Eugene Hu
Machine Learning Software Engineer at Bittensor
Coquitlam, British Columbia, Canada
Summary
🤩
Rockstar🎓
Top SchoolEugene Hu is a Machine Learning Software Engineer with five years of experience applying statistical learning and mathematical modeling to biological data, cryptography-adjacent projects, and internet-scale neural network systems. Based in Coquitlam, BC, he combines a strong academic foundation (MS in Physics, BS in Biophysics) with hands-on engineering at Bittensor, contributing full-stack and DevOps fixes that improved CI, testing, and validator/server reliability for an ambitious peer-to-peer neural network project. His research background produced ensemble models and CNNs that uncovered cofactors in androgen-dependent enhancer activity and a linearized LSTM for dynamical systems, reflecting an ability to move from interpretable science to production-ready ML. He enjoys peeling back randomness to reveal causal structure, and brings a rare blend of experimental rigor, teaching experience in STEM outreach, and production engineering to complex, data-rich problems.
4 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BS, Biophysics, Bachelor of Science - BS, Biophysics at University of WaterlooMaster of Science - MS, Physics, Master of Science - MS, Physics at Simon Fraser University
Github Skills (18)
pytest10
python10
testing10
cicd10
artificial-neural-networks9
pytorch9
machine-learning9
devops9
deeplearning-ai9
neural-network9
deep-learning9
blockchain8
ai8
cryptocurrency8
substrate8
Programming languages (4)
Github contributions (5)
opentensor/bittensor
Jan 2023 - Jan 2023
Internet-scale Neural Networks
Role in this project:
Full-stack Developer & DevOps Engineer
Full-stack Developer & DevOps EngineerContributions:7 releases, 415 reviews, 70 commits in 23 days
Contributions summary:Eugene contributed to fixing Circle CI issues and implemented mock calls to improve testing within the bittensor project. They also addressed issues related to external IP retrieval by fixing and reordering the external APIs. Furthermore, they merged various branches, indicating code integration and potentially refactoring work, along with fixing validator and server-related issues. They demonstrated proficiency in maintaining and improving core functionalities of the project.
pytorchsubstratedeep-learningp2pblockchain
opentensor/validators
Jun 2023 - Aug 2023
Repository for bittensor validators
Contributions:4 releases, 52 reviews, 52 PRs in 2 months
aiblockchaincryptocurrencydeep-learningmachine-learning
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