Mohamed聽Abogazia
Software Development Engineer II at Amazon
London, England, United Kingdom
Summary
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Rockstar馃帗
Top SchoolMohamed Abogazia is a Software Development Engineer II at Amazon Prime Video Financial Systems in London with seven years of experience building secure, high-performance systems. He combines backend production engineering at Amazon with deep applied research in privacy-preserving ML, having contributed significant SMPC and federated-learning features to the widely used OpenMined/PySyft library. His work has reduced network delays and scaled secure dataset handling, and he鈥檚 implemented cryptographic protocols that improved system throughput. Earlier roles include engineering leadership in the Egyptian Army where he digitized archival workflows and cut costs and manpower dramatically, demonstrating a talent for pragmatic, high-impact solutions. Mohamed mentors peers and has a track record of shipping both research-grade cryptography and production financial systems. He holds a BE in Computer Engineering from Tanta University and uniquely bridges secure ML research with production-grade software delivery.
7 years of coding experience
3 years of employment as a software developer
Bachelor of Engineering - BE, Computer Engineering, Bachelor of Engineering - BE, Computer Engineering at Tanta University
Arabic, English, French, German
Github Skills (10)
tensorrt10
secure-computation10
pytorch10
tensor10
tensorflow10
federated-learning10
python10
operation10
cryptography9
deep-learning8
Programming languages (6)
Github contributions (5)
OpenMined/PySyft
Dec 2019 - Aug 2020
Perform data science on data that remains in someone else's server
Role in this project:
ML Engineer
ML EngineerContributions:34 reviews, 8 commits, 20 PRs in 8 months
Contributions summary:Mohamed contributed extensively to the development of the `pysyft` library, focusing on secure computation and federated learning. Their work involved implementing and refining functionalities related to dataset management, including creating, sending, and retrieving datasets within a federated environment. The user also added support for SMPC (Secure Multi-Party Computation) with operations like maxpooling and avgpooling for 2d/3d tensors. Furthermore, they implemented Replicated Sharing Tensor (RST) functionalities, including secret sharing, reconstruction, and linear operations.
pytorchcryptographyacquiringpythonscience
abogaziah/PySyft
Jan 2020 - Apr 2020
A library for encrypted, privacy preserving machine learning
Contributions:52 pushes, 1 branch in 3 months
privacy-enhancing-technologiesprivacyprivacy-preserving-machine-learningmachine-learningencrypted
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