Paolo Losi
CTO & Partner at enuan Srl
Cernusco sul Naviglio, Lombardy, Italy
Summary
👤
Senior🎓
Top SchoolPaolo Losi is a seasoned CTO and systems architect with 17 years of experience designing and delivering large-scale Internet applications, networks, and platforms from Italy. As co-founder and technical lead at enuan, he drives the Message3 conversational applications platform and manages customer projects, blending hands-on engineering with product leadership. His background includes leading a 10-person service engineering group at Elitel, where he integrated ISP networks, provisioning systems and piloted VoIP deployments, demonstrating deep operational and integration expertise. Paolo combines interests in conversational AI and NLP with agile development and mentoring, and has contributed to scikit-learn by improving Logistic Regression and LinearSVC behavior for sparse data and regularization edge cases. Trained as an electronic engineer at Politecnico di Torino, he brings both telecoms foundations and a pragmatic, research-informed approach to building dependable, production-ready systems.
17 years of coding experience
3 years of employment as a software developer
Electronic Engineering, Telecomunications, Electronic Engineering, Telecomunications at Politecnico di Torino
Stackoverflow
383reputation
6kreached
3answers
4questions
Github Skills (20)
python10
data-science10
scikit10
machine-learning10
scikit-learn10
data-structures8
data-structure8
numpy8
algorithms8
algorithm8
scipy7
sparse-matrix7
logging6
double6
ieee6
Programming languages (6)
Github contributions (5)
scikit-learn/scikit-learn
Dec 2010 - Mar 2012
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:38 commits in 1 year 3 months
Contributions summary:Paolo primarily contributed to the implementation and refinement of machine learning models within the scikit-learn library. Their work included enhancements to the Logistic Regression and LinearSVC models, particularly focusing on the handling of intercepts, minimum C calculation, and support for sparse data formats. These changes involved modifying core algorithms, adding new features, and improving existing functionalities related to model regularization and performance.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
enuan/golibstemmer
Mar 2017 - Mar 2023
Contributions:3 pushes in 6 years 1 month
golangportersnowballlibstemmer
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