Feb 2015 - Aug 2017
A large scale non-linear optimization library
Role in this project:
Back-end Developer / Performance Engineer
Back-end Developer / Performance EngineerContributions:6 commits, 22 comments, 3 issues in 2 years 6 months
Contributions summary:Chris contributed to improving the performance and efficiency of the Ceres Solver library. Their work included making the multithreading check less strict to improve flexibility and fixing an issue with reversing ordered groups. They also optimized the library by adding a new Schur specialization, specifically for bundle adjustment scenarios commonly used in computer vision applications. Furthermore, the user extended the use of Jets with Eigen matrices and arrays, resulting in optimized scalar-to-jet binary operations.
structure-from-motiontrust-regionnonlinear-optimization-algorithmsc-plus-plusnonlinear-least-squares
nuernber/Theia
Aug 2013 - Nov 2013
Contributions:27 commits in 3 months
structure-from-motionparticularvisionproblemstheia
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