John Aslanides
Staff Research Engineer at DeepMind
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolJohn Aslanides is a Staff Research Engineer at DeepMind with 11 years of experience building and productionising ML systems and reinforcement-learning libraries. He bridges research and engineering, contributing to core JAX and TensorFlow Probability codebases—adding type annotations, improving developer ergonomics, and hardening checkpointing and model-saving for agents like R2D2/R2D3. His background spans probabilistic inference, gravitational-wave data analysis, and practical ML R&D consulting, reflecting a strong blend of theoretical and systems expertise. Based in London, he’s known for quietly improving code clarity and reproducibility in high-impact open-source projects (e.g., JAX, TFP, Haiku, Acme), a sign of craftsmanship that benefits both researchers and users.
11 years of coding experience
8 years of employment as a software developer
Australian National UniversityHigh School NSW HSC, High School NSW HSC at Canberra Grammar School
English, French, Spanish
Stackoverflow
137reputation
16kreached
0answers
4questions
Github Skills (34)
transformers10
probabilistic-programming10
checkpoint10
python10
machine-learning10
reinforcement-learning10
numpy10
haiku10
checkpointing10
type-annotations10
agent10
deep-learning10
tensorflow10
neural-network10
transformer10
Programming languages (7)
Github contributions (5)
google-deepmind/bsuite
Aug 2019 - Oct 2021
bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent
Role in this project:
Data Scientist
Data ScientistContributions:7 releases, 1 review, 96 commits in 2 years 2 months
Contributions summary:John removed an unused section from an analysis notebook related to how to cite the project, indicating a focus on refining the presentation of results. This suggests a role in data analysis, ensuring the clarity and conciseness of project documentation. Their work is likely aimed at improving the user experience by cleaning up the notebook and removing unnecessary content.
stable-baselinesagentreinforcement-learningdeep-reinforcement-learningcapabilities
google-deepmind/acme
May 2020 - Aug 2021
A library of reinforcement learning components and agents
Role in this project:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:4 releases, 83 commits, 1 PR in 1 year 3 months
Contributions summary:John primarily contributed to the core components of the reinforcement learning library. Their work involved significant modifications to the agent's checkpointing and model saving mechanisms, improving the stability and efficiency of the training process. They refactored the code to make checkpointing an optional feature in agents like R2D3, and made enhancements to the R2D2 learner. They also added a global acme_id flag for finer-grained control over path creation.
reinforcement-learningreinforcementagentsdeep-reinforcement-learning
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