Federico Barrios
Senior Software Engineer at Booking.com
Amsterdam, North Holland, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolFederico Barrios is a Senior Software Engineer based in Amsterdam with 11 years of experience building robust back-end systems and data-driven features. Currently at Booking.com, he applies deep engineering skills to production-scale problems while maintaining a strong interest in natural language processing and summarization. His open-source contributions include improvements to gensim and a TextRank implementation, where he focused on sentence tokenization, ROUGE evaluation, and Python 3 compatibility—work that underpins more reliable summarization tooling. Trained as a software engineer at the University of Buenos Aires with an earlier electronics background, he blends low-level technical curiosity with practical software delivery. Colleagues find him both detail-oriented in algorithmic refinements and pragmatic about shipping measurable improvements. He often bridges data science and engineering, turning experimental NLP ideas into reproducible, tested code.
11 years of coding experience
Software Engineer, Software Engineer at University of Buenos AiresTécnico en electrónica, Técnico en electrónica at Escuela de Educación Técnica Nro. 9, Lanús
Spanish, English, Portuguese, Dutch
Github Skills (17)
gensim10
python10
data-science10
machine-learning10
textrank10
topic-models10
natural-language-processing10
topic-modeling10
nlp10
data-structure9
algorithm9
algorithms9
scikit9
scikit-learn9
data-structures9
Programming languages (5)
Github contributions (5)
summanlp/textrank
Oct 2014 - Jun 2019
TextRank implementation for Python 3.
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:4 releases, 166 commits, 31 PRs in 4 years 9 months
Contributions summary:Federico primarily contributed to the development of the text summarization features. Their work involved implementing sentence splitting functionality and integrating NLTK for sentence tokenization. They also refactored the code, made improvements to the text cleaning and tokenization processes, and created an evaluation script. Furthermore, the user worked on incorporating ROUGE metrics and dataset parsing to analyze the performance of the summarization algorithm.
nlppythonpython-3text-summarizationsummarization
piskvorky/gensim
Apr 2015 - Nov 2017
Topic Modelling for Humans
Role in this project:Data Scientist
Data ScientistContributions:34 commits, 4 PRs, 13 comments in 2 years 7 months
Contributions summary:Federico primarily contributed to the gensim library, focusing on improving the summarization features. Their commits addressed bugs like `ZeroDivisionError` and Python 3 compatibility issues within the summarization module. They also added tests to validate summarization results and enhance the performance of the BM25 algorithm, indicating a focus on improving the quality and reliability of summarization tools.
pythonword-similarityword-embeddingsdata-miningfor-humans
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