Christian Widmer
Senior Staff Software Engineer at Google
Los Angeles, California, United States
Summary
👤
Senior🎓
Top SchoolChristian Widmer is a Senior Staff Software Engineer at Google with 18 years of experience building large-scale machine learning systems that span statistical modeling, production engineering, and data-heavy computation. He holds a PhD in machine learning and computational biology and has applied those skills across academia (Max Planck, TU Berlin, MSKCC), Microsoft Research, and industry roles at Activision Blizzard where he led ML efforts to boost engagement and monetization. Deeply hands-on in Python and C++, he has a track record of turning advanced algorithms into reliable software (author of pythongrid) and contributing to the Shogun toolbox with model serialization and domain-adaptive SVMs. Comfortable in cluster environments and production infrastructures, he blends rigorous research instincts with pragmatic engineering to ship scalable, persistent ML solutions.
18 years of coding experience
21 years of employment as a software developer
Diplom Computer-Science (Bioinformatics), Diplom Computer-Science (Bioinformatics) at University of TübingenPhD CS / Machine Learning / Bioinformatics, PhD CS / Machine Learning / Bioinformatics at FML Max Planck, MSKCC, TU-Berlin
German, English, Spanish, French
Github Skills (9)
machine-learning10
data-serialization10
c-language10
cprogramming-language10
serialization10
data-science9
cplus9
adaptation9
cpp9
Programming languages (3)
Github contributions (5)
shogun-toolbox/shogun
Mar 2009 - Nov 2012
Shōgun
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:117 commits in 3 years 8 months
Contributions summary:Christian contributed to the Shōgun machine learning toolbox by adding serialization support for core classes, allowing for saving and loading of model states. They implemented serialization for base classes like `SGObject`, and features like `StringFeatures` and `Alphabet`. Additionally, the user added support for Domain Adaptive SVMs including a full implementation. This indicates a focus on extending the library's capabilities for model persistence and advanced machine learning techniques.
cmakedata-sciencegunc-plus-plusmachine-learning
cwidmer/mtl_dcd_ecml
Jun 2012 - Jan 2014
Contributions:6 commits in 1 year 7 months
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