Connelly Barnes

Seattle, Washington, United States

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Summary

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Senior

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Top School

Connelly Barnes is a senior research scientist at Adobe in Seattle with 14 years' experience advancing computer graphics, imaging, and data-parallel computing. He holds a Ph.D. in Computer Science from Princeton University, along with an MA in CS and a dual honors B.S. in Mathematics and Computational Physics from Oregon State University, each with a 4.0 GPA. Before Adobe, he served as Assistant Professor of Computer Science (Graphics) at the University of Virginia and did postdoctoral research at Adobe, grounding his work in both academia and industry. An active open-source contributor to the Halide project, he worked on backend components—cpp_bindings, MLFImage, FImage and schedule_transforms—to enable fast, portable data-parallel pipelines and LLVM-based code generation. His work bridges rigorous research with production-ready tooling, helping image-processing workflows scale across platforms.

14 years of coding experience

11 years of employment as a software developer

Honors B.S., Mathematics, 4.0/4.0 Grade Point Average, Honors B.S., Mathematics, 4.0/4.0 Grade Point Average at Oregon State University

Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Princeton University

Stackoverflow

Stats

21reputation

11kreached

1answer

0questions

Github Skills (11)

halide10

c-language10

ocaml10

c-programming-language10

image-processing9

compilers9

compiler-compiler9

compiler9

dsl8

libjpeg6

python6

Programming languages (2)

C++Python

Github contributions (5)

halide/Halide

Oct 2011 - Oct 2013

a language for fast, portable data-parallel computation

Role in this project:

Back-end Developer

Contributions:79 commits in 2 years

Contributions summary:Connelly contributed to the cpp_bindings and apps directories, modifying files related to MLFImage, FImage and schedule_transforms. These changes involve integration of Halide with external tools, including cpp and OCaml bindings, to enable fast, portable data-parallel computation. The modifications appear to be focused on supporting image processing pipelines with a focus on enabling LLVM module generation through the doCompile and doLower calls.

computationhexagonhalideparallelgpu

tcyhx/image_perforation

Jul 2016 - Jul 2016

Contributions:22 commits in 1 day

acmpipelinesskipping

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