Independent Research Organization
Advancing the
Science of Space
Theoretical foundations for in-context learning, geospatial artificial intelligence, quantum geodesy, and geospatial intelligence at the intersection of transformer architectures and Earth observation.
Featured Research
In-Context Learning Characterization for Geospatial AI
A comprehensive seven-paper theoretical framework establishing the ICL-Easy/Hard dichotomy through SSC/AC-SSC analysis. We prove complete dichotomy theorems across climate prediction, remote sensing, multi-modal geo-foundation models, and navigation systems—with tight sample complexity bounds and quantum advantages reaching 10¹² reduction for geopotential estimation.
Read Publications →Research Areas
In-Context Learning Theory
Mathematical foundations of transformer ICL through SSC/AC-SSC framework, establishing the ICL-Easy/Hard dichotomy with matching upper and lower bounds.
Geospatial Artificial Intelligence
Domain-specific dichotomy theorems for climate models, remote sensing, multi-modal geo-foundation models, and navigation systems.
Quantum Geodesy
Quantum-enhanced geopotential estimation with Heisenberg-limited gravimetry, achieving 10¹² sample complexity reduction over classical sensors.
Geospatial Intelligence
Theoretical frameworks for GEOINT applications including imagery analysis, change detection, and multi-INT fusion with foundation models.