University of Arizona | Integrating Physics, Machine Learning & Data Science
I am a Ph.D. candidate in Hydrology and Atmospheric Sciences at the University of Arizona, working at the intersection of physical hydrology, geospatial data science, scientific computing, and machine learning.
My research focuses on developing scalable and interpretable modeling systems for water-resources applications. I work with large-scale hydrologic and land-surface models, including Noah-MP and RAPID, and build reproducible workflows for environmental data processing, model evaluation, and streamflow prediction.
I have experience with Python, PyTorch, xarray, GDAL, GIS, DuckDB, SQL, AWS, and high-performance computing, with a strong focus on handling large environmental datasets and building efficient scientific data pipelines.
My current work includes developing hybrid physics–machine learning models, graph neural network routing frameworks, and GPU-enabled hydrologic modeling systems. This includes translating Fortran-based hydrologic models such as Noah-MP into PyTorch-based, deep-learning-ready implementations that can be coupled with neural networks.
I am especially interested in systems that combine:
- Physics-based hydrologic modeling
- Geospatial and climate data pipelines
- Machine learning and graph neural networks
- Cloud-native and high-performance scientific computing
- Interpretable AI for water-resources applications
