MARIA CARMEN FERNANDEZ ROSELL

MARIA CARMEN FERNANDEZ ROSELL is a Professor in the Department of Mathematical Analysis at the Faculty of Mathematics, University of Valencia. She is a member of the ESALDI research group (Spaces and Algebras of Differentiable Functions), focusing on functional analysis, operator theory, and time-frequency analysis, with significant interdisciplinary contributions in Bayesian statistics and econometrics.

Her research spans Functional Analysis, Operator Theory, Harmonic Analysis, and Bayesian Modeling. She investigates ultradifferentiable functions, pseudodifferential operators, composition operators on function spaces, and time-frequency localization. Her statistical work includes Bayesian inference for fat tails, skewness, model averaging, and spatial modeling, often in collaboration with prominent statisticians like Mark F. J. Steel and Peter J. Green.

The most recent publications highlight her expertise in composition operators on Gelfand-Shilov classes, spectral analysis, and characterizations of function spaces via harmonic oscillators. Her work bridges pure mathematical analysis with applied statistical modeling, particularly in econometrics and biological systems.

• On Bayesian modeling of fat tails and skewness (1998)
• Benchmark priors for Bayesian model averaging (2001)
• Annihilating sets for the short time Fourier transform (2010)
• Dynamics and spectra of composition operators on the Schwartz space (2018)

She has supervised doctoral research, including Dr. Manuel Valdivia Ureña. Her work is supported by extensive collaborations, particularly with Antonio Galbis, José Bonet, and Mark F. J. Steel. She has contributed to major journals such as the Journal of Mathematical Analysis and Applications, Journal of Econometrics, and The Journal of Fourier Analysis and Applications.

She is active in the research group ESALDI, which focuses on the theory of spaces and algebras of differentiable functions, exploring structural properties, duality, and applications to partial differential equations and signal analysis.