Luis França
Research Software Development Engineer at Microsoft
São Paulo, Brazil
Summary
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Rockstar🎓
Top SchoolLuis França is a Research Software Development Engineer based in São Paulo with nine years of experience bridging machine learning research and production-grade software. He combines a doctoral background in Applied Computing and academic work on Markov Decision Processes with hands-on ML engineering at Microsoft, contributing to interpretable ML, Neural Architecture Search, and RL agents for games. Prior to that he spent six years building and supporting telecommunications BSS systems at Ericsson, giving him deep experience in C/C++, C#, large-scale systems and customer-facing operations. An active contributor to the interpretml/interpret project, he improved the Explainable Boosting Machine implementation and its documentation, reflecting a focus on reproducible, user-friendly ML tooling. Comfortable across Python, Scala, Spark and production workflows, he excels at translating research prototypes into robust products and operational solutions.
9 years of coding experience
6 years of employment as a software developer
Doctor in Applied Computing, Doctor in Applied Computing at Instituto Nacional de Pesquisas EspaciaisBsc in Computer Science, Bsc in Computer Science at Universidade Federal de Mato Grosso
Portuguese, English, French
Github Skills (8)
machine-learning10
explainable-artificial-intelligence10
python10
pytest9
testing9
scikit-learn9
scikit9
ai8
Programming languages (2)
Github contributions (5)
interpretml/interpret
May 2021 - Sep 2022
Fit interpretable models. Explain blackbox machine learning.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1 review, 36 commits, 11 PRs in 1 year 4 months
Contributions summary:Luis contributed significantly to the codebase by modifying and enhancing the Explainable Boosting Machine (EBM) implementation. Their work included refactoring code, handling unsigned integer data types within the EBM preprocessor, and adding a module for composite feature importance calculation. Furthermore, the user focused on improving documentation, including installation and debugging guides, which aids in user adoption and understanding. These changes involved modifications to core EBM files and related testing modules.
xaiinterpretmlfitinterpretableexplainability
luisffranca/shogun
Apr 2017 - Apr 2018
The Shogun Machine Learning Toolbox (Source Code)
Contributions:10 pushes, 5 branches in 11 months
toolboxmachine-learning-toolboxshogunmachine-learning
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