Professor Matsuo's main research interest is the design and development of statistical inferential methodologies for Earth and Geospace environmental observations, including the modeling of spatio-temporal random scalar and vector fields and designing sequential Monte Carlo methods for high-dimensional dynamical systems. Her research focuses on data assimilation of various types of remotely sensed and in-situ measurements into numerical models of Earth and Geospace systems, encompassing the Earth's whole atmosphere, ionosphere and magnetosphere. She is also interested in integrating design and development of engineering systems into geophysical modeling and prediction. Other areas of interest include the quantification of predictability of the whole atmosphere and ionosphere through applications of the dynamical systems theory, estimation theory, and information theory.
Data assimilation and inverse methods, applications to remote sensing and in-situ data of the atmosphere and geospace, Atmospheric and space physics, Spatial statistics, Estimation theory, Dynamical systems theory