Mani Malek
Research Staff at Google DeepMind
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolMani 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.
10 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy (PhD) Electrical and Computer Engineering, Doctor of Philosophy (PhD) Electrical and Computer Engineering at The University of British ColumbiaMaster of Science (M.Sc.) Electrical and Computer Engineering, Master of Science (M.Sc.) Electrical and Computer Engineering at University of Tehran
English, Persian
Github Skills (6)
neural-network10
pytorch10
machine-learning10
deep-learning10
differential-privacy10
python10
Programming languages (2)
Github contributions (5)
pytorch/opacus
Mar 2020 - Jan 2021
Training PyTorch models with differential privacy
Role in this project:
ML Engineer
ML EngineerContributions: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.
pytorchpytorch-modelsprivacydeep-learningprivacy-preserving-machine-learning
birdx0810/opacus
Dec 2021 - Dec 2021
Training PyTorch models with differential privacy
Contributions:1 push in 1 day
pytorchpytorch-modelsprivacydeep-learningdifferential-privacy
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