Fernando Maurera
Senior Software Engineer at Pearl
Madrid, Community of Madrid, Spain
Summary
👤
Senior🎓
Top SchoolFernando Maurera is a Senior Software Engineer and PhD in Artificial Intelligence based in Madrid with 11 years of experience building and deploying machine learning-driven systems. He specializes in recommender systems, having pioneered Impression-Aware Recommenders during his PhD at Politecnico di Milano and published 10+ peer-reviewed papers that improved accuracy and diversity by over 10%. Combining industrial full-stack experience (cloud deployments on Azure/GCP, Java, .NET, React) with rigorous research practices, he moves algorithms from prototype to production with attention to efficiency and testability. At Mahisoft and Dinama he shipped customer-facing services under Scrum, and he now contributes to production engineering at Pearl. He brings a collaborative, multicultural working style and teaching experience, translating academic insights into practical workshops and code. Outside work he follows gaming, music, and emerging industry trends, which informs his user-centric approach to AI systems.
11 years of coding experience
2 years of employment as a software developer
Bachelor's degree, Computer Science, Computing Engineering, Bachelor's degree, Computer Science, Computing Engineering at Universidad Simón BolívarHigh School, Science, High School, Science at U.E. Colegio Nuestra Señora de PompeiDoctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at Politecnico di Milano
Spanish, English, Italian
Github Skills (67)
python10
impressions10
algorithm10
knn10
data-structures10
sorting-algorithms10
recommender9
deep-learning9
machine-learning9
factorization9
splits8
refer8
recommender-system8
cython8
collaborative-filtering8
Programming languages (6)
Github contributions (5)
fernandobperezm/UndergradThesis-RecSysCotraining
Mar 2017 - Sep 2017
Contributions:236 commits, 2 pushes in 5 months
fernandobperezm/ECIR2022-AnEvaluationStudyOfGANsForCF
Jan 2022 - Sep 2024
This repository contains the source code and data used in our experiments described in the paper "An evaluation of Generative Adversarial Networks for Collaborative Filtering". Refer to the README file to run our experiments.
Contributions:3 releases, 2 PRs, 14 pushes in 2 years 8 months
collaborative-filteringevaluationgenerative-adversarial-networkrecommender-systems
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