Results-driven Postdoctoral Researcher with a PhD in Physical Geography, specializing in remote sensing, machine learning, and their integration for hydrological and ecological assessment. Proven expertise in leveraging satellite-derived datasets and geospatial modeling to quantify land surface dynamics, vegetation-water interactions, and ecohydrological responses to climate variability. Advanced proficiency in R and Python for large-scale spatiotemporal analysis, time-series decomposition, and development of hybrid ML–process-based models. Passionate about applying AI to assess water availability, vegetation health, and ecosystem resilience, supporting nature-based climate solutions and sustainable watershed management. Demonstrated impact through 30+ peer-reviewed publications, bridging cutting-edge remote sensing science with actionable hydrological and ecological insights.