Federico Di Mattia

AI Strategic Lead

Emilia-Romagna, Italy
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Summary

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Senior
🎓
Top School
Federico Di Mattia is an AI Strategic Lead with nine years of experience turning research-grade machine learning and computer vision into reliable, production-scale automation for manufacturing. At ZURU he built and scaled the "ZURU AI" ecosystem across multiple global hubs, driving strategy, P&L-aligned roadmaps, and technology transfer from NZ R&D into industrial operations. His hands-on background ranges from high-performance C++ vision pipelines to GANs and graph-based architectures for quality control, with repeated success translating prototypes into robust robotics integrations. He also conceived the "Enabling Team" model to accelerate group-wide AI adoption while balancing rapid GenAI prototyping and enterprise-grade reliability. Based in Emilia-Romagna and pursuing an MBA, he pairs deep engineering expertise with growing business acumen to make AI a measurable business asset. An often-overlooked strength is his track record scaling teams and governance internationally, ensuring AI assets remain both portable and operational across diverse regulatory and manufacturing environments.
code9 years of coding experience
job6 years of employment as a software developer
bookMaster's degree Ingegneria informatica, Master's degree Ingegneria informatica at Università degli Studi di Modena e Reggio Emilia
languagesItalian, English
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Github Skills (43)

python10
data-science10
machine-learning10
ml10
deep-learning10
tensorflow10
deep-neural-networks10
neural-network10
theory9
generative-adversarial-network9
classification8
keras-tensorflow8
anomaly-detection8
pytorch7
semi-supervised-learning7

Programming languages (6)

TypeScriptC++SCSSJavaScriptJupyter NotebookPython

Github contributions (5)

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zurutech/anomaly-toolbox

May 2021 - Nov 2022

Anomaly detection using GANs.
Contributions:1 review, 103 commits, 5 PRs in 1 year 5 months
pytorchanomalydeep-learninganomaly-detectionmachine-learning
Material for the tutorial: "Deep Diving into GANs: from theory to production"
Contributions:33 commits, 3 PRs, 21 pushes in 1 year
pytorchtfganpycondeep-learninggenerative-adversarial-network
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