I am a Research Assistant Professor in the Department of Geology and Environmental Science at the University of Pittsburgh. I am a remote sensing hydro-geomorphologist, applying techniques in the realm of Spatial Data Science, Deep Learning, and Explainable AI, to study geomorphology, freshwater monitoring, extreme events, and the cascading effects of natural hazards in riverine processes.

My PhD Dissertation on spatial modeling of rainfall thresholds for landslide occurrence was awarded the CAPES Thesis Award – the highest distinction for a PhD Dissertation defended in Brazil.

During my postdoctoral phase, I led the development of a NASA dataset of sediment flux for every major river on Earth using Deep Learning applied to optical satellite imagery (paper at Geophysical Research Letters). The model is being operationalized at NASA’s PO.DAAC archive.

I have coded scientifically for over a decade across Python, R, Fortran, JavaScript, C, MATLAB, and bash, with deep experience in the geospatial Python stack (GDAL, rasterio, geopandas), deep learning frameworks (PyTorch, Keras), and platforms like Google Earth Engine, QGIS, and ArcGIS. I enjoy building scalable, operational, near-real-time pipelines and running software on HPC clusters and in cloud computing environments. I also use and advocate for open-source software and collaborative Git-based development.