Cedric Kulbach
Wirtschaftsinformatiker
Basel, Basel-City, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolCedric Kulbach is a Wirtschaftsinformatiker with six years of experience blending research-grade machine learning and applied IT in finance and industry, currently working at the Swiss National Bank. He brings a strong academic foundation from KIT, Grenoble and a research stay at Télécom Paris, and a track record at FZI where he bridged scientific research and engineering for practical systems. Cedric has hands-on ML engineering experience integrating PyTorch models into the well-regarded online-ml/river framework, enabling neural nets to operate in streaming learning workflows. His background in industrial engineering and logistics (including a master’s thesis at Bugatti) gives him a practical optimization mindset that complements his software and research skills. Based in Basel, he combines rigorous research experience with production-focused implementations, making him effective at turning complex models into deployable solutions.
6 years of coding experience
Forschungsaufenthalt, Computer Science, Forschungsaufenthalt, Computer Science at Télécom ParisMaster, Génie industriel, Master, Génie industriel at Institut polytechnique de GrenobleMaster, Wirtschaftsingenieurwesen, Master, Wirtschaftsingenieurwesen at Karlsruher Institut für Technologie (KIT)
Github Skills (9)
incremental-learning10
pytorch10
machine-learning10
deep-learning10
python10
elearning10
data-science9
streaming9
drift9
Programming languages (2)
Github contributions (5)
online-ml/river
Oct 2021 - Jan 2023
🌊 Online machine learning in Python
Role in this project:
ML Engineer
ML EngineerContributions:8 reviews, 10 commits, 20 PRs in 1 year 3 months
Contributions summary:Cedric's primary focus was integrating PyTorch neural networks into the `river` online machine learning framework. This involved creating compatibility layers, including `PyTorch2RiverBase`, `PyTorch2RiverClassifier`, and `PyTorch2RiverRegressor` classes. They implemented methods for model initialization, training (`learn_one`, `learn_many`), and prediction, enabling the use of PyTorch models within the `river` ecosystem. Further contributions included refactoring, documentation updates, and fixing issues related to classifier functionality.
stream-processingpythonstreaming-datadata-scienceonline-statistics
online-ml/deep-river
Nov 2021 - Jan 2023
Contributions:28 releases, 5 reviews, 231 commits in 1 year 2 months
pytorchoutlier-detectiondata-sciencedeep-learningmachine-learning
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