Elle O'Brien
Research Scientist at umsi
Ann Arbor, Michigan, United States
Summary
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RockstarElle O'Brien is a research scientist and faculty member at the University of Michigan School of Information with eight years of experience applying machine learning to real-world prediction problems. She blends academic rigor with hands-on data science—building and iteratively tuning Random Forest models while using visualizations to surface feature importance and model residuals. Her work emphasizes interpretable, performance-driven modeling that translates research insights into usable tools. Based in Ann Arbor, she routinely bridges scholarly research and practical implementation, favoring iterative experiments to continuously improve model behavior.
8 years of coding experience
Github Skills (12)
scikit10
data-visualizations10
pandas10
machine-learning10
data-visualization10
data-visualisation10
seaborn10
python10
matplotlib10
scikit-learn10
evaluation9
eval9
Programming languages (9)
Github contributions (5)
elleobrien/wine
Jul 2020 - Nov 2020
wine prediction dataset
Role in this project:
Data Scientist
Data ScientistContributions:16 commits, 5 PRs, 12 pushes in 3 months
Contributions summary:Elle primarily focused on building and refining a machine learning model for wine quality prediction. Their contributions involved data preparation, model training using Random Forest Regressor, and evaluating model performance. The user also implemented data visualization techniques to display feature importances and residuals, gaining insights into the model's behavior and performance. The iterative nature of the commits reveals an emphasis on model tuning and performance improvement.
winedatasetmachine-learningprediction
iterative/cml_cloud_case
Jun 2020 - Feb 2021
Contributions:41 commits, 15 PRs, 41 pushes in 8 months
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