Ramon 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
3 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at École Polytechnique
Associate's degree, Informatics, Associate's degree, Informatics at Fundação de Ensino de Contagem (FUNEC)
Master's degree, Computer Science, Master's degree, Computer Science at Universidade Federal de Minas Gerais
Uma classe LaTeX para dissertações, teses e propostas do Programa de Pós-Graduação em Ciência da Computação (PPGCC) da Universidade Federal de Minas Gerais (UFMG).
Contributions:2 releases, 12 commits, 1 PR in 5 months
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