Jean-michel Perraud
Software Architect at CSIRO
Canberra, Australia
Summary
👤
Senior🎓
Top SchoolJean-michel Perraud is a software architect based in Canberra with 13+ years building large-scale environmental modelling and hydrology simulation systems. At CSIRO he has led architecture and development for continental-scale calibration, ensemble Kalman filter data assimilation, and workflow tools that underpin commercial products like Source. Comfortable in numerically complex domains, he gravitates toward problems that demand novel software interoperability and has contributed practical interop work in projects such as rdotnet and the Mono toolchain. His open-source activity includes refining educational materials for the tsai time-series library, reflecting an attention to clear documentation and user experience. Trained at Ecole Centrale Paris, he combines multidisciplinary systems thinking with hands-on backend engineering to bridge models, data, and production software.
13 years of coding experience
Masters degree, Multi-disciplinary; Management of information systems, Masters degree, Multi-disciplinary; Management of information systems at Ecole Centrale ParisClasse preparatoires, Lycee Champollion
English, French, German, Russian
Stackoverflow
1,503reputation
154kreached
49answers
1question
Github Skills (26)
interoperability10
msbuild10
r10
net10
dotnet10
time-series10
vector10
asp-net10
dotnet-core10
csharp10
documentation10
data-structure9
visual-studio9
performance-optimization9
data-structures9
Programming languages (17)
Github contributions (5)
rdotnet/rdotnet
Aug 2012 - Jul 2021
.NET interop library to call the R statistical language in the same process
Role in this project:
Back-end Developer
Back-end DeveloperContributions:315 commits, 13 PRs, 149 pushes in 9 years
Contributions summary:Jean-michel's contributions focus on enhancing the initialization process of the R engine within the .NET interop library. They implemented options to adapt the initialization when the R library is pre-loaded in the main application and improved the performance of initializing vectors at creation time. Additionally, the user addressed visibility requirements for data frame tests, suggesting a focus on the library's core functionality and performance optimization.
dotnetembedded-scripting-languagecffistatisticalinteroperability
timeseriesAI/tsai
Jul 2022 - Jul 2022
Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
Role in this project:Technical Writer
Technical WriterContributions:6 commits, 1 PR, 1 comment in 1 day
Contributions summary:Jean-michel's contributions primarily involve minor spelling and grammar corrections across multiple tutorial notebooks within the `tsai` repository. These changes are focused on improving the clarity and readability of the documentation, indicating a focus on refining the educational content. The user appears to be enhancing the user experience by ensuring the notebooks are free of grammatical errors. This work benefits the learning process for others using the `tsai` library.
forecastingpythontime-series-analysisstate-of-the-arttime-series
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