Thomas Veasey is a Senior Principal Software Engineer based in York, UK, with nine years of focused industry experience and a long career building production-grade systems for finance, defence, and large-scale analytics. He led R&D at Prelert, developing core time-series modelling and anomaly-detection engines for high-throughput streaming data, and continues that work at Elastic where he contributes to ML features in the widely used Elasticsearch project. His background spans low-latency quantitative systems at Bloomberg and embedded optimisation and architecture work earlier in his career, reflecting deep applied mathematics and systems thinking rooted in a Physics degree from Cambridge. Thomas combines hands-on algorithm design with product-facing responsibilities—authoring technical papers, talks, and marketing material—and a pragmatic focus on test reliability and robust change-detection algorithms. He’s comfortable moving between research, implementation and operational hardening, and has a track record of keeping complex ML components stable in large open-source ecosystems.
Free and Open Source, Distributed, RESTful Search Engine
Role in this project:
ML Engineer
Contributions:103 reviews, 23 commits, 49 PRs in 4 years 6 months
Contributions summary:Thomas primarily contributed to the machine learning (ML) components of the Elasticsearch project, specifically within the "x-pack/plugin/ml" directory. Their work involved fixing integration tests, re-enabling tests, and improving the reliability of detection rules. The user also implemented a new parameter for classification and made improvements to change detection aggregation algorithms. The commits suggest a focus on enhancing the stability and functionality of ML-related features.
Contributions:2288 pushes, 1581 branches in 5 years 9 months
cppmachine-learninglearning-c
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Thomas Veasey - Senior Principal Software Engineer at Elastic