Christian Osendorfer
Senior Applied Scientist at Amazon
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolChristian Osendorfer is a Senior Applied Scientist in Mountain View with 18 years of hands-on experience marrying deep learning research and software engineering. He holds a summa cum laude Ph.D. from TUM in deep learning and has applied unsupervised and metric learning techniques across computer vision and large-scale systems at organizations from academic labs to Amazon and X. A pragmatic researcher, he has contributed to widely used open-source tooling—improving scikit-learn’s Factor Analysis for numerical stability, testing, and usability—bridging research rigor with production needs. His background spans signal and time-series analysis, medical and automotive data, and real-time system development, reflecting a knack for transferring methods across domains. Colleagues rely on him for robust model implementations and mentoring, informed by supervising dozens of theses and a long track record of shipping ML systems.
18 years of coding experience
18 years of employment as a software developer
Dr. rer. nat. (Ph.D.), Computer Science, Machine Learning, Deep Learning, Summa cum Laude, Dr. rer. nat. (Ph.D.), Computer Science, Machine Learning, Deep Learning, Summa cum Laude at Technical University MunichDipl. Inf., Computer Science, Mathematics, 1.3, Dipl. Inf., Computer Science, Mathematics, 1.3 at Technical University of Munich
Stackoverflow
818reputation
78kreached
5answers
8questions
Github Skills (17)
python10
scikit10
machine-learning10
numpy10
scikit-learn10
data-analysis10
linear-algebra9
testing8
statistics7
pooling6
lisp6
neural-network6
vim6
common-lisp6
ide6
Programming languages (10)
Github contributions (5)
scikit-learn/scikit-learn
Dec 2010 - Oct 2012
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:17 commits in 1 year 10 months
Contributions summary:Christian significantly contributed to the Factor Analysis (FA) implementation within the scikit-learn library. They fixed numerical stability issues, implemented the FA model, and wrote tests to ensure its functionality. Their contributions included adding documentation, plots, and a scoring function to assess the model's performance, demonstrating a focus on both model development and practical usability.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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