Jan Gasthaus
Software Engineer at Meta
Germany
Summary
🤩
Rockstar🎓
Top SchoolJan Gasthaus is a software engineer and machine learning researcher with 18 years of experience building production-ready ML systems and probabilistic forecasting tools. He has deep expertise in time-series and forecasting from roles at Amazon/AWS and now Meta, and has contributed to the popular GluonTS library focusing on code quality, core distribution modules, and release preparation. Jan teaches practical forecasting techniques through industry courses, translating two decades of real-world retail and cloud-facing problems into hands-on lessons. He holds a PhD in machine learning from UCL’s Gatsby Unit and combines rigorous research foundations with a knack for maintainable, release-ready code and clear documentation. An understated strength is his ability to bridge research and engineering—turning complex probabilistic models into robust, deployable components used by practitioners.
18 years of coding experience
9 years of employment as a software developer
PhD, Machine Learning, PhD, Machine Learning at UCL, Gatsby UnitBachelor of Science, Cognitive Science, Excellent, with distinction, Bachelor of Science, Cognitive Science, Excellent, with distinction at Universität OsnabrückMaster of Science, Intelligent Systems / Machine Learning, With Distinction, Master of Science, Intelligent Systems / Machine Learning, With Distinction at University College London, U. of London
Github Skills (14)
forecasting10
machine-learning10
deeplearning-ai10
time-series10
forecast10
deep-learning10
python10
mxnet9
pytorch8
neural-network8
data-science8
artificial-neural-networks8
documentation7
artificial-intelligence7
Programming languages (3)
Github contributions (5)
awslabs/gluonts
May 2019 - Sep 2022
Probabilistic time series modeling in Python
Role in this project:
ML Engineer
ML EngineerContributions:17 reviews, 21 commits, 23 PRs in 3 years 4 months
Contributions summary:Jan primarily contributed to refactoring and updating code within the GluonTS library. Their work involved reformatting code using black, updating documentation, and preparing releases. They also made changes to core components like distribution and block modules. These updates suggest a focus on code quality, maintainability, and preparing the library for release.
pythontime-seriesdeep-learningforecastingneural-networks
jgasthaus/gluon-ts
Jun 2019 - Sep 2022
GluonTS - Probabilistic Time Series Modeling in Python
Contributions:35 pushes, 21 branches in 3 years 4 months
gluontspythontime-series
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