Giovanni De Toni
Research Scientist at Fondazione Bruno Kessler - FBK
Trentino – Alto Adige/Südtirol, Italy
Summary
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Rockstar🎓
Top SchoolGiovanni De Toni is a research scientist focused on responsible, human-centric AI, currently developing provable safeguards that help machine learning systems support — rather than supplant — human expertise. With a PhD from the University of Trento and ELLIS and over a decade of experience, he blends deep academic rigor with hands-on engineering from stints at institutions like CERN, the European Commission, Max Planck, and X (the moonshot factory). He has a track record shipping research into practice—from conversational AI at a Boston startup to contributing reliability improvements in the widely used Shogun machine learning toolbox. Giovanni’s work emphasizes fail-safes and preserving human agency, pairing formal guarantees with practical code hygiene and maintainability. Based in Trentino-Alto Adige, he navigates both large research labs and production codebases, often surfacing subtle system failure modes before they reach users.
11 years of coding experience
6 years of employment as a software developer
Erasmus+ Visiting Student Artificial Intelligence, Erasmus+ Visiting Student Artificial Intelligence at The University of EdinburghDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Università di TrentoMaturità Scientifica Indirizzo Moderno, Maturità Scientifica Indirizzo Moderno at Liceo Scientifico Niccolò Tron
English, Italian
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (5)
build-system10
c-language10
cmake10
cprogramming-language10
unit-testing9
Programming languages (7)
Github contributions (5)
shogun-toolbox/shogun
Jan 2017 - Aug 2020
Shōgun
Role in this project:
Back-end Developer
Back-end DeveloperContributions:277 commits, 100 PRs, 36 pushes in 3 years 7 months
Contributions summary:Giovanni primarily focused on improving the codebase's reliability and maintainability. They addressed build and compilation warnings by removing dependencies and replacing unsafe methods. They also modified code to remove and exclude meta examples based on dependency availability. Furthermore, they implemented improvements to the progress bar functionality.
cmakedata-sciencegunc-plus-plusmachine-learning
geektoni/seasonal_influenza_predictor
Mar 2017 - Oct 2020
Contributions:4 PRs, 233 pushes, 9 branches in 3 years 7 months
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