Sophie Villerot, PhD
About Sophie Villerot, PhD
Sophie Villerot, PhD, is a Machine Learning Research Engineer at Inria in Lille, France, specializing in cryptography and decentralized privacy-preserving machine learning. She coordinates the Tailed project and has a background in remote sensing and quantum turbulence research.
Work at Inria
Sophie Villerot has been a Machine Learning Research Engineer at Inria since 2020. She is based in Lille, Hauts-de-France, France, and has been involved in various projects focusing on decentralized privacy-preserving machine learning. Villerot coordinates the Tailed project, collaborating with a team of four PhD candidates and three research engineers. Her responsibilities include maintaining and selecting coding conventions for the project. She also integrates experimental code related to U-Statistics and differential privacy, employing tools such as C++11/17, Python, and Cython. Documentation for her projects is managed using Doxygen and Sphinx, linked by Breathe.
Education and Expertise
Sophie Villerot holds a Doctor of Philosophy (Ph.D.) in Physics from ENS Lyon - Université Lyon I, where she conducted research on Quantum Turbulence from 2009 to 2012. She has a strong background in Informatics, having studied at JetBrains Academy (Hyperskill) and completed a master's program in 2021. Additionally, she engaged in self-paced learning and a MOOC at CNAM in 2018, focusing on Informatics. Villerot's expertise extends to cryptography, particularly in Zero-Knowledge Proofs, utilizing tools such as GMP, BOTAN, and Mpfr.
Previous Work Experience
Before her tenure at Inria, Sophie Villerot worked as a Remote Sensing & Machine Learning Engineer at SIRS from 2017 to 2019 in Villeneuve d'Ascq. Prior to that, she served as a Remote Sensing Engineer at Rayference for ten months in the Brussels Area, Belgium, from 2016 to 2017. Her early career included a research project at ENS Lyon, where she focused on Quantum Turbulence during her Ph.D. studies.
Technical Skills and Tools
Sophie Villerot employs a range of technical skills and tools in her work. She utilizes programming languages such as C++11/17 and Python, along with Cython for performance optimization. Her deployment processes are automated using Gitlab Pipelines. Villerot also engages in decentralized training techniques that address potential threats from malicious parties, ensuring the integrity and security of machine learning models. Her documentation practices involve using Doxygen and Sphinx, which are integrated through Breathe for effective project management.