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
ASEN 4057 - Aerospace Software
Spring 2018 / Spring 2019
Provides an overview of prevalent software and hardware computing concepts utilized in practice and industry. Establishes the background necessary to tackle programming projects on different computing platforms with various software tools and programming languages.
ASEN 6337 - Remote Sensing Data Analysis
Covers some of the most commonly used machine learning techniques in remote sensing data analysis, specifically for clustering, classification, feature extraction and dimensionality reduction, and inverse methods used to retrieve geophysical information from remote sensing data. Hands-on computational homework and group and individual projects provide opportunities to apply classroom curricula to real remote sensing data.
ASEN 6519 - Special Topics
Reflects upon specialized aspects of aerospace engineering sciences. Course content is indicated in the online Schedule Planner. May be repeated up to 9 total credit hours. Recommended prerequisite: varies.