Anton Lee
Wellington, Wellington, New Zealand
Summary
👤
Senior🎓
Top SchoolAnton Lee is a research-focused software engineer and PhD candidate in AI based in Wellington, New Zealand, with eight years of experience building robust ML and distributed systems. He has bridged academic research and production-grade tooling as a founding contributor and core maintainer of CapyMOA, a Python library for data stream learning with PyTorch and online continual learning support that gained notable community traction. His work spans continual learning, data stream mining, and adaptive cloud systems, including deploying Kubernetes clusters and building resilient stream processing for agricultural robotics. At Victoria University and Waikato he combined research, teaching, and hands-on engineering—managing CI/CD, releases, and community contributions for research software. Anton’s background includes industry experience at a storage/processing startup and a track record of turning research prototypes into maintained open-source tools. He favors practical solutions to catastrophic forgetting and adapting ML systems to changing data in real-world environments.
8 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at The University of WaikatoMaster of Science - MS, Artificial Intelligence, Master of Science - MS, Artificial Intelligence at Victoria University of Wellington
Github Skills (80)
moa10
clustering10
hydra10
stream10
mining10
recommender10
classification10
java10
avalanche10
pytorch10
evaluation10
jpype10
machine-learning-algorithms9
cross-language9
python9
Programming languages (5)
Github contributions (5)
tachyonicClock/avalanche
Aug 2021 - Dec 2023
Avalanche: an End-to-End Library for Continual Learning.
Contributions:37 pushes, 7 branches in 2 years 4 months
pytorchend-to-enddeep-learningavalanchecontinual-learning
tachyonicClock/CapyMOA
May 2024 - Mar 2025
Enhanced machine learning library tailored for data streams, featuring a Python API integrated with MOA backend support. This unique combination empowers users to leverage a wide array of existing algorithms efficiently while fostering the development of new methodologies in both Python and Java.
Contributions:3 releases, 120 pushes, 39 branches in 10 months
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