I am a Research Assistant Professor at the Department of Geology and Environmental Science of the University of Pittsburgh. I apply Remote Sensing, Spatial Data Science, and Machine Learning (especially Neural Networks paired with SHAP for xAI), to study geomorphology, freshwater monitoring, extreme events, and the cascading effects of natural hazards in natural and riverine processes.

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

During my postdoctoral phase, I led the development of a National Aeronautics and Space Administration (NASA) dataset of sediment flux for every major river on Earth using Deep Learning applied to optical satellite imagery. The model is operational on PO.DAAC.

My research is based on Remote Sensed products and GIS techniques. I have coded scientifically for over a decade, having used many different languages over these years (Python, R, Fortran, JavaScript, C). I am used to building scalable, near real time pipelines, and running software in supercomputers and cloud computing environments. I also use and support the use of open-source software. Every example you will see in my Remote Sensing and GIS blog is based on open-source technologies.