Gilles Vandewiele
Chief Product Officer at Optioryx
Ghent, East Flanders, Belgium
Summary
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Rockstar🎓
Top SchoolGilles Vandewiele is Chief Product Officer at Optioryx with 11 years of experience combining AI research and product leadership to commercialize optimization solutions for packing, palletizing and assortment planning. He holds a PhD in Artificial Intelligence from Ghent University and transitioned from postdoctoral research into product strategy, bridging rigorous ML research with practical logistics impact. An active contributor to the tslearn time-series ML toolkit, he brings hands-on expertise in algorithms, clustering and reproducible testing consistent with sklearn standards. Based in Ghent, Belgium, Gilles is fluent in turning complex data and algorithmic advances into usable product features that reduce transport waste—an uncommon blend of academic depth and startup execution.
11 years of coding experience
1 year of employment as a software developer
Doctor of Computer Science Engineering Artificial Intelligence, Doctor of Computer Science Engineering Artificial Intelligence at Ghent Universityeconomics - mathematics, economics - mathematics at KA Vijverhof
Dutch, English, west-flemish, python
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (9)
algorithm10
scikit10
machine-learning10
time-series10
machine-learning-algorithms10
python10
clustering10
scikit-learn10
testing9
Programming languages (7)
Github contributions (5)
tslearn-team/tslearn
Jul 2019 - Dec 2020
The machine learning toolkit for time series analysis in Python
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:3 reviews, 219 commits, 20 PRs in 1 year 5 months
Contributions summary:Gilles significantly contributed to the `tslearn-team/tslearn` repository, focusing on enhancing the time series machine learning toolkit. Their work involved implementing tests and ensuring that the project conformed to the standards of other machine learning libraries. Specifically, they added and integrated several features related to machine learning clustering techniques, and fixed bugs related to the application of these machine learning clustering techniques in order to maintain consistency with sklearn.
dtwdynamic-time-warpingforecastingpythontime-series-analysis
GillesVandewiele/tslearn
Jul 2019 - Dec 2020
A machine learning toolkit dedicated to time-series data
Contributions:167 pushes, 6 branches in 1 year 5 months
forecastingdedicatedmachine-learningseries-datatime-series
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