Jorge Alvarez

As a Consulting Engineer in Rock Mechanics, I applied interpretable machine learning techniques to identify critical zones in hydraulic tunnels, optimizing numerical modeling. I also developed a methodology using Graph Neural Networks (GNN) for spatial variability modeling of geomechanical properties, enhancing prediction accuracy.

In the Geomechanics Superintendency, I conducted geomechanical reviews for the Andes Norte mine, assessing estimated behavior against construction data. I also developed a numerical model to evaluate hydraulic fracturing behavior and implemented analytical pressure range estimations.

As a Geotechnical Engineer, I performed geotechnical evaluations for various projects, including pillar stability estimation and support design using numerical modeling. I also conducted rock burst potential estimation and dynamic fortification analysis.

As a Thesis Engineer, I contributed to various projects, applying my knowledge in civil and mining engineering. My work involved supporting geotechnical analyses and contributing to project documentation.

Completed a Master's degree focusing on Artificial Intelligence. Gained expertise in Deep Learning, graph neural networks (GNN), and unstructured data analysis, applying these techniques to real-world industry projects.

Completed a diploma focused on geomechanics as applied to mining design. Enhanced knowledge in the practical application of geomechanical principles for mining operations.

Obtained a degree in Mining Civil Engineering. Developed a strong foundation in mining operations, geotechnics, and related engineering principles.