Alexander Sarango

Undergraduate Thesis: Gravastar Model with Vanishing Complexity

The goal of this research was to develop a new gravastar model with vanishing complexity, a condition useful for the study of complex stellar objects. To achieve this, the methodology employed consisted of using extended minimal geometric deformation approach under the gravitational decoupling framework. Through this methodology, a new gravastar solution was obtained and it was demonstrated that this new solution satisfies the necessary conditions to be considered a physically acceptable solution.

GEAA - ESPOCH

Research Assistant

My activities carried out at the GEAA - ESPOCH as research assistant, focusing on implementing a dynamic clustering algorithm in Python using K-Means algorithm. Dynamic clustering offers advantages over traditional methods in identifying climate patterns. The work included literature review on climate models, clustering algorithms, and dimensionality reduction techniques (PCA/EOF), followed by practical implementation using random data. Results showed the effectiveness of dynamic clustering on precipitation and temperature data from the CNRM and CMIP6 models.

Special & General Relativity

• Schwarzschild solution derivation (PDF)

Courses

• - Introduction to LaTeX (Spanish) (material)

Talks

• - "Gravastar model with vanishing complexity" at the III Symposium on Theoretical and Computational Physics (Poster)
• - "Gravastars, the missing piece of the cosmic puzzle?" at the Scientific Coffee (Slides)

Workshops

• - "GitHub for Team Collaboration" with the Club de Física Emmy Noether
• - "Quantum Machine Learning with Qiskit" at the Qiskit Fall Fest 2025 ESPOCH