Joachim Van Der Herten
Co-Founder & CTO at ML2Grow
Hamme, East Flanders, Belgium
Summary
🤩
Rockstar🎓
Top SchoolJoachim Van Der Herten is a machine learning-focused technology leader and Co-Founder & CTO with 11 years of experience building AI solutions that integrate into customers' workflows. Based in Hamme, Belgium, he combines academic depth—as a PhD and current postdoc at Ghent University researching data-efficient probabilistic methods—with hands-on product engineering through his startup ML2Grow. He has practical experience hardening core ML tooling, contributing fixes and documentation to GPflow (Gaussian processes in TensorFlow) to improve numerical stability and developer experience. His background in scientific computing (Magna cum laude) and early software roles gives him a strong foundation in both research and production engineering. Colleagues value his ability to turn probabilistic research into deployable systems that reduce computational cost for complex design and optimization problems.
10 years of coding experience
Graduate, Latin-Sciences, Graduate, Latin-Sciences at Onze-lieve-vrouwecollegeErasmus Master, Computer Science, Erasmus Master, Computer Science at Aalto-yliopistoMaster’s Degree, Computer science: scientific computing, Magna cum laude, Master’s Degree, Computer science: scientific computing, Magna cum laude at Universiteit Antwerpen
English, Dutch, French
Stackoverflow
11reputation
212reached
0answers
1question
Github Skills (14)
algorithm10
machine-learning10
machine-learning-algorithms10
tensorflow10
python10
numpy10
gpflow10
gaussian-processes10
testing9
bayesian8
scipy8
bayesian-statistics8
google-cloud-composer6
google-cloud-platform6
Programming languages (5)
Github contributions (5)
GPflow/GPflow
May 2017 - Jun 2019
Gaussian processes in TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:26 commits, 8 PRs, 11 pushes in 2 years 1 month
Contributions summary:Joachim primarily contributed to improving the functionality and robustness of Gaussian Process models within the GPflow library. Their work included fixing issues related to numerical stability in the softplus transform, adding detailed documentation, and addressing recompilation issues to enhance efficiency. The user also made minor updates to the setup file to improve clarity and installation procedures. These contributions demonstrate a focus on core model implementation and optimization within the context of a machine learning library.
information-theorygpflowdeep-learningmachine-learningmarkov-chain-monte-carlo
ml2grow/GAEPyPI
Nov 2017 - Dec 2019
PyPI private package index on Google App Engine
Contributions:1 release, 4 commits, 6 PRs in 2 years
google-apppythonpypiapp-engineindex
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.