Professor Matsuo's research aims to advance the science and engineering of forecasting, as applied to the Earth’s atmosphere from the ground to near-Earth space environments, while developing fundamental understanding of the predictability of a coupled Earth-Geospace system. Prediction of constantly changing environmental conditions requires a systematic integration of observations with a first-principles models using data assimilation. Data assimilation reduces uncertainties in initial conditions and drivers, extending the predictive capability of numerical models, and is used for designing of future missions and targeting of observations to maximize scientific returns of observing systems. Professor Matsuo's research also focuses on methodological problems, including the development of the development of scalable data assimilation methods for high-dimensional problems, inversion and machine learning techniques to extract relevant geophysical information from large volumes of data.
keywords
Atmospheric sciences and space physics, Predictability of geophysical dynamical systems, Data assimilation, Statistical (Machine) Learning
APPM 4510 - Data Assimilation in High Dimensional Dynamical Systems
Primary Instructor
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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
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Fall 2020 / Spring 2022 / Spring 2023
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 4018 - Senior Projects 1: Design Synthesis
Primary Instructor
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Fall 2024
Focuses on the synthesis of technical knowledge, project management, design process, leadership, and communications within a team environment. Students progress through the design process beginning with requirements development, then preliminary design and culminating with critical design. Offered fall only.
ASEN 4057 - Aerospace Software
Primary Instructor
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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 I
Primary Instructor
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Fall 2022 / Spring 2023
Exposes MS and PhD students to project management and systems engineering disciplines while working a complex aerospace engineering project as part of a project team. The project team may perform some or all of the following project activities during this first semester of the two-semester course sequence: requirements, definition, design and design review, build, test, and verification. Recommended prerequisite: ASEN 4138 or ASEN 5148 or ASEN 5158 or instructor consent required.
ASEN 5044 - Statistical Estimation for Dynamical Systems
Primary Instructor
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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
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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 6028 - Graduate Projects II
Primary Instructor
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Spring 2023
Exposes MS and PhD students to leadership positions in project management and systems engineering while working a complex aerospace engineering project as part of a project team. The project team may perform some or all of the following project activities during this second semester of the two-semester course sequence: requirements definition, design and design review, build, test, and verification. Recommended prerequisite: ASEN 4138 or ASEN 5148 or ASEN 5018 or ASEN 5158 or instructor consent required.
ASEN 6055 - Data Assimilation & Inverse Methods for Earth & Geospace Observations
Primary Instructor
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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
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Fall 2019 / Fall 2024
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
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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.
