Mani Malek

Python Programmer at AstraBionics

Tajrish, Tehran Province, Iran
email-iconphone-icongithub-logolinkedin-logotwitter-logostackoverflow-logofacebook-logo
Join Prog.AI to see contacts
email-iconphone-icongithub-logolinkedin-logotwitter-logostackoverflow-logofacebook-logo
Join Prog.AI to see contacts

Summary

🤩
Rockstar
🎓
Top School
Mani Malek is a research staff scientist and software engineer with a decade of experience building privacy-preserving and large-scale multimedia systems, now working at Google DeepMind after leadership roles at Meta. He led Private ML efforts—differential privacy and federated learning—and previously architected video and audio fingerprinting and approximate k-NN search at scale. Mani blends deep academic grounding (PhD in Electrical and Computer Engineering) with hands-on production impact, from boosting SQL equijoin performance to shipping deployed ML systems. An active open-source contributor, he’s contributed privacy tooling to the widely used PyTorch Opacus project, improving privacy engines and model inspection for DP training. Colleagues rely on him for bridging research and engineering: he moves novel algorithms into robust, production-ready implementations.
code10 years of coding experience
bookAmirkabir University of Technology
bookHigh School Diploma, Mathematics, 19.86, High School Diploma, Mathematics, 19.86 at Dr. Hashtroodi High School
github-logo-circle

Github Skills (6)

neural-network10
pytorch10
machine-learning10
deep-learning10
differential-privacy10
python10

Programming languages (2)

Jupyter NotebookPython

Github contributions (5)

github-logo-circle
meta-pytorch/opacus

Mar 2020 - Jan 2021

Training PyTorch models with differential privacy
Role in this project:
userML Engineer
Contributions:1 review, 28 commits, 33 comments in 10 months
Contributions summary:Mani primarily contributed to the development and modification of utilities related to differential privacy for PyTorch models within the `opacus` repository. Their commits focused on enhancing the functionality of the privacy engine, adding and improving model inspection tools for compatibility with differential privacy techniques, and integrating clipping strategies and statistics gathering. The user's work directly supports training PyTorch models with differential privacy.
differential-privacypytorchprivacy-preserving-machine-learningdeep-learningmachine-learning
Contributions:5 pushes in 2 months
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.
Request Free Trial