David Roberts
Associate Professor
New South Wales, Australia
Summary
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Rockstar🎓
Top SchoolDavid Roberts is an Associate Professor at the University of New England, Australia, with 12 years of professional experience bridging academic research and production-grade software engineering. Based in New South Wales, he pairs a PhD in History with hands-on back-end and ML engineering, bringing a rigorous analytical perspective to problem solving. He has contributed to the high-profile open-source Elasticsearch project, improving timestamp format detection, enabling custom formats, and hardening ML job management by fixing race conditions and optimizing model memory estimation. David's work demonstrates an uncommon blend of humanities-trained critical thinking and practical systems engineering, translating complex requirements into robust, debuggable code. He emphasizes observability and reproducibility, adding targeted logging and refactors that reduce operational friction in distributed search and ML components. Colleagues rely on him for thoughtful, detail-oriented improvements that make large-scale systems more reliable and maintainable.
12 years of coding experience
Doctor of Philosophy (PhD), History, Doctor of Philosophy (PhD), History at University of Newcastle
Github Skills (19)
amazon-elasticsearch10
machine-learning10
java10
javas10
elasticsearchquery10
aws-elasticsearch10
elasticsearch10
elasticsearch-api10
algorithm9
code-optimization9
algorithms9
optimisation9
numerical-optimization9
optimization9
concurrent-programming8
Programming languages (12)
Github contributions (5)
elastic/elasticsearch
Jan 2016 - Jan 2023
Free and Open Source, Distributed, RESTful Search Engine
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1237 reviews, 1521 commits, 1564 PRs in 7 years 1 month
Contributions summary:David focused on enhancing the file structure finder component within the Elasticsearch project. They made significant improvements to the timestamp format determination code, addressing issues with differentiating date formats and enabling the use of custom timestamp formats. The user also refactored code related to ML model management, refactoring code and addressing a race condition that prevented the opening of certain ML jobs on an impacted node. They optimized the model memory estimation process and integrated improved logging to aid troubleshooting.
restrestfulsearch-engineelasticsearchjava
droberts195/ml-cpp
Apr 2018 - Feb 2024
Machine learning C++ code
Contributions:369 pushes, 1155 branches in 5 years 10 months
cppmachine-learninglearning-c
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