🎓 Statistics student at Universidad de Oriente, Núcleo Nueva Esparta
📊 Aspiring Data Scientist & AI Researcher
🧠 Focused on Statistical Learning, AI, and Mathematical Foundations
I am currently working on adaptive density estimation for weakly dependent data, with a strong emphasis on theoretical foundations and practical implementation.
I am deeply studying mathematics for machine learning, including:
- Linear Algebra
- Probability Theory
- Calculus
while programming intensively in Python to translate theory into reproducible and efficient algorithms.
My long-term goal is to contribute to advanced AI research by combining statistics, mathematics, and computation.
- 📌 Become a Data Scientist
- 🤖 Contribute to cutting-edge AI research at companies such as
Google DeepMind, OpenAI, Anthropic, or Mistral - 🎓 Pursue a Master’s degree in Random Models
- 🎓 Obtain a PhD in Mathematics
- Probability Theory & Random Processes
- Regression Analysis
- Multivariate Analysis (PCA, Clustering, Factor Analysis)
- Time Series Analysis
- Statistical Learning Theory
- Kernel Density Estimation (KDE)
- Supervised & Unsupervised Learning
- Algorithm Design & Evaluation
- Adaptive Methods
- Mathematical Foundations of ML
- Python (OOP, numerical computing, data analysis)
- Jupyter Notebooks
- Reproducible research workflows
- 🔬 Adaptive Density Estimation for weakly dependent data
- 📊 Statistical Data Analysis in public policies
- ⏱️ Time Series Forecasting Algorithms
- 📉 Data Visualization & Exploratory Analysis in Python and R
- Mathematical foundations of Machine Learning
- Dependence structures in stochastic processes
- Nonparametric estimation
- Theory ↔ implementation bridge in statistical algorithms
⭐ I believe strong AI systems are built on solid mathematics, statistics, and careful modeling.