Timon Erhart
Computer Scientist at OST University, Switzerland
Summary
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RockstarTimon Erhart is a computer scientist and data scientist with six years of experience blending research and applied machine learning. Based in Switzerland, he contributes to academic research at IFS-HSR OST University Rapperswil while also leading the Python community as president of the python-summit association. His open-source work spans documentation and tooling improvements for notable projects like darts (time-series forecasting) and fastai, where he enhanced plotting and usability in core training utilities. Timon brings a pragmatic focus on clarity and reproducibility—evident from technical writing that makes complex libraries more approachable—paired with hands-on ML engineering that improves developer workflows.
6 years of coding experience
Github Skills (8)
pytorch10
machine-learning10
fastai10
deep-learning10
python10
documentation10
matplotlib10
notebook9
Programming languages (19)
Github contributions (5)
unit8co/darts
Jan 2023 - Mar 2025
A python library for user-friendly forecasting and anomaly detection on time series.
Role in this project:
Technical Writer
Technical WriterContributions:5 reviews, 6 PRs, 35 comments in 2 years 2 months
Contributions summary:Timon primarily focused on improving the documentation within the `darts` repository. Their contributions involved renaming documentation files, correcting references to existing notebooks, fixing formatting issues, and clarifying docstrings. They also added a function to the documentation and fixed a link in the quickstart guide. The user's work improved the clarity and usability of the project documentation.
forecastingpython-libraryanomalypythontime-series-analysis
fastai/fastai
Sep 2023 - Feb 2024
The fastai deep learning library
Role in this project:ML Engineer
ML EngineerContributions:1 PR, 4 comments, 1 issue in 5 months
Contributions summary:Timon made several contributions to the `fastai` library, primarily focusing on improving the `plot_loss` function within the `Recorder` callback. They enhanced the functionality by adding options for logarithmic axes and epoch visualization, and made adjustments to the plot function's flexibility by including ax input. Additionally, the user performed an nbdev_export and merged upstream changes, reflecting an involvement in the library's development and maintenance.
pytorchpythondeep-learninggpumachine-learning
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