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Publications in VIVO

Matsuo, Tomoko

Assistant Professor and H. Joseph Smead Faculty Fellow


Research Areas research areas


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.


  • Atmospheric sciences and space physics, Predictability of geophysical dynamical systems, Data assimilation, Statistical (Machine) Learning


selected publications


courses taught

  • APPM 4510 - Data Assimilation in High Dimensional Dynamical Systems
    Primary Instructor - Fall 2020
    Develops and analyzes approximate methods of solving the Bayesian inverse problem for high-dimensional dynamical systems. After briefly reviewing mathematical foundations in probability and statistics, the course covers the Kalman filter, particle filters, variational methods and ensemble Kalman filters. The emphasis is on mathematical formulation and analysis of methods. Same as APPM 5510, STAT 4250 and STAT 5250.
  • ASEN 1320 - Aerospace Computing and Engineering Applications
    Primary Instructor - Fall 2020 / Spring 2022
    Uses problems and tools from Engineering. Teaches techniques for writing computer programs in higher level programming languages to solve problems of interest in Engineering and other domains. Appropriate for students with little or no prior experience in programming.
  • 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 5018 - Graduate Projects II
    Primary Instructor - Fall 2022
  • 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 and space, including oceanography, meteorology, vegetation monitoring, geology, geodesy and space science, with emphasis on techniques for extracting geophysical information from data from airborne and spaceborne platforms. Course requirement for Remote Sensing Certificate. Formerly ASEN 6210.
  • ASEN 6055 - Data Assimilation & Inverse Methods for Earth & Geospace Observations
    Primary Instructor - Fall 2020
    Covers a selection of topics in probability theory, spatial statistics, estimation theory, numeric optimization, and geophysical nonlinear dynamics that form the foundation of commonly used data assimilation and inverse methods in the Earth and Space Sciences. Hands-on computational homework and projects provide opportunities to apply classroom curricula to realistic examples in the context of data assimilation.
  • 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.
  • ASEN 6950 - Master's Thesis
    Primary Instructor - Fall 2020 / Spring 2021 / Fall 2021


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