AI-Enabled Earth Systems Scientist
I build AI-driven, physically grounded systems to improve how we monitor hazards, model climate-risk interactions, and accelerate scientific discovery.
I develop methods that align foundation-model capabilities with Earth system data, from seismic waveforms to environmental time series. The focus is robust inference under sparse, noisy, and shifting conditions.
Learn moreI design cross-modal monitoring strategies that combine seismic, geodetic, infrasound, and remote sensing streams. These systems improve situational awareness for critical infrastructure and public safety workflows.
Learn moreI study links between climate variability and geohazard processes to support risk-informed planning. The work blends physical modeling with data-driven detection for more actionable forecasting insights.
Learn moreAn agentic AI framework for Earth science workflows, designed to speed up hypothesis generation, data triage, and interpretable analysis across heterogeneous datasets.
Integrated observational networks combining multiple sensing modalities to improve hazard detection, characterization, and downstream decision support in real time.
Machine learning pipelines for waveform interpretation, emphasizing reliable event detection, uncertainty estimation, and physically plausible model outputs.