My Research
Developing intelligent systems to understand and protect our planet's critical subsurface resources.
My work sits at the nexus of AI and geophysics. I translate complex subsurface data into actionable insights for environmental challenges. Using physics-informed learning and new uncertainty diagnostics, I help cities plan for land subsidence, accelerate groundwater exploration, and make probabilistic forecasts trustworthy.
Core Research Areas
Land Subsidence Forecasting
Problem:
Rapid urbanization and groundwater extraction threaten critical infrastructure.
Approach:
Physics-informed deep learning (XTFT variants) for early warning and sustainable urban planning.
Sustainable Groundwater Exploration
Problem:
Locating clean water in data-scarce regions remains a significant global challenge.
Approach:
AI-assisted inversions of AMT/CSAMT data, supported by open-source tooling like pyCSAMT.
Methodological Focus
Interpretable Uncertainty Diagnostics
Problem:
Making reliable decisions requires a trustworthy understanding of forecast uncertainty.
Approach:
Developing novel polar diagnostics (k-diagram) to analyze forecast coverage, reliability, and severity.
Physics-Informed Machine Learning
Problem:
Standard machine learning models can produce physically implausible results.
Approach:
Embedding physical laws and constraints into neural networks to improve generalization and data efficiency.