Alex Athorne
Research Engineer at Seldon
Stony Stratford, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolAlex Athorne is a research engineer with a PhD in Mathematics and eight years of software experience, currently maintaining Seldon's open-source XAI and drift-detection libraries, Alibi-Explain and Alibi-Detect. He brings a rare blend of rigorous mathematical training and practical engineering, contributing clear documentation and examples for complex explainability methods like counterfactuals, integrated gradients and SHAP. At Alibi-Detect he improved dependency management and testing infrastructure, streamlining optional dependencies and device handling to boost maintainability. Previously a software developer at AlliedCrowds, he focuses on production-ready tooling for model monitoring, reinforcement learning and generative models. Based in Stony Stratford, he combines deep theoretical insight with hands-on DevOps and backend work to make advanced ML tooling more accessible and reliable.
8 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Mathematics, Doctor of Philosophy - PhD, Mathematics at Imperial College LondonMMath, Mathematics, 1.1, MMath, Mathematics, 1.1 at University of Warwick
Stackoverflow
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Github Skills (13)
dependency-management10
machine-learning10
interpretation10
tensorflow10
python10
testing10
nlp8
pytorch8
documentation7
drift7
git7
automations7
automation7
Programming languages (4)
Github contributions (5)
SeldonIO/alibi-detect
Oct 2021 - Jan 2023
Algorithms for outlier, adversarial and drift detection
Role in this project:
Backend & DevOps Engineer
Backend & DevOps EngineerContributions:1 release, 240 reviews, 11 commits in 1 year 3 months
Contributions summary:Alex focused on enhancing the project's dependency management and testing infrastructure. They added functionality for optional dependencies and updated testing frameworks to reflect these changes. Furthermore, the user removed checks and corrected warnings related to device handling within the codebase. These contributions suggest improvements in maintainability and a focus on ensuring the project's stability with optional dependencies.
benchmarkingadversarialdrifttime-seriesoutlier-detection
SeldonIO/alibi
Oct 2021 - Jan 2023
Algorithms for explaining machine learning models
Role in this project:ML Engineer
ML EngineerContributions:1 release, 115 reviews, 40 commits in 1 year 2 months
Contributions summary:Alex's commits primarily involve adding and refining documentation related to explainable AI (XAI) methods implemented within the `alibi` library. The user added an overview example, including code, and added details on global and local insights, methods like counterfactuals, anchors, and integrated gradients, and SHAP. The commits demonstrate a focus on clarifying the application and functionality of various explainers.
fairness-mlpythoninterpretabilityxaiexplanations
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