Kheireddine Abainia
Associate Professor
Annaba, Annaba, Algeria
Summary
👤
Senior🎓
Top SchoolKheireddine Abainia is an Associate Professor and seasoned software engineer with over a decade of programming experience and a PhD in Telecommunication and Information Processing. He blends academic research in NLP, IR, and computational linguistics with practical skills in robotics, embedded systems, and IoT, having taught courses from C++ and embedded systems to web technologies. A reliable and punctual leader, he has six years of organizational and teaching leadership and consults across e-marketing and systems engineering. His open-source contributions include implementing and refining an Arabic light stemmer for NLTK, demonstrating both language-specific expertise and a focus on algorithm quality and maintainability. Based in Annaba, Algeria, he pairs hands-on development with autonomous research capabilities to drive technological development in both academic and industry settings.
10 years of coding experience
3 years of employment as a software developer
Master's degree, Pattern Recognition and Artificial Intelligence, Master's degree, Pattern Recognition and Artificial Intelligence at Université Badji Mokhtar de AnnabaPh.D, Telecommunication and Information Processing, Ph.D, Telecommunication and Information Processing at Université des Sciences et de la Technologie 'Houari Boumediène'
French, anglais, vieux (ca.450-1100), Arabic, Japanese
Github Skills (5)
nltk10
nlp10
python10
natural-language-processing10
machine-learning7
Programming languages (1)
Github contributions (5)
nltk/nltk
Aug 2017 - Dec 2020
NLTK Source
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 commits, 2 PRs, 8 comments in 3 years 4 months
Contributions summary:Kheireddine primarily contributed to the development of an Arabic light stemmer, evidenced by the creation and updates of `arlstem.py` and `arlstem2.py`. The user implemented the ARLSTem algorithm, a light stemmer for Arabic, by defining methods to remove prefixes, suffixes, and perform normalization. Their work included refactoring and improving code layout, indicating a focus on maintainability and efficiency within the Arabic language processing context. The user's later contributions centered around improving the algorithm and reducing under-stemming errors, indicating a strong focus on the quality of the algorithm.
nlppythonmachine-learningnltknatural-language-processing
xprogramer/academic-kickstart
Nov 2018 - Jun 2023
Contributions:220 pushes, 1 branch in 4 years 8 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.