Jesper Van Stee
Senior Technology Specialist at Valcon
Leiden, South Holland, Netherlands
Summary
👤
Senior🎓
Top SchoolJesper Van Stee is a Senior Technology Specialist with 12 years of experience bridging software engineering and machine learning, currently delivering infrastructure-focused solutions for a major client at Valcon in the Netherlands. He holds a BSc in Computer Science from Leiden University with an exchange at CityU Hong Kong and an MSc in Data Science and Artificial Intelligence from the University of Antwerp, grounding him in both theory and applied ML. Jesper has research experience in reinforcement learning, recommender systems, clustering, and NLP, and has contributed to the well-known auto-sklearn AutoML project by improving tree-based model implementations and fixing memory calculation issues. Comfortable moving between research and production, he combines hands-on engineering with careful model and hyperparameter tuning to make ML systems robust in real-world environments. Based in Leiden, he brings a global academic background and practical open-source impact to enterprise technology engagements.
12 years of coding experience
Master Data Science and Artificial Intelligence, Computer Science, Master Data Science and Artificial Intelligence, Computer Science at University of AntwerpBachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Leiden UniversityBachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at City University of Hong Kong
Github Skills (13)
scikit10
automated-machine-learning10
random-forest10
python10
scikit-learn10
hyperparameter-optimization9
testing8
optimisation7
optmization7
automl7
optimization7
git7
machine-learning7
Programming languages (6)
Github contributions (5)
automl/auto-sklearn
Oct 2017 - Feb 2018
Automated Machine Learning with scikit-learn
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 2 PRs, 18 comments in 3 months
Contributions summary:Jesper primarily contributed to the auto-sklearn project by implementing and modifying components related to machine learning algorithms, particularly random forests and extra trees. Their work included modifying hyperparameters and default values in the configuration of these models. The user also made changes to test files, updating default result values. Furthermore, they integrated updates from the upstream development branch and fixed memory calculation issues.
pythonmeta-learningmetalearningbayesian-optimizationhyperparameter-tuning
jsmlt/visualml
Sep 2017 - Oct 2017
Contributions:74 commits, 52 pushes, 1 branch in 21 days
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