Tudor Cebere
Doctoral Student at Harvard University
Bucharest, Romania
Summary
🤩
Rockstar🎓
Top SchoolTudor Cebere is a doctoral student and researcher with eight years of experience at the intersection of privacy-preserving machine learning, applied cryptography, and data science. Based in Bucharest and affiliated with Inria and Harvard SEAS, he contributes to OpenDP and has strengthened PySyft’s Plans system to enable flexible, type-safe remote data workflows. His background spans internships and research stints at the Vector Institute, UiPath, and Keysight, plus leadership in the ROSEdu student community, demonstrating both technical depth and mentorship. Tudor’s work blends theory and implementation—he tackles differential privacy and secure ML problems while shipping practical code that makes privacy-respecting analytics more usable.
8 years of coding experience
1 year of employment as a software developer
Licenta, Computer Science and Engineering Departament, Licenta, Computer Science and Engineering Departament at Universitatea POLITEHNICA din BucureștiMaster's degree, Mathematics and Computer Science, Research Master with focused on Applied Cryptography and Machine Learning., Master's degree, Mathematics and Computer Science, Research Master with focused on Applied Cryptography and Machine Learning. at École normale supérieure de Lyon
Github Skills (8)
pytorch10
python10
data-science10
federated-learning9
machine-learning9
deep-learning8
type-checking8
typehinting8
Programming languages (7)
Github contributions (5)
OpenMined/PySyft
Feb 2020 - Dec 2022
Perform data science on data that remains in someone else's server
Role in this project:
Data Scientist
Data ScientistContributions:174 reviews, 463 commits, 280 PRs in 2 years 10 months
Contributions summary:Tudor primarily focused on improving the flexibility of Plans, a core component of the pysyft library used for data science on remote data. Their contributions involved adding support for various data structures like lists, dictionaries, and tuples as inputs to Plans, alongside implementing typechecking to ensure the consistency of inputs between the build and call stages. This work directly addressed issue #3184, improving the flexibility and type safety of Plans, essential for ensuring the models worked as expected on other servers. The user also worked on adding support for different data type casting.
pytorchcryptographyacquiringpythonscience
tudorcebere/PySyft
May 2020 - Oct 2022
A library for encrypted, privacy preserving machine learning
Contributions:513 commits, 7 pushes in 2 years 4 months
pythonprivacy-enhancing-technologiesprivacyprivacy-preserving-machine-learningmachine-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.