Data
High-fidelity datasets and advanced algorithmic frameworks are the cornerstones of breakthroughs in the Earth sciences. This portfolio is dedicated to fortifying foundational data infrastructures and integrating novel data-driven methodologies into Earth system research:
🌡️ Development of Foundational Climatological Datasets
Developing and deploying a global, near real-time daily apparent temperature and heatwave dataset. This work provides robust empirical support for multi-scale investigations into climate change dynamics and hydrological responses.
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🤖 Interdisciplinary Application of Data-Driven Methodologies in the Earth Sciences
Pioneering the integration of cutting-edge data mining and machine learning algorithms within hydrometeorology. For instance, this work includes the development of a Transformer-based, data-driven model that achieves highly efficient and accurate simulations of multi-scale soil moisture—a critical metric for assessing agricultural drought. Ultimately, this advancement propels the deeper application of data-driven techniques in decoding the complex, non-linear processes inherent to the global water cycle.
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I am an early-career climate scientist pushing the boundaries of understanding wildfires and climate extremes using hydroclimatic, data-driven, and geostatistical approaches. My work has led to step-changes in understanding the synchronicity and persistence of extreme fire weather, factors that strongly influence extreme fire activity. My recent research focuses on extreme fires, one of the most societally and environmentally destructive consequences of climate change.
I work with Prof. John Abatzoglou, who leads the Climatology Lab at University of California, Merced.