Christos Aridas
AI Engineering Lead at Satways Ltd
Greece
Summary
🤩
Rockstar🎓
Top SchoolChristos Aridas is an AI Engineering Lead based in Greece with 11 years of experience advancing machine learning from research to production. He has led R&D and built a proprietary AI audit platform that evaluates model performance, fairness and robustness, while mentoring teams and supporting business development as a subject-matter expert. His hands-on background spans NLP systems, entity recognition and similarity search for legal documents, plus practical ML engineering with Docker and AWS. An active open-source contributor, he has improved documentation and implemented algorithmic refinements in high-profile libraries like scikit-learn and imbalanced-learn, including a SMOTE refactor and an inductive clustering example. Holding a PhD in Machine Learning and an MSc in Information Systems, he combines rigorous academic foundations with a pragmatic, problem-first mentality summed up by his GitHub motto: “Solving problems no matter what.”
11 years of coding experience
1 year of employment as a software developer
Bachelor of Science Statistics, Bachelor of Science Statistics at Athens University of Economics and BusinessMaster of Science Information Systems, Master of Science Information Systems at Hellenic Open UniversityDoctor of Philosophy Machine Learning, Doctor of Philosophy Machine Learning at University of Patras
Github Skills (8)
scikit10
scikit-learn10
statistics10
machine-learning10
python10
data-science10
data-analysis10
documentation9
Programming languages (6)
Github contributions (5)
scikit-learn-contrib/imbalanced-learn
Jun 2016 - Feb 2021
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
Role in this project:
Data Scientist
Data ScientistContributions:21 reviews, 53 commits, 75 PRs in 4 years 8 months
Contributions summary:Christos primarily contributed to refactoring and enhancing the SMOTE (Synthetic Minority Oversampling Technique) implementation within the imbalanced-learn library. Their work involved modifying initialization processes, refining the sampling logic, and addressing internal method calls to improve code structure and maintainability. The changes focused on core oversampling techniques and related internal library features. The user also worked on ensuring the code was compatible with the library's existing structure.
data-analysispythonstatisticstackledata-science
scikit-learn/scikit-learn
Oct 2016 - Dec 2021
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:9 reviews, 22 commits, 24 PRs in 5 years 2 months
Contributions summary:Christos primarily contributed to documentation improvements and bug fixes within the scikit-learn library. Their commits involved correcting documentation errors, such as fixing user guide links and correcting the project name. They also addressed minor issues like removing repeated words and fixing an error message, indicating their attention to detail and contributions to the library's accuracy. The user also added an example for inductive clustering in scikit-learn.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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