Ben Hamner
Co-founder & CTO at Sumble
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolBen Hamner is a data-driven technology leader and co-founder/CTO with 14 years of experience building machine learning products and companies from San Francisco. He helped scale Kaggle into a hub for data science competition and now leads engineering at Sumble, blending product vision with hands-on ML and systems work. His background in biomedical engineering and neural signal processing at EPFL informs a pragmatic approach to noisy real-world data and robust probabilistic models. An active open-source contributor, Ben authored cross-language evaluation tools (notably the widely used Metrics repo implementing QWK and many ML metrics), reflecting a focus on rigorous model evaluation. He combines deep technical breadth—from signal processing to production ML—with entrepreneurial instincts for turning data into actionable learning.
14 years of coding experience
11 years of employment as a software developer
B.S.E., Biomedical Engineering, Electrical and Computer Engineering, Mathematics, Economics, B.S.E., Biomedical Engineering, Electrical and Computer Engineering, Mathematics, Economics at Duke UniversityWhitaker Fellow, Bioengineering and Biomedical Engineering, Whitaker Fellow, Bioengineering and Biomedical Engineering at Ecole polytechnique fédérale de Lausanne
Github Skills (8)
machine-learning10
auc10
python10
evaluation10
metric10
r9
matlab9
mse9
Programming languages (10)
Github contributions (5)
benhamner/Metrics
Jun 2012 - Sep 2015
Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave
Role in this project:
Data Scientist
Data ScientistContributions:215 commits, 7 PRs, 6 pushes in 3 years 3 months
Contributions summary:Ben primarily contributed to the project by implementing and testing evaluation metrics commonly used in machine learning. They added implementations of the quadratic weighted kappa (QWK) metric in both R and Python. Furthermore, they added several other metrics in MATLAB, including AUC, MAE, MSE, RMSE, AP@K, MAP@K, MSLE, RMSLE, LL, and classification error. The contributions show a focus on providing a comprehensive suite of evaluation tools.
haskellmachine-learningmatlaboctavepython
benhamner/GEFlightQuest
Dec 2012 - Aug 2013
Contributions:104 commits in 8 months
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