Jan Ernsting
Wissenschaftlicher Mitarbeiter at Universität Münster
Münster, North Rhine-Westphalia, Germany
Summary
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Rockstar🎓
Top SchoolJan Ernsting is a research-oriented software engineer and PhD candidate in Computer Science at Universität Münster with 11 years of hands-on experience in machine learning and data science. As a wissenschaftlicher Mitarbeiter, he applies rigorous research methods to practical ML engineering tasks, notably contributing to graph embedding implementations and upgrading Keras integrations in open-source repositories. His work blends test-driven development, bug fixing, and model validation across diverse graph datasets, highlighting a focus on reproducibility and robust experimental pipelines. Based in Münster, Germany, he combines academic depth with production-aware engineering and a track record of adapting research code for broader usability.
11 years of coding experience
Doktor (Ph.D.), Doktor (Ph.D.) at Universität MünsterMaster of Science - MS, Master of Science - MS at Westfälische Wilhelms-Universität Münster
Github Skills (6)
machine-learning10
graph-embedding10
tensorflow10
python10
network-analysis10
testing9
Programming languages (13)
Github contributions (5)
palash1992/GEM
Feb 2022 - Oct 2022
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:1 review, 39 commits, 2 PRs in 8 months
Contributions summary:Jan primarily contributed to the testing and implementation of graph embedding methods within the repository. This included updating existing tests, fixing bugs, and ensuring the methods functioned correctly on different graph datasets. The user also refactored and adapted existing tests to validate the implemented models' performance. Furthermore, the user worked on incorporating the integration of `tf.keras` to upgrade the underlying Keras version.
network-embeddingnetwork-visualizationlink-predictiongraph-embeddingsgraph-embedding
jernsting/poitools
Jun 2020 - Jul 2022
Contributions:1 release, 12 pushes, 1 tag in 2 years 1 month
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