My path began in field geology, but I quickly realized that many critical problems, from mineral exploration to environmental risk modeling, demand quantitative methods beyond traditional observation. This insight led me to pursue a second degree in mathematics, with a focus on remote sensing, 3D geospatial modeling, and data integration. In my master’s project, I led the digital reconstruction of the Vaca Muerta Formation using UAV-acquired imagery, LiDAR point clouds, and hyperspectral data, producing a high-resolution stratigraphic model. This confirmed for me how computational geotechnology can decode geological complexity. Through a PhD, I aim to push this further: designing robust spatial analysis pipelines, improving reproducibility standards, and contributing to geoscience through data-intensive, domain-specific tool development.

I’ve independently led several research projects from concept to execution. These include the integration of UAV, LiDAR, and hyperspectral imagery for stratigraphic reconstruction; post-fire vegetation recovery mapping in the Pantanal wetlands; and terrain classification in Mars analog environments using radar–optical data fusion from Sentinel-1 and -2. I developed reproducible workflows, built custom geospatial databases, and authored multiple first-author manuscripts currently under peer review. Project details, including methods, datasets, and outcomes, are documented in the Projects section.

I combine strong technical capabilities in geospatial analysis with a solid foundation in mathematics, statistics, and software engineering. My academic background includes a B.Sc. in Mathematics, as well as training in database systems and algorithm design. I’ve applied statistical modeling and spectral analysis to remote sensing datasets, built spatial databases in SQL, and developed automation scripts in Python. My research involves integrating multisensor data (e.g., LiDAR, hyperspectral, SAR), generating 3D models, and deploying interactive platforms in Unreal Engine. These skills directly support the data-heavy, interdisciplinary demands of PhD-level geoscience research.

Yes. I’ve already tackled PhD-level challenges through independent and collaborative research. I’ve published in peer-reviewed venues like Lecture Notes in Computer Science (LNCS) and LNICST, and have manuscripts under review in high-impact journals such as Geo-spatial Information Science, Geomatics, Natural Hazards and Risk, and Arctic, Antarctic, and Alpine Research.

I’ve presented at major international conferences, including the 37th International Geological Congress (IGC) in South Korea and the World Congress on Earth Science and Climate Change in Paris. These efforts involved integrating complex datasets (UAV, LiDAR, hyperspectral), developing software in C++ and Python, and building immersive VR platforms. My technical depth, publication record, and project leadership show that I’m more than ready for the demands of a rigorous PhD program.

After completing the PhD, I intend to pursue a postdoctoral position and later become a university professor. My long-term goal is to develop solutions for society and the geosciences by integrating mathematics, statistics, database systems, and remote sensing. I want to bridge technical rigor with real-world applications, from environmental monitoring to resource management, using scalable, data-driven geospatial tools. I also aim to consistently contribute to top-tier journals in the field, such as Remote Sensing of Environment and the ISPRS Journal of Photogrammetry and Remote Sensing. I strive to create work that combines academic impact with societal relevance, helping to shape the next generation of applied geoscience research.