Silvia Gandy
Principal Engineer at Midokura
Barcelona, Catalonia, Spain
Summary
🤩
Rockstar🎓
Top SchoolSilvia Gandy is a Principal Engineer based in Barcelona with a 7-year professional focus on AI, machine learning, and secure backend systems, now leading advanced development at Midokura. She blends academic rigour from a PhD at Tokyo Institute of Technology and dual master’s training in computer science and applied mathematics with hands-on delivery in automotive vision, biometrics, and industrial ML production. Silvia has progressed from research and team leadership to directing AI efforts and architecting resilient systems, shipping real-time, hardware-constrained solutions for pedestrian detection and telecom/processing projects. An active open-source contributor, she strengthened privacy-preserving data science tooling by improving WebSocket security and reliability in the well-known OpenMined/PySyft project. Colleagues rely on her for bridging deep research, secure engineering, and production-grade reliability across distributed systems.
7 years of coding experience
13 years of employment as a software developer
Japanese, Japanese at The Naganuma School - Tokyo School of Japanese LanguageDoctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at Tokyo Institute of TechnologyMaster’s Degree Computer Science, Master’s Degree Computer Science at Technical University of Munich
English, German, French, Spanish, Japanese, Arabic
Github Skills (5)
websocket10
python10
cryptography10
security10
networking9
Programming languages (4)
Github contributions (5)
OpenMined/PySyft
Mar 2019 - Mar 2020
Perform data science on data that remains in someone else's server
Role in this project:
Back-end Developer & Security Engineer
Back-end Developer & Security EngineerContributions:128 commits, 57 PRs, 62 pushes in 11 months
Contributions summary:Silvia contributed to the core functionality of the `pysyft` library, specifically focusing on the implementation of secure communication channels using WebSockets. The contributions involve adding features such as max size arguments and reconnection mechanisms for dropped connections, and refactoring existing websocket-related code for improved reliability. Furthermore, the user implemented security enhancements and contributed to the core functionality. These updates significantly improve the stability and security of the library's data science capabilities.
pytorchcryptographyacquiringpythonscience
midokura-silvia/PySyft
Mar 2019 - Mar 2020
A library for encrypted, privacy preserving deep learning
Contributions:140 pushes, 22 branches in 11 months
privacy-enhancing-technologiesprivacydeep-learningmachine-learningencrypted
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