// geotechnical engineer · AI & machine learning specialist
// rail infrastructure · monitoring · data-driven geomechanics
Proven record applying AI techniques to complex geotechnical problems. Focused on enhancing infrastructure resilience and performance through data-driven insights.
// experience
Developed SchemaGAN, an AI model that improved geotechnical data interpretation. The model, part of my Master's thesis, was validated through expert surveys and demonstrated superior performance in predicting soil behaviour patterns.
Examined anchor length influence and compared load-distribution models in PLAXIS. Research supported the implementation of double grouted anchors for optimised retaining wall performance, improving safety margins by 15%.
Directed comprehensive in-situ testing programmes including SPT, CPT, boreholes, and geophysical surveys across 20+ project sites. Managed data acquisition, quality control, and interpretation for infrastructure projects.
Assessed geotechnical suitability for the Quito Metro project and specialised landfill stability. Collaborated with government departments, providing recommendations that reduced projected risks by identifying critical failure mechanisms.
Delivered geotechnical engineering courses and mentored 8+ bachelor's thesis projects focused on applied geomechanics and site investigation methods.
Provided technical feedback on new geotechnical analysis features and contributed to the trainee programme, improving user onboarding for 200+ engineers.
// education
Geo-Engineering
Delft University of Technology, Netherlands
Geomechanics
// Cum LaudeInternational Geotechnical Center
Geology
// With HonorsUniversity of Costa Rica
// skills
// professional
// technical
// languages
// contact
Available for opportunities in geotechnical engineering, AI applications in geomechanics, and infrastructure resilience.