https://github.com/agarbuno/agarbuno
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (7.3%) to scientific vocabulary
Last synced: 10 months ago
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JSON representation
Repository
Basic Info
- Host: GitHub
- Owner: agarbuno
- Default Branch: main
- Size: 24.4 KB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Created almost 4 years ago
· Last pushed about 2 years ago
Metadata Files
Readme
README.md
Hello, this Alfredo Garbuno 🤓
- 🔭 I’m currently working in BBVA as discipline leader of Advanced Analytics
- 🌱 I’m an Applied Mathematician / Data Scientist with research interest in Uncertainty Quantification
- 🎓 I have a Phd in Engineering and Computational Mathematics
- 👯 I’m looking to collaborate on research projects
- 💬 Ask me about Bayesian inference, Machine Learning, Applied Probability, Numerics, Stochastic Processes or any intersection of the above
- 📖 You can find the material I use for teaching below in Spanish
- 🎸 I play guitar and started learning audio engineering
- 💥 I read manga and enjoy fantasy based stories
- 🏀 I play basketball and I am currently training
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Courses
These are the courses that I taught at ITAM while I was a professor in the Stats Department and director of the Msc in Data Science. All courses were planned from scratch (except the Stats foundation course). If used for any academic purposes you can find the Zenodo ids to cite them properly.
Owner
- Name: Alfredo Garbuno Iñigo
- Login: agarbuno
- Kind: user
- Location: Mexico City
- Company: ITAM
- Website: agarbuno.github.io
- Twitter: AlfredoGarbuno
- Repositories: 71
- Profile: https://github.com/agarbuno
Bayesian inference, non-parametric Bayesian models, MCMC algorithms, Kernel Methods, Data assimilation, Langevin dynamics