Ibrahim Ganiev
Student at 42 Abu Dhabi
United Arab Emirates
Summary
👤
Senior🎓
Top SchoolIbrahim Ganiev is a pragmatic C++ developer and student based in the United Arab Emirates with 11 years of professional experience spanning technical, managerial, and customer-facing roles. He combines hands-on engineering—demonstrated by substantive contributions to scikit-learn such as sparse-matrix support, parallel VotingClassifier, and decision-tree fixes—with business leadership as a current Business Manager. Comfortable moving between low-level technical problem solving and operations, he previously led technical support as a chief technician and started his career in service roles that sharpened communication and teamwork. Now studying at 42 Abu Dhabi, he’s focused on deepening software engineering skills while bringing a rare blend of open-source ML library experience and practical management acumen to early engineering or technical leadership positions.
11 years of coding experience
42 Abu DhabiМосковский Государственный Технический Университет Гражданской Авиации (МГТУ ГА)
Russian, English
Stackoverflow
9,202reputation
794kreached
109answers
22questions
dataframe
top-5%
scikit-learn
top-1%
python
top-1%
machine-learning
top-1%
statistics
top-5%
classification
top-5%
Github Skills (26)
python10
scikit10
sparse-matrix10
machine-learning10
scikit-learn10
testing9
pandas9
dataframe9
classification9
statistics9
data-analysis9
gradient-boosting8
data-science8
algorithm7
algorithms7
Programming languages (13)
Github contributions (5)
scikit-learn/scikit-learn
Aug 2015 - Oct 2016
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:13 commits, 15 PRs, 92 comments in 1 year 2 months
Contributions summary:Ibrahim's commits primarily focus on modifications related to the Scikit-learn library, a machine learning library in Python. Their contributions include adding tests for sparse matrix support within OneClassSVM, fixing issues related to the GradientBoosting decision function for sparse inputs, and fixing and improving the cloning process for the decision tree criterion. The user also addressed an overflow error in the dataset loading function and implemented the parallelization of VotingClassifier and sample weight support. They demonstrate a strong understanding of the library's internal workings and its integration with sparse data formats.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
olologin/serialization
Feb 2017 - Jul 2023
Boost.org serialization module
Contributions:1 PR, 1 push, 1 branch in 6 years 5 months
serializationboost
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