Mario Graff
CONACYT Researcher - INFOTEC at Infotec
Aguascalientes, Mexico
Summary
👤
Senior🎓
Top SchoolMario Graff is a CONACYT researcher at INFOTEC with 11 years of experience applying evolutionary computation and genetic programming to supervised learning, especially text classification and sentiment analysis. He holds a PhD in Computer Science from the University of Essex where he developed practical performance models for GP, later applied to time series forecasting and other learners. Mario builds reproducible research software—four Python projects (microTC, B4MSA, EvoMSA, EvoDAG) with CI and pip/conda distribution—demonstrating a commitment to open science and deployment-ready tooling. His NLP work focuses on multilingual representations that enable cross-lingual transfer, reflecting a practical bridge between theory and applied systems. He has academic experience as an assistant professor and visiting faculty at Colgate University, and contributes to CI/CD improvements in prominent community infrastructure like conda-forge staged-recipes.
11 years of coding experience
4 years of employment as a software developer
PhD, Computer Science, PhD, Computer Science at University of EssexMaster of Science (MSc), Computer Science, Master of Science (MSc), Computer Science at Universidad Michoacana de San Nicolás de Hidalgo
Spanish, English
Github Skills (13)
sh10
github-ci10
conda10
docker10
script10
shell10
githubaction-workflow10
dockers10
scripting10
build-automation10
cicd10
python9
conda-forge9
Programming languages (6)
Github contributions (5)
conda-forge/staged-recipes
May 2022 - Aug 2022
A place to submit conda recipes before they become fully fledged conda-forge feedstocks
Role in this project:
DevOps Engineer
DevOps EngineerContributions:3 reviews, 13 commits, 8 PRs in 2 months
Contributions summary:Mario primarily contributed to the repository's CI/CD infrastructure. They modified build scripts (.sh and .bat files) and workflows to integrate and configure services like Miniforge and conda. These changes focused on setting up environments, installing dependencies, and integrating tooling such as conda-forge-ci-setup and conda-build, improving build and testing procedures. They also incorporated improvements to error handling and dependency management within the build process, contributing to a more robust and reliable continuous integration pipeline.
condaconda-forge
mgraffg/EvoDAG
Dec 2015 - Jan 2023
Evolving Directed Acyclic Graph
Contributions:4 releases, 604 commits, 45 PRs in 7 years 2 months
directed-acyclic-graphgenetic-programmingsupervised-learning
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