I am a Postdoctoral Research Scholar at Arizona State University, working on AI/ML, remote sensing, and geospatial modeling for environmental systems — soil, water, climate, carbon, and agriculture.
My research turns large, heterogeneous environmental datasets into decision-relevant tools: continental-scale soil-carbon baselines and attainable benchmarks; field-scale water-use diagnostics for irrigated agriculture; operational, uncertainty-aware streamflow forecasts for arid watersheds; and projections of compound heat extremes for urban climate resilience. The current threads are described on the projects page, but the underlying interest is portable: methods that pair satellite, soil, climate, and yield data with modern ML — gradient-boosted trees, LSTMs, transformers, and the reliability diagnostics that make their outputs trustworthy for policy and practice.
My PhD in Computational Science (UTEP, 2022) is grounded in optimization, decision-making under uncertainty, and the mathematical foundations of machine learning.
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