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Matsuo, Tomoko Assistant Professor

Positions

Research Areas research areas

Research

research overview

  • 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.

keywords

  • 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

Publications

selected publications

Teaching

courses taught

  • ASEN 4057 - Aerospace Software
    Primary Instructor - Spring 2018 / Spring 2019 / Spring 2020
    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 5044 - Statistical Estimation for Dynamical Systems
    Primary Instructor - Spring 2020
    Introduces theory and methods of statistical estimation for general linear and nonlinear dynamical systems, with emphasis on aerospace engineering applications. Major topics include: review of applied probability and statistics; optimal parameter and dynamic state estimation; theory and design of Kalman filters for linear systems; extended/unscented Kalman filters and general Bayesian filters for non-linear systems.
  • ASEN 5210 - Remote Sensing Seminar
    Primary Instructor - Fall 2019
    Covers subjects pertinent to remote sensing of the Earth, including oceanography, meteorology, vegetation monitoring, and geology. Emphasizes techniques for extracting geophysical information from satellite data. Course requirement for Remote Sensing Certificate. Formerly ASEN 6210.
  • ASEN 6337 - Remote Sensing Data Analysis
    Primary Instructor - Fall 2019
    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
    Primary Instructor - Fall 2018
    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.
  • ATOC 7500 - Special Topics in Atmospheric and Oceanic Sciences
    Primary Instructor - Fall 2019
    Acquaints students with current research in atmospheres, oceans, and climate. Topics may vary each semester. May be repeated up to 9 total credit hours. Students may register for more than one section of this course in the same semester.

Background

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