Júlio Hoffimann
CEO at Arpeggeo®
Rio de Janeiro, Brazil
Summary
👤
Senior🎓
Top SchoolJúlio Hoffimann is a CEO and software leader based in Rio de Janeiro with 15 years of experience building advanced geostatistical and scientific software. He blends deep academic training—PhD from Stanford and postdoctoral work at IMPA—with hands-on contributions to open-source projects in Julia, improving image processing algorithms and adding advanced graph algorithms like Boykov-Kolmogorov max-flow. His background includes research roles at IBM and a consultancy for the United Nations, demonstrating an ability to translate complex mathematical methods into practical, production-ready tools. As founder of Arpeggeo®, he combines entrepreneurial drive with domain expertise in energy and geoscience, often tackling edge cases and robustness in algorithmic implementations. An uncommon strength is his cross-disciplinary fluency: from morphological image operations to maximum-flow graph algorithms, he navigates both theory and engineering to ship reliable analytical software.
15 years of coding experience
2 years of employment as a software developer
Master of Engineering (M.Eng.), Civil Engineering, Master of Engineering (M.Eng.), Civil Engineering at Universidade Federal de PernambucoPostdoctoral Fellow, Industrial Mathematics, Postdoctoral Fellow, Industrial Mathematics at IMPADoctor of Philosophy (Ph.D.), Energy Resources Engineering, Doctor of Philosophy (Ph.D.), Energy Resources Engineering at Stanford University
English, Portuguese
Github Skills (9)
algorithm10
image-analysis10
data-structures10
algorithms10
graph-algorithms10
image-processing10
julia10
data-structure10
unit-testing9
Programming languages (19)
Github contributions (5)
JuliaImages/Images.jl
Nov 2015 - Oct 2018
An image library for Julia
Role in this project:
Data Scientist
Data ScientistContributions:11 commits, 15 PRs, 149 comments in 2 years 11 months
Contributions summary:Júlio primarily contributed to the implementation and refinement of image processing algorithms within the Julia library. Their work involved adding and fixing the entropy calculation for grayscale images, addressing edge cases, and improving the overall robustness of the image analysis tools. Further contributions included adding the modified Hausdorff distance calculation between images and expanding the morphological operations. These changes suggest a focus on enhancing the core functionality of the image processing capabilities.
image-libraryimage-processingjulia
sbromberger/LightGraphs.jl
Mar 2016 - Mar 2017
An optimized graphs package for the Julia programming language
Role in this project:Back-end Developer
Back-end DeveloperContributions:18 commits, 9 PRs, 1 push in 1 year
Contributions summary:Júlio contributed significantly to the `lightgraphs.jl` package, primarily focusing on the implementation and testing of graph algorithms related to maximum flow problems. The contributions include the addition of the Boykov-Kolmogorov max-flow/min-cut algorithm, the modification of the generic `maximum_flow` function to include this algorithm, and the creation of new test cases to validate the implemented algorithms across multiple graphs. Other changes included refactoring and code improvements.
graphsoptimizedjulia-programming-languagegraph-drawinggraph
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.