Projects

Wind and Spores | Aug 2024 – May 2025

• Open source R package for dispersion of spores in hilly terrains.
• Simulate the dispersion of spores in hilly terrains in R and C++.

Used: C++, Git, R: RStudio, Rcpp, ggplot2, dplyr, tidyr

Tleco | Jan 2024 – Sep 2024

• Tleco stands for both in the fire and rise in the nahuatl language.
• Open Source Code for Simulation of Relativistic Particles Radiation
• Numerical code that simulates particles in relativistic plasma, and the rise of radiation from accelerating particles.
• Consists of both Rust functions and Python functions previously built in the Fortran code Paramo.

Used: Rust, HDF5, Git, Python: Numpy, Matplotlib, Scipy

Publications using Tleco

• Davis, Z., Rueda-Becerril, J.M., Giannios, D. (2024). “Tleco: A Toolkit for Modeling Radiative Signatures from Relativistic Outflows”. The Astrophysical Journal, 976(2), 182, 12. DOI:10.3847/1538-4357/ad8bc2

Paramo | Oct 2018 – Apr 2022

Open Source Code for Radiative Transfer Simulations in Relativistic Astrophysics

Key Achievement: Achieved 24× speedup (2 min → 5 sec) through algorithmic optimization and OpenMP parallelization

• Independently developed production-grade HPC code for Monte Carlo radiative transfer simulations
• Optimized through systematic profiling, algorithmic improvements, and memory efficiency enhancements
• Applied gradient descent optimization to fit simulation parameters to NASA telescope observations
• Used by 3+ graduate students; resulted in 5+ peer-reviewed publications
• Developed comprehensive Python analysis and visualization pipeline

Used: Fortran, HDF5, OpenMP, MPI, Git, Python: NumPy, Pandas, Matplotlib, SciPy

Publications using Paramo

• Rueda-Becerril, J.M., Harrison, A.O., Giannios, D. (2021). “Blazar jets launched with similar energy per baryon, independently of their power”. Monthly Notices of the Royal Astronomical Society, 501(3), 4092-4102. DOI:10.1093/mnras/staa3925
• Rueda-Becerril, J.M., Harrison, A.O., Giannios, D. (2021). “The blazar sequence revised”. Astronomische Nachrichten, Volume 342, Issue 1-2, pp. 147-152. DOI:10.1002/asna.202113895
• Rueda-Becerril, J.M. (2021). “A numerical approach for radiative cooling in relativistic outflows”. Astronomische Nachrichten, Volume 342, Issue 1-2, pp. 277-282. DOI:10.1002/asna.202113919
• Davis, Z., Rueda-Becerril, J.M., Giannios, D. (2022). “Balancing turbulent heating with radiative cooling in blazars “. Monthly Notices of the Royal Astronomical Society, 513(4), 5766-5779. DOI:10.1093/mnras/stac1282
• Combi, L., Siegel, D.M. (2023). “GRMHD Simulations of Mergers with Weak Interactions: r-process Nucleosynthesis and Electromagnetic Signatures of Dynamical Ejecta”. The Astrophysical Journal, 944(1), 29. DOI:10.3847/1538-4357/acac29

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Portfolio

Machine Learning From Scratch

Fundamental ML algorithms implemented from scratch to demonstrate deep understanding of underlying mathematics:

Algorithm	Description
Neural Network	Feedforward NN solving XOR problem with backpropagation
Decision Tree	Classification tree with Gini impurity
Random Forest	Ensemble with bootstrap aggregating
Gradient Boosting	Sequential ensemble with residual fitting
Movie Recommender	Collaborative filtering (user-based, item-based, matrix factorization)

Used: Python: NumPy, Matplotlib, Scikit-learn

DataCamp

Applied data science projects completed through DataCamp courses:

Project	Topic	Description
Avocado Toast Analysis	Exploratory Data Analysis	Price and trend analysis of avocado-related datasets with visualization
Nobel Prizes	Historical Data Analysis	Examining Nobel Prize award data, winners, and trends over time
Oldest Businesses	Business & History	World’s oldest businesses: longevity, characteristics, and geographic distribution
Sleep Data Analysis	Health & Predictive Modeling	Sleep disorder prediction and health metrics analysis

Used: Python: Pandas, NumPy, Matplotlib, Seaborn

Kaggle

Project	Competition / Dataset	Description
Titanic — A Study of a Shipwreck	Titanic: Machine Learning from Disaster	Binary classification to predict passenger survival; female survival rate 74.2% vs. male 18.9%
Telco Customer Churn	Telco Customer Churn	Predict which telecom customers are likely to churn; EDA + model comparison across 7 classifiers on 7K records

Used: Python: Pandas, Scikit-learn (Logistic Regression, KNN, Decision Tree, Random Forest, Gradient Boosting, SVM), XGBoost

Templates

Reusable Jupyter notebook templates for standard data science workflows, available in the Templates directory:

Template	Purpose
Data Cleaning	Data quality checks and cleaning workflows
Data Querying	Data retrieval and querying operations
EDA	Exploratory data analysis and visualization
ML Preprocessing	Feature engineering and preprocessing for ML models

Tonalpowalli

I have a GitHub repository for my tonalpowalli (the nawah count of the days) project (in progress).

Gists

I have a collection of GitHubGists that you can explore.