David Laviña
Research Engineer at Barcelona Supercomputing Center
Barcelona, Catalonia, Spain
Summary
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Rockstar🎓
Top SchoolDavid Laviña is a research engineer with 11 years of experience who blends hands-on open-source development with academic research in computer architecture and AI. Currently an AI4Science fellow at the Barcelona Supercomputing Center and a part-time PhD student at UPC, he focuses on accelerating matrix multiplication for parallel architectures. His background spans human-centered computing at UCM-GRASIA to building data pipelines and dashboards for DAO analysis, and he has a strong ML engineering track record contributing evaluation metrics and cold-start improvements to the popular Recommenders project. David also brings full‑stack and language-diverse experience, having enhanced IHaskell’s Jupyter widgets, reflecting comfort across systems from low-level performance work to interactive tooling. Fluent in Spanish and learning Japanese, he combines rigorous academic training (MSc in AI) with practical engineering that bridges research prototypes and production-grade evaluation.
11 years of coding experience
Master's Degree, Artificial Intelligence, 8,82, Master's Degree, Artificial Intelligence, 8,82 at Polytechnic University of MadridPhD, Computer Architecture, PhD, Computer Architecture at Universitat Politècnica de Catalunya (UPC)Diploma Mención Honorífica ESO, Educación Secundaria Obligatoria (ESO), Diploma Mención Honorífica ESO, Educación Secundaria Obligatoria (ESO) at IES Miguel DelibesBachelor, Computer Science, 8.42, Bachelor, Computer Science, 8.42 at IT Faculty, Complutense University of MadridPhD, Computer Science, PhD, Computer Science at Complutense University of MadridGeneral Certificate of Education (GCE), Programa de Excelencia en Bachillerato, General Certificate of Education (GCE), Programa de Excelencia en Bachillerato at IES Palas Atenea
Spanish, English, Japanese
Stackoverflow
458reputation
60kreached
16answers
33questions
Github Skills (24)
recommendation-system10
python10
jupyter10
machine-learning10
haskell10
data-science9
back-end-development9
pandas9
pytest9
front-end-development9
deep-learning8
deeplearning-ai8
ranking8
pygobject6
jupyter-notebook6
Programming languages (23)
Github contributions (5)
IHaskell/IHaskell
Jun 2021 - Aug 2021
A Haskell kernel for the Jupyter project.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:5 reviews, 51 commits, 2 PRs in 2 months
Contributions summary:David primarily worked on enhancing the IHaskell project, focusing on the implementation of widgets for the Jupyter environment. They added functionality for the widget metadata to store version information and integrated the core features of the HTML widgets, with specific contributions to the String, Numeric, and Media widgets. The user also made changes to the Display and Types files for better support and improvements.
kerneljupyter-notebookhaskelljupyterjupyter-kernels
recommenders-team/recommenders
Jul 2023 - Oct 2024
Best Practices on Recommendation Systems
Role in this project:ML Engineer
ML EngineerContributions:13 reviews, 13 PRs, 52 pushes in 1 year 3 months
Contributions summary:David contributed to the implementation and testing of recommendation system evaluation metrics, specifically R-precision. They modified the evaluation module by adding and testing the r-precision metric, including unit tests and corresponding code modifications. Further contributions involved refactoring code for improved efficiency and supporting cold-start scenarios within the implicit collaborative filtering model, along with bug fixes.
recommendation-systemspythonjupyter-notebookoperationalizationmicrosoft
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