Andres Idrobo
Researcher at Rakuten Tech in Europe
Paris, Ile-de-France
Summary
👤
Senior🎓
Top SchoolAndres Idrobo is a researcher and machine learning/statistics specialist with 11 years of experience applying scalable, randomized methods and large-scale optimization to real-world datasets. Based in Paris, he holds advanced training from Paris-Sud and ENS Cachan and is completing a PhD focused on fast, stable statistical techniques that improve prediction on massive data. At Rakuten Tech he translated research into production-ready solutions while contributing to open-source neuroimaging tooling (notably enhancements and compatibility work in the popular nilearn library). His background in biomedical and electrical engineering gives him a practical edge when bridging theory and application, and he has a history of teaching database and math courses that sharpens his ability to communicate complex ideas.
11 years of coding experience
7 years of employment as a software developer
Master of Science (M.Sc.) Applied Mathematics, Master of Science (M.Sc.) Applied Mathematics at École normale supérieure de CachanDoctor of Philosophy (PhD) Informática, Doctor of Philosophy (PhD) Informática at Paris-Sud University (Paris XI)Ingeniero Biomédico Ingeniería biomédica/médica, Ingeniero Biomédico Ingeniería biomédica/médica at Universidad Autónoma de Occidente
Spanish, English, French
Github Skills (10)
scikit10
python10
neuroimaging10
scikit-learn10
brain-imaging9
testing9
machine-learning9
connectivity7
mri7
decoding7
Programming languages (2)
Github contributions (5)
nilearn/nilearn
Jun 2016 - May 2017
Machine learning for NeuroImaging in Python
Role in this project:
Data Scientist
Data ScientistContributions:178 commits, 8 PRs, 71 comments in 11 months
Contributions summary:Andres primarily contributed to the development and maintenance of the `nilearn` library, focusing on incorporating and updating components related to machine learning, particularly those from the scikit-learn library. Their work involved integrating backports for older versions of scikit-learn, ensuring compatibility with the project, and modifying validation and checking input routines within the library. Furthermore, the user has implemented tests related to the SpaceNet, ensuring that the models function correctly and follow the desired structure.
neuroimagingpythondecodingbrain-connectivitymachine-learning
ahoyosid/ReNA
Jun 2016 - Oct 2020
Contributions:18 commits, 1 PR, 15 pushes in 4 years 4 months
neighbornearest-neighborrecursivenearest
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