Nikolay Zakirov
Blockchain Cryptography Architect at Terminal 3
Hong Kong, China
Summary
🤩
Rockstar🎓
Top SchoolNikolay Zakirov is a Blockchain Cryptography Architect based in Hong Kong with six years of focused experience at the intersection of mathematics, Rust, and privacy-preserving cryptography. He leads applied cryptography teams building production-grade PETs—FHE, MPC, ZK proofs and TEE integrations—while also architecting decentralized systems that span IPFS, DIDs/VCs and MPC networks. Prior roles as a quant investor and portfolio manager sharpened his ability to turn theoretical models into robust, high-performance production code in Rust, Python and TypeScript. An active contributor to high-profile open-source projects (notably performance work in Google Brain’s AutoML/EfficientDet), he brings practical memory- and training-optimizations experience to ML-enabled crypto systems. He has published and implemented novel primitives—such as a threshold ML-KEM variant—and designed an original approach for efficiently queryable fingerprinted private databases with ZK proofs. His background in advanced discrete mathematics (MSc with honors and PhD work) underpins a rare blend of rigorous theory and pragmatic engineering.
6 years of coding experience
6 years of employment as a software developer
Master of Business Administration (MBA), Strategy, Master of Business Administration (MBA), Strategy at HKUST Business SchoolDoctor of Philosophy - PhD (not completed), Discrete Mathematics, Doctor of Philosophy - PhD (not completed), Discrete Mathematics at Moscow State University (Department of Mechanics and Mathematics)High school, High school at Kolmogorov School of Physics and MathematicsHong Kong University of Science and Technology (HKUST)
English, French, Russian
Stackoverflow
1,545reputation
149kreached
80answers
7questions
Github Skills (16)
machine-learning10
tensorflow10
python10
efficientnet10
multi-process9
python-multiprocessing9
multiprocessing9
computer-vision9
gcp8
pandas6
rust6
logistic-regression6
scipy6
loops6
dataframe6
Programming languages (13)
Github contributions (5)
google/automl
Aug 2020 - Sep 2020
Google Brain AutoML
Role in this project:
ML Engineer
ML EngineerContributions:28 reviews, 5 commits, 5 PRs in 29 days
Contributions summary:Nikolay contributed significantly to the implementation of memory-saving gradients and gradient checkpointing within the EfficientDet model. They modified the model's architecture and training loop to include this optimization. Further contributions focused on improving the training process, including adding functionality to run epochs in child processes and integrating NVIDIA GPU memory logging.
brainefficientnetv2pythonefficientnetdeep-learning
NikZak/automl
Aug 2020 - Sep 2020
Google Brain AutoML
Contributions:79 pushes, 7 branches in 27 days
brainpythondeep-learningmachine-learningautoml
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