Jordi Pont-Tuset is a research scientist at Google DeepMind based in Zurich with 11 years of software engineering and research experience focused on robust, production-ready systems. He combines deep technical work—contributing to projects like Multiscale Combinatorial Grouping for image segmentation—with rigorous QA and test automation, as seen in his maintenance and testing contributions to a widely used arXiv LaTeX cleaner. Comfortable across back-end development, build systems, and test frameworks, he emphasizes stability and reproducibility in research software. Jordi’s profile reflects a researcher-engineer who bridges algorithmic innovation and the engineering discipline needed to make research tools reliable in practice.
Multiscale Combinatorial Grouping - Object Proposals and Segmentation
Role in this project:
Back-end Developer
Contributions:2 releases, 1 review, 88 commits in 7 years 11 months
Contributions summary:Jordi appears to be a back-end developer focused on implementing and maintaining core functionalities within the MCG project. Their initial commit added the base code, and subsequent commits added and modified files related to the project's core functionality. They also made updates related to the build system and the handling of empty UCMs, indicating involvement in the project's underlying structure and stability.
arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv
Role in this project:
QA Engineer / Test Automation Engineer
Contributions:9 reviews, 77 commits, 44 PRs in 3 years 11 months
Contributions summary:Jordi primarily focused on testing and quality assurance, evident through commits fixing tests and adding new test cases. Their work involved modifying test files to address bugs and improve the overall testing coverage. They interacted with test frameworks and made adjustments to ensure accurate and reliable results. The user also added new parameters to improve test configurations.
arxivcleansubmitlatex-templatelatex
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