Joan Massich
Software Engineer at Disney Research
Zurich, Zurich, Switzerland
Summary
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Rockstar🎓
Top SchoolJoan Massich is a software engineer with 12 years of experience blending research-grade rigor and production-quality engineering, currently improving code quality at Disney Research in Zurich. With a background spanning postdoctoral and research engineering roles at Inria and multiple European universities, he brings deep expertise in numerical stability and testing infrastructure. He is an active open-source contributor to high-profile projects like scikit-learn—focusing on numerical precision and data-type correctness—and has modernized test suites across imbalanced-learn and MNE to pytest and CI workflows. Joan’s work sits at the intersection of ML algorithms, scientific computing, and developer tooling, ensuring reproducible, robust implementations rather than just feature delivery. Colleagues rely on him for hard-to-reproduce numeric fixes and for turning experimental code into maintainable, well-tested libraries.
11 years of coding experience
2 years of employment as a software developer
Heriot-Watt University Edinburgh CampusErasmus Mundus European Masters of Computer Vision and Robotics (ViBOT), Erasmus Mundus European Masters of Computer Vision and Robotics (ViBOT) at Universite de Bourgogne, Universitat de Girona, and Heriot-Watt UnivesityUniversité de BourgogneTexas Tech UniversityBachelor of Science (BSc), Computer Science, Bachelor of Science (BSc), Computer Science at Universitat de Girona
English, French, Spanish, Catalan
Stackoverflow
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Github Skills (17)
pytest10
python10
data-science10
testing10
scikit10
machine-learning10
cicd10
numpy10
scikit-learn10
data-analysis10
devops9
linear-models9
test-integration8
integration-testing8
unit-test8
Programming languages (20)
Github contributions (5)
mne-tools/mne-python
Aug 2017 - Sep 2019
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Role in this project:
Backend Developer & DevOps Engineer
Backend Developer & DevOps EngineerContributions:3 releases, 144 commits, 249 PRs in 2 years 1 month
Contributions summary:Joan primarily contributed to the codebase by implementing testing frameworks and streamlining development workflows. Their commits focused on integrating pytest for automated testing, porting existing tests, and updating the testing infrastructure, including Travis and Appveyor configurations. Additionally, they made changes to support code quality with pep8. The user also made contributions that involved the pre-processing, specifically concerning the annotations for the EDF files and added more test cases.
visualizationneuroimagingneurosciencepythonstatistics
scikit-learn/scikit-learn
May 2017 - May 2019
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:21 commits, 38 PRs, 285 comments in 1 year 11 months
Contributions summary:Joan primarily contributes to the `scikit-learn` repository by addressing numerical precision and data type consistency in machine learning algorithms. Their commits involve modifying existing code to ensure the proper handling of data types (float32/float64), including test cases to prevent unintended data type conversions. They focused on the LogisticRegression and Ridge models, particularly optimizing the implementation for the SAG solver, addressing related numerical stability, and preventing upcasting. This indicates a focus on numerical accuracy and robustness within the library.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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