Innovative GDL models for anomaly detection

The ultimate goal of the project is to develop efficient, systematic, and reliable learning mechanisms for the onboard exploration by explicitly integrating both space and time dimensions into the knowledge representation at multiple spectral and spatial resolutions; using radiance data from NASA’s satellites, Topological and Geometric Machine Learning methods, and High-End Computing (HEC) systems.

UTDallas - NASA-JPL

Ignacio Segovia-Dominguez
Ignacio Segovia-Dominguez
Visiting Scientist / Postdoctoral Research Associate

My research interests include topological and geometric methods in statistics and machine learning, analysis of complex dynamic networks, evolutionary computation, and computational statistics.