Giorgio Patrini
Co-Founder, CEO And Chief Scientist at Sensity
Amsterdam, North Holland, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolGiorgio Patrini is a machine learning founder and scientist with 11 years of experience building practical AI systems and commercializing them at scale. As Co-Founder, CEO and Chief Scientist of Sensity, he led the creation of the first company focused on detecting AI-generated and manipulated media, securing enterprise and government clients across four continents. His background spans deep generative models, privacy-preserving ML and federated learning from PhD and research roles, and he has a history of turning academic insights into product features. Giorgio is also an open-source contributor to scikit-learn, improving numerical stability and adding online-learning support to core algorithms—work that reflects his attention to robustness in production ML. He combines technical depth from institutions like ANU, Politecnico di Milano and Inria with proven startup and advisory experience in AI strategy and exits.
11 years of coding experience
2 years of employment as a software developer
Australian National UniversityMaster of Science (MSc) Computer Engineering, Master of Science (MSc) Computer Engineering at Politecnico di Milano
Italian, English, French
Github Skills (13)
data-analysis10
scikit-learn10
machine-learning10
pca10
python10
data-science10
numpy10
scikit10
statistics9
data-structure7
data-structures7
algorithm7
algorithms7
Programming languages (8)
Github contributions (5)
scikit-learn/scikit-learn
Aug 2015 - Mar 2016
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:24 commits, 22 PRs, 488 comments in 7 months
Contributions summary:Giorgio primarily contributed to improving the scikit-learn library by addressing numerical stability issues and enhancing existing functionalities. This involved removing potential runtime warnings in the PCA module, specifically in a corner case of the fit method. The user also implemented the partial_fit method for various scalers, extending their usability for online learning scenarios. Furthermore, the user introduced and documented changes related to the randomized SVD implementation, including improvements to the power iteration normalizer.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
giorgiop/scikit-learn
Aug 2015 - Oct 2016
scikit-learn: machine learning in Python
Contributions:334 pushes, 41 branches in 1 year 2 months
pythondata-sciencelearn-machine-learningneural-networksmachine-learning
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