Daniel Gibbons
Senior Software Engineer at DEWC Services
Adelaide, South Australia
Summary
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Rockstar🎓
Top SchoolDaniel Gibbons is a Senior Software Engineer based in Adelaide with four years of professional experience bridging research and applied machine learning. He has held roles from cadet researcher to lead ML engineer and now delivers production-grade solutions at DEWC Services, drawing on an MPhil in Electrical and Electronics Engineering and dual bachelors in math/computer science and music. Daniel contributes to high-profile open-source ML tooling—fixing reliability and explainability issues in MLflow and SHAP—showing a focus on reproducible evaluation and visualization. Comfortable moving between research-grade experiments and hardened engineering, he brings a track record of pragmatic bug fixes and refactors that improve core functionality in widely used ML libraries. An uncommon mix of technical depth and creative training in music informs his analytical yet human-centred approach to problem solving.
4 years of coding experience
5 years of employment as a software developer
Master of Philosophy, Electrical and Electronics Engineering, Master of Philosophy, Electrical and Electronics Engineering at University of Adelaide
Github Skills (21)
shap10
python10
pandas10
machine-learning10
mlflow10
model-management10
numpy10
explainable-artificial-intelligence10
pytest9
dataframe9
dataframes9
deeplearning-ai9
deep-learning9
interpretation8
ai8
Programming languages (8)
Github contributions (5)
shap/shap
Nov 2022 - Sep 2023
A game theoretic approach to explain the output of any machine learning model.
Role in this project:
ML Engineer
ML EngineerContributions:29 reviews, 20 PRs, 29 pushes in 10 months
Contributions summary:Daniel primarily focused on refactoring and updating code related to the explainability of machine learning models. Their commits addressed deprecation warnings within the codebase, specifically targeting NumPy-related issues. They also made changes to plotting functionalities, removing dependencies and resolving issues related to the visualization of SHAP values, demonstrating expertise in the core functionality of the library.
explaininterpretabilityshapdeep-learningapproach
mlflow/mlflow
Aug 2022 - Sep 2022
Open source platform for the machine learning lifecycle
Role in this project:ML Engineer
ML EngineerContributions:23 reviews, 5 commits, 7 PRs in 16 days
Contributions summary:Daniel primarily contributed to bug fixes and improvements within the MLflow project. Their work addressed issues related to handling non-string column names in evaluation datasets and fixing errors associated with the use of set literals. Additionally, the user resolved truncation errors within the explainable evaluator, enhancing its usability. These contributions demonstrate a focus on improving the reliability and functionality of core MLflow components.
pythonlifecyclemlmachine-learningincremental-learning
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