Behzad Tabibian
Machine Learning Scientist at Amazon
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolBehzad Tabibian is a Machine Learning Scientist with 14 years of experience blending rigorous research and product-focused ML, currently applying his expertise at Amazon in Mountain View. He holds a PhD in Machine Learning from the Max Planck Society and has led research and commercialization efforts as Chief Scientist and co-founder of Reasonal Inc. His background spans academic research (Max Planck, Facebook collaboration), industry internships and roles at Amazon, and a strong foundation in statistics and network-focused ML applications. An active contributor to scikit-learn, he has improved ElasticNet’s handling of sparse and multiple-output cases, highlighting a practical attention to correctness and robustness in widely used open-source tooling. Colleagues would describe him as a scientist who moves fluidly between deep theory and production-grade implementation.
15 years of coding experience
5 years of employment as a software developer
Bachelor of Science, Computer Science, Bachelor of Science, Computer Science at The University of EdinburghDoctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at Max Planck SocietyMaster of Science (M.S.), Computer and Information Sciences, General, Master of Science (M.S.), Computer and Information Sciences, General at University of Pittsburgh
Stackoverflow
16reputation
13kreached
1answer
0questions
Github Skills (12)
scikit10
linear-models10
machine-learning10
python10
data-science10
scikit-learn10
testing9
sparse-matrix8
numpy8
data-analysis7
sift6
image-processing6
Programming languages (8)
Github contributions (5)
scikit-learn/scikit-learn
Jul 2016 - Aug 2016
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:8 commits, 4 PRs, 46 comments in 24 days
Contributions summary:Behzad focused on improving and testing the ElasticNet model within the scikit-learn library. They addressed issues related to sparse matrix handling and decision function outputs, ensuring consistency with dense output. Furthermore, the user contributed by adding test cases for multiple output scenarios, verifying the model's behavior across sparse and dense data representations. The changes reflect a focus on the correctness and robustness of the linear model implementation.
machine-learningpythonscikit-learnstatisticsdata-science
btabibian/btabibian.github.io
Jan 2015 - Apr 2021
Contributions:1 PR, 322 pushes, 1 branch in 6 years 4 months
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