Thomas Bolton
AI Researcher at XBOW
Oxford, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolThomas Bolton is an AI researcher and machine learning engineer with a decade of experience bridging academic physics and production ML systems from Oxford to GitHub and now XBOW. He holds a PhD in Physics from the University of Oxford where he applied convolutional neural networks to ocean turbulence and taught Python to peers, and later translated that scientific rigor into production-grade ML work at GitHub. At GitHub he focused on ML-driven code analysis and quality assurance, contributing tests and infrastructure to the widely-used CodeQL action to ensure ML queries behaved across CLI versions and platforms. He combines strong research credentials and publication history with hands-on software engineering—Python, Linux, Git and test automation are daily tools—and has a track record of presenting results at conferences. Curious and methodical, he brings an uncommon mix of oceanographic modeling experience and practical ML validation expertise to applied AI problems.
10 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Physics, Doctor of Philosophy (Ph.D.) Physics at University of OxfordSt Johns Marlborough
Github Skills (12)
typescript10
javascript10
typescripts10
typescript-types10
codeql10
test-automation10
act9
cil9
semmle-ql8
sec7
security7
it-security7
Programming languages (9)
Github contributions (5)
github/codeql-action
Jun 2022 - Jun 2022
Actions for running CodeQL analysis
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:2 reviews, 6 commits, 3 PRs in 14 days
Contributions summary:Thomas primarily contributed to the testing infrastructure of the CodeQL action. Their work included adding and updating tests, specifically focusing on the interaction of ML-powered queries with different CodeQL CLI versions and platforms. They modified configuration test files, integrated tests for new ML query pack versions, and ensured the correctness of security-extended tests. The user's contributions were essential for validating CodeQL's functionality and ensuring code quality.
code-scanningsemmle-qladvanced-securitycodeqlci
TomBolton/rvm-find
Feb 2019 - Mar 2019
Relevance Vector Machine implementation using the scikit-learn API.
Contributions:13 commits, 17 pushes in 6 days
apiscikit-learn-apipythondata-sciencemachine-learning
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