Ramon Gonze
Belo Horizonte, Minas Gerais, Brazil
Summary
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SeniorRamon Gonze is a PhD candidate in computer science cotutelle between École Polytechnique and UFMG, with nine years of experience focusing on formal models for quantifying privacy and utility in data releases. His work applies Quantitative Information Flow and differential privacy frameworks to help data curators balance participant privacy with analytical usefulness, and he has collaborated with Inria on shuffling and differential privacy analyses. Ramon’s master thesis developed a QIF model for attribute-inference attacks and sampling-based utility, reflecting a strong mix of theoretical rigor and practical tooling (including a geometric visualization tool built during undergraduate research). He has hands-on experience teaching programming and supporting IT operations, giving him an uncommon blend of research depth and user-facing technical support. Based in Minas Gerais, Brazil, Ramon brings a measured, formal approach to privacy-preserving data publishing that is directly aimed at informing real-world curator decisions.
9 years of coding experience
Github Skills (15)
differential-privacy9
data-privacy8
latex8
re-identification8
python8
qif8
machine-learning7
visualization7
de-identification6
privacy6
data-analytics5
cross-platform4
open-source4
vulnerability1
microdata1
Programming languages (5)
Github contributions (5)
ramongonze/qiflib
Oct 2021 - Jul 2022
Contributions:1 release, 48 commits, 21 pushes in 9 months
ramongonze/pc
Apr 2019 - Dec 2019
Contributions:42 commits, 18 pushes in 8 months
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