Pierre-yves Lablanche
Chief Technology Officer at WiseFins
Mougins, Provence-Alpes-Côte d'Azur, France
Summary
👤
Senior🎓
Top SchoolPierre-yves Lablanche is a seasoned CTO and solution architect with over 20 years of experience designing scalable IoT, cloud and data-driven platforms for industrial and financial customers. He has repeatedly built and led R&D and engineering teams from concept to deployment, aligning technical roadmaps with business goals while keeping security, reliability and technical debt under control. As co-founder of multiple startups he combines product-led thinking with hands-on architecture and has a track record of securing innovation funding and operationalizing AI and sustainability goals in product roadmaps. His open-source work as a data scientist—implementing gcForest for image and sequence processing—underscores a practical ML curiosity that complements his executive practice. Based in Mougins, France, he is known for turning complex legacy systems and high-stakes programs into maintainable, high-performance solutions through collaborative leadership.
10 years of coding experience
22 years of employment as a software developer
Master's degree, Master's degree at Institut supérieur d'électronique et du numérique
English
Stackoverflow
23reputation
446reached
1answer
1question
Github Skills (14)
machine-learning10
random-forest10
python10
image-processing9
data-science9
scikit-learn9
scikit9
algorithm9
algorithms9
deep-learning8
decision-tree6
slice6
numpy6
binary-tree6
Programming languages (3)
Github contributions (5)
pylablanche/gcForest
Mar 2017 - Sep 2017
Python implementation of deep forest method : gcForest
Role in this project:
Data Scientist
Data ScientistContributions:89 commits, 39 pushes, 35 comments in 6 months
Contributions summary:Pierre-yves primarily contributed to the implementation of the gcForest algorithm, focusing on image and sequence data processing. Their work involved developing multi-grain scanning methods and implementing the cascade forest layer. The contributions included code changes related to slicing images and sequences, integrating random forests, and implementing the cascade forest structure. The user demonstrated a solid understanding of machine learning concepts and the application of random forest classifiers within the context of the deep forest method.
pythondeep-learningpython-implementationmethodmachine-learning
pylablanche/bigdata_aims_senegal_II
Jan 2016 - Feb 2017
Contributions:17 pushes, 1 branch in 1 year 1 month
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