Elle O'brien
Graduate Data Science Certificate Program Director
Ann Arbor, Michigan, United States
Summary
🤩
Rockstar🎓
Top SchoolElle O'brien is a data scientist and academic leader with eight years of experience bridging generative AI research, neuroscience-informed machine learning, and curriculum development. As Graduate Data Science Certificate Program Director at the Michigan Institute for Data and AI in Society and a Lecturer/Research Investigator at the University of Michigan, she studies adoption of generative AI in scientific workflows while training the next generation of data practitioners. Her background—PhD in Speech and Hearing Science and research on auditory processing and reading development—gives her a rare blend of domain expertise in signal processing, Bayesian methods, and applied ML. A hands-on contributor to open-source ML tooling (co-creator of CML) and practical projects like a wine quality prediction model, she combines reproducible engineering with rigorous empirical inquiry. Colleagues describe her work as both deeply technical and pedagogically grounded, able to translate complex research into usable tools and courses.
8 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD Speech and Hearing Science, Doctor of Philosophy - PhD Speech and Hearing Science at University of WashingtonBachelor’s Degree Mathematics, Bachelor’s Degree Mathematics at Agnes Scott College
French, English
Github Skills (12)
scikit-learn10
data-visualizations10
pandas10
machine-learning10
data-visualization10
data-visualisation10
seaborn10
python10
matplotlib10
scikit10
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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