Hilde Weerts
Machine Learning Engineer at Eindhoven University of Technology
Eindhoven, North Brabant, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolHilde Weerts is a machine learning engineer with nine years of experience bridging academic research and industry-grade ML solutions, currently based in Eindhoven. She holds a PhD and graduated cum laude throughout her studies, combining deep theoretical rigor with practical deployments in predictive maintenance, fraud detection, and product quality prediction. At Eindhoven University of Technology she contributed to AI research and teaching, while industry roles at Canon, Rabobank and startups sharpened her applied ML and data engineering skills. An active contributor to the Fairlearn project’s documentation, she demonstrates attention to fairness, reproducibility and clear developer guidance—traits that complement her technical work. Colleagues describe her as both a meticulous communicator and a practitioner who turns interpretable models into actionable decision support.
9 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Cum Laude, Doctor of Philosophy - PhD, Cum Laude at Eindhoven University of TechnologyGymnasium, Nature, Technology & Health, Cum Laude (Average Grade: 8.7/10), Gymnasium, Nature, Technology & Health, Cum Laude (Average Grade: 8.7/10) at Raayland College Venray
Github Skills (7)
documentation10
sphinx9
python7
ai5
n5
f5
machine-learning5
Programming languages (6)
Github contributions (5)
fairlearn/fairlearn
Jul 2020 - Nov 2022
A Python package to assess and improve fairness of machine learning models.
Role in this project:
Technical Writer
Technical WriterContributions:1 release, 276 reviews, 8 commits in 2 years 4 months
Contributions summary:Hilde primarily contributed to the project's documentation. Their commits focused on improving the contributor guide, fixing API documentation entries, and updating the copyright information. They also replaced the logo and fixed source links within the documentation, showcasing an attention to detail and commitment to providing clear and accurate information for other users and contributors.
python-packagefairness-assessmentfairness-mlpythongroup-fairness
hildeweerts/responsiblemachinelearning
Jan 2022 - Feb 2025
Lecture notes responsible data science.
Contributions:2 PRs, 47 pushes, 5 branches in 3 years 1 month
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