I obtained my Ph.D. in Industrial and Systems Engineering on Dec. 2023, after completing the MSc. in Data Science and Analytics program at The University of Oklahoma (OU) in the Summer of 2019. I also hold a BSc. in Systems Engineering from Universidad El Bosque in Bogotá, Colombia where I am from originally.
My Ph.D dissertation revolved around the operationalization of historical flash flood reports, yielded an unprecedented flash flood impacts dataset, and laid the foundation for the development of ML-based decision support tools, which aim to enable weather forecasters to issue improved impact-based flash flood warnings.
My MSc. Thesis was entitled Probabilistic characterization of floods from catchment-scale precipitation moments, in which machine learning models were used in conjunction with data pertaining the spatial variability of rainfall, attempting to characterize flood conditions and characteristics over gauged locations across the Contiguous United States (CONUS).
Since Spring of 2017, I have worked with NOAA's National Severe Storms Laboratory (NSSL) through OU's Cooperative Institute for Mesoscale Meteorological Studies (CIMMS). As part of NSSL's Warning Research and Development Division (WRDD), I've been actively involved with projects such as ANCHOR (Automated Non-Contact Hydrologic Observation in Rivers) and FLASH (Flooded Locations and Simulated Hydrographs).
Revolving around topics related to hydrometeorology and hydroinfomatics: hydrologic modeling of flash floods, remote sensing and post-wildfire debris flows, my contributions have involved designing and implementing machine learning and statistical computing approaches for predictive modeling, data processing and analysis. I have also worked with remote autonomous non-contact sensing technologies applied to hydrologic observations in streams. These include Stream RADAR technologies for real-time streamflow monitoring and alerting, as well as automated LIDAR-based bathymetry retrieval for small streams.