I'm a Mathematical Engineering student at EAFIT University with a strong background in applied mathematics, statistics, optimization, and stochastic processes.
I enjoy turning mathematical ideas into practical computational solutions, particularly in Machine Learning, data analysis, optimization, and simulation.
My current work focuses on building and comparing predictive models, designing optimization strategies, and developing data pipelines for large and messy datasets.
- Machine Learning & Statistical Learning
- Optimization & Metaheuristics
- Applied Mathematics
- Stochastic Processes
- Data Science
- Mathematical Modeling
- Simulation
Languages
Python R MATLAB Java SQL
Data & Machine Learning
pandas scikit-learn XGBoost MLflow
Other
MySQL Stata Simul8
Comparison of regularized linear models, tree-based models, boosting methods, and a neural network for house price prediction.
Includes data preprocessing, cross-validation, hyperparameter tuning, and experiment tracking with MLflow.
Python · scikit-learn · XGBoost · MLflow
A mathematical optimization approach for assigning employees to workplaces under hybrid work conditions.
The project explores the use of metaheuristics to obtain efficient assignments while satisfying operational constraints.
Python · Optimization · Metaheuristics
A movie recommendation system formulated as a multi-objective optimization problem, balancing novelty, familiarity, and variety.
The problem is solved using genetic algorithms.
Python · Multi-Objective Optimization · Genetic Algorithms
An exploratory study comparing five strategies for handling class imbalance in a cardiovascular dataset.
The study combines Kernel Density Estimation, Logistic Regression with interactions, and Gradient Boosting.
Python · Statistics · Machine Learning
Before studying Mathematical Engineering, I studied Psychology at Universidad de Antioquia.
That background continues to influence how I approach technical problems: with curiosity about both the mathematical structure of a problem and the people who ultimately interact with its results.
Interested in the structure behind complex problems — from mathematical models to real-world data.