Marcela M
PhD Candidate In Computer Science
Guanajuato City, Guanajuato, Mexico
Summary
👤
Senior🎓
Top SchoolMarcela M is a computer scientist and PhD candidate at CIMAT with nine years of experience applying high-performance and cloud software engineering to research-driven problems. Her current research focuses on generative models for crowd motion using Denoising Diffusion Probabilistic Models, building on a strong background in numerical methods, parallel computing (CUDA, OpenMP, Xeon Phi), and validation for large-scale systems. She spent several years at Intel across roles in software development, cloud engineering, and research, contributing to Open Source Technology Center projects and Intel Unite® solutions. Marcela combines hands-on production experience with academic rigor, aiming to become a research scientist who tackles social, medical, and environmental challenges through AI. An uncommon thread in her profile is the blend of low-level HPC optimization and modern generative modeling, enabling efficient, scalable research prototypes.
10 years of coding experience
9 years of employment as a software developer
Master of Science (MS), Mathematics and Computer Science, Master of Science (MS), Mathematics and Computer Science at Centro de Investigación en Matemáticas (CIMAT)Bachelor's degree, Computer Systems Engineering, Bachelor's degree, Computer Systems Engineering at Universidad Autónoma de Aguascalientes
Spanish, English, Italian
Github Skills (100)
python10
toolbox10
clearlinux10
deep-learning10
gpu10
uncertainty10
operator10
charts10
bayesian-deep-learning10
ml10
helm10
neural-network10
jax10
data-science10
tensorflow10
Programming languages (9)
Github contributions (5)
marcemq/appinventor-ws
Jun 2019 - Sep 2019
Contributions:9 commits, 14 pushes, 1 branch in 2 months
marcemq/dl-examples
Apr 2019 - May 2022
A variety of ML/DL examples using python, from data exploration to more complex DL in action models.
Contributions:14 PRs, 11 pushes, 12 branches in 3 years 1 month
pythondata-explorationdata-sciencedeep-learningmachine-learning
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