Wannes Meert
Member at Leuven.AI
Leuven, Flemish Brabant, Belgium
Summary
🤩
Rockstar🎓
Top SchoolWannes Meert is a research manager and seasoned machine learning scientist with 15 years of experience leading industrial AI projects from KU Leuven’s Declarative Languages and AI group and as a member of Leuven.AI. His work bridges statistical relational learning, probabilistic (logic) programming and pragmatic applications of graphical models, with a PhD in machine learning and dual masters in microelectronics and AI. He combines academic leadership and hands-on engineering—contributing performance-critical C implementations of Dynamic Time Warping for n-dimensional time series to open-source—to make probabilistic methods production-ready. Colleagues know him for turning formal reasoning techniques into scalable solutions for industry settings while maintaining strong ties to research and publications.
15 years of coding experience
9 years of employment as a software developer
Master in Engineering, Microelectronics, Master in Engineering, Microelectronics at KU LeuvenMachine Learning, Statistical Learning, Machine Learning, Statistical Learning at Machine Learning Summer School, ANU, Canberra
Github Skills (10)
c1710
algorithm10
numerical-optimization10
code-optimization10
algorithms10
c1110
optimisation10
optimization10
openmp7
numpy5
Programming languages (11)
Github contributions (5)
wannesm/dtaidistance
Oct 2016 - Jan 2023
Time series distances: Dynamic Time Warping (fast DTW implementation in C)
Role in this project:
Back-end Developer
Back-end DeveloperContributions:8 releases, 701 commits, 33 PRs in 6 years 4 months
Contributions summary:Wannes made significant improvements to the C implementations of the Dynamic Time Warping (DTW) algorithm, focusing on optimizations and bug fixes, particularly related to the handling of memory and matrix operations. They added support for n-dimensional series data structures and implemented a more efficient version of the warping path computation. The contributions centered around enhancing the performance and Windows compatibility of the DTW algorithm in C.
dtwdynamic-time-warpingdistancespythontime-dynamic
ML-KULeuven/PySDD
Mar 2018 - Jan 2023
Contributions:1 release, 213 commits, 17 PRs in 4 years 11 months
diagramspythonweighted-model-countingdecisionsdd
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