Kerem Turgutlu
Machine Learning Software Engineer at Google
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolKerem Turgutlu is a Machine Learning Software Engineer with a decade of experience building production and research ML systems across Google, Adobe, and health-care research at UCSF. He combines applied ML in search and Sensei products with hands-on model interpretation work—co-authoring tools for permutation and drop-column feature importances and contributing to the fast.ai ecosystem. His research includes multi-task 3D convolutional networks for tumor segmentation and survival prediction, alongside classical baselines using Random Forests and logistic regression. At startups and consultancies he translated models into business value, delivering measurable savings for insurance clients. Based in San Francisco, he balances R&D at AnswerDotAI with active open-source contributions, reflecting a pragmatic bridge between research, production engineering, and machine-learning interpretability. A lifelong learner and fast.ai collaborator, he brings both academic rigor and product-minded execution to complex ML problems.
9 years of coding experience
5 years of employment as a software developer
Master of Science - MS, Analytics, Master of Science - MS, Analytics at University of San FranciscoBachelor of Engineering - BE, Industrial Engineering, Bachelor of Engineering - BE, Industrial Engineering at Istanbul Technical UniversityUndergraduate - Exchange, Management Science, Undergraduate - Exchange, Management Science at University of Waterloo
Turkish, English
Stackoverflow
11reputation
1kreached
0answers
1question
Github Skills (13)
scikit10
data-visualizations10
machine-learning10
data-visualization10
data-visualisation10
random-forest10
python10
scikit-learn10
pandas9
xmlhttprequest6
excel6
webscraping6
vba6
Programming languages (10)
Github contributions (5)
parrt/random-forest-importances
Mar 2018 - Mar 2018
Role in this project:
Data Scientist
Data ScientistContributions:15 commits, 13 pushes in 3 days
Contributions summary:Kerem appears to have primarily focused on enhancing and refining the analysis of machine learning models, particularly in the realm of permutation importance. Their work includes the implementation and correction of plotting functionalities for feature importances. The user also addressed issues related to plotting and incorporated improvements for visualization. This indicates a focus on model interpretation and analysis.
pythondata-sciencemachine-learningscikit-learnscikit-learn-python
KeremTurgutlu/deeplearning
Dec 2017 - Sep 2018
Contributions:55 commits, 124 pushes, 1 branch in 9 months
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