Néstor López
Principal Machine Learning Scientist at Musical AI
Montreal, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolNéstor López is a Principal Machine Learning Scientist with nine years of experience specializing in music technology at the nexus of music theory and deep learning, currently leading Music AI efforts at Musical AI after productizing MIR research for Avid's Sibelius. He holds a PhD in Music Technology from McGill and has a strong track record bridging symbolic and audio representations to build large-scale attribution and harmonization pipelines. His contributions to the widely used music21 toolkit—adding a HarmSpine/HarmParser and associated tests—underscore a practical commitment to reproducible computational musicology. Earlier work ranges from annotated Roman-numeral datasets for classical harmony to industry roles in graphics security and C++ development, reflecting unusual cross-disciplinary fluency. He teaches and mentors as well, having co-lectured courses on music and the web, which informs his knack for turning academic research into user-facing tools.
9 years of coding experience
12 years of employment as a software developer
Licenciatura en Informática, Licenciatura en Informática at Universidad de GuadalajaraProfesional Medio en Música, Profesional Medio en Música at Instituto Nacional de Bellas ArtesMaster in Sound and Music Computing, Master in Sound and Music Computing at Universitat Pompeu FabraPhD in Music Technology, Music Technology, PhD in Music Technology, Music Technology at McGill University
Spanish, English, French
Github Skills (4)
unit-testing10
regular-expression10
python10
computer-music9
Programming languages (13)
Github contributions (5)
cuthbertLab/music21
Jun 2020 - Jan 2022
music21 is a Toolkit for Computational Musicology
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 reviews, 14 commits, 3 PRs in 1 year 7 months
Contributions summary:Néstor focused on adding a parser for harmonic analysis annotations within the `music21` library, a toolkit for computational musicology. This involved creating a `HarmSpine` class and integrating a `HarmParser` to interpret Humdrum data related to harmony. The user also implemented unit tests for the `HarmParser` and `HarmSpine` to ensure the functionality's accuracy. Furthermore, the user made a few refactoring changes for better code quality and for passing linting tests.
dtwpythonmusicologymusic21music
DDMAL/cantus-staticpages
Jul 2019 - Jul 2022
This repository accommodates the content of the static pages in the Cantus Ultimus Website
Contributions:2 reviews, 4 PRs, 20 pushes in 2 years 11 months
static-pages
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