Doug Coleman
Principal Software Engineer at GLG
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolDoug Coleman is a Principal Software Engineer in Austin with 19 years of experience building robust backend systems and improving ML libraries. Currently leading architecture and delivery at GLG, he progressed from senior engineer to principal while focusing on system stability and scalable service design. His open-source contributions include deep work on the Factor programming language’s parser and VM internals and targeted fixes to scikit-learn’s random forest and covariance code, showing strength across language runtimes and machine learning tooling. With an electrical engineering foundation from UT Austin and past experience in test engineering at Apple, he combines low-level systems insight with practical quality and performance improvements. Colleagues rely on him for tough bug hunts that bridge parsing, concurrency, and numerical correctness.
19 years of coding experience
8 years of employment as a software developer
Electrical Engineering, Electrical Engineering at The University of Texas at Austin
Stackoverflow
111reputation
64kreached
2answers
1question
Github Skills (22)
parser10
python10
scikit10
machine-learning10
numpy10
scikit-learn10
random-forest10
lang9
data-analysis9
syntax9
statistics8
code-optimization8
testing7
boost6
xcode6
Programming languages (23)
Github contributions (5)
factor/factor
May 2009 - Jan 2023
Factor programming language
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 reviews, 5197 commits, 140 PRs in 13 years 10 months
Contributions summary:Doug contributed to the Factor programming language project by implementing and refining code related to parsing, language syntax, and system components. This involved cleaning up existing code, improving the parsing of various language constructs, and enhancing the stability and functionality of the virtual machine. The user's work also included addressing specific issues and improving the overall robustness of the codebase.
script-enginecompilerprogramming-languagecompiledapl
scikit-learn/scikit-learn
Dec 2012 - Jul 2013
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:18 commits, 2 PRs, 9 comments in 7 months
Contributions summary:Doug primarily contributed to bug fixes and code improvements within the scikit-learn library, focusing on issues related to machine learning algorithms. Their work involved correcting errors in random state handling, resolving parallel processing issues in random forests, and optimizing code related to random forests and covariance calculations. The user's contributions directly improved the functionality and performance of core machine learning components.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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