Camilo has been developing and advancing the extended dynamic mode decomposition (EDMD) method for many years, including the proposal of his own variant, the pqEDMD. His work spans both methodological contributions and practical applications of EDMD in system modeling and identification. Beyond modeling and identification, he has contributed analytical results such as the characterization of regions of attraction of asymptotically stable points when the EDMD approximates the Koopman operator. He has also designed techniques to translate EDMD structure into forms suitable for use in model predictive control frameworks.

Currently, his research focuses on integrating EDMD, neural ODEs, and physics informed NN with condition monitoring methods, aiming to bridge advanced operator-theoretic approaches with practical tools for system health assessment and control.