Teaching Philosophy

Tomoko Matsuo's teaching philosophy is guided by the same enthusiasm for interdisciplinary ideas that motivate her听research. As Norbert Wiener (1894-1964) stated, 鈥�the most fruitful areas for growth of the sciences are those between established fields鈥t is these boundary regions of science that offer the richest opportunities to the qualified investigator鈥�. She听supports听students thrive in interdisciplinary听fields of aerospace engineering sciences by learning together how to overcome听the gap between the development of fundamental scientific understanding and the search for tangible solutions to real-world problems in classroom. Her听teaching philosophy is also informed by听a growing body of research demonstrating the benefits of student-centered teaching approaches and Active Learning听and听Deeper Learning听pedagogical strategies, which help students engage with the material more actively and develop their critical-thinking and self-reflection skills.听The听Deeper Learning framework advances student-centered learning goals with its emphasis on six competencies:听mastering rigorous academic content, learning how to think critically and solve problems, working collaboratively, communicating effectively, directing one鈥檚 own learning by learning how to learn, and developing an academic mindset, which fosters听the ability to acquire and use knowledge to tackle new real-world problems. Recent research suggests its effectiveness in bridging the achievement gap between the听educationally advantaged and disadvantaged. The courses she has taught in the past are as follows.

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ASEN 6055: Data Assimilation and Inverse Methods for Earth and Geospace Observations

Fall 2020 | Fall 2018听| Spring听2017 |

Course Overview

Data assimilation and inverse methods play a key role in integrating remote-sensing and in-situ Earth and space observations into a model of the Earth and geospace system or subsystems, enabling weather prediction and climate projection of high societal relevance.听The goal of this course is to provide the background on听probability, spatial statistics, statistical estimation, numeric optimization, as well as听geophysical nonlinear dynamics, that form the foundation of commonly used data assimilation and inverse methods in the Earth and space sciences,听and to equip students with the knowledge and skills to construct a data assimilation system on their own. 乐播传媒 will: (1) attain a deeper understanding of the underlying statistical principles of data assimilation and inverse methods; (2) actively apply their own understanding of the fundamentals and tradeoffs of different approaches in critiquing current data assimilation research; and (3) develop the skills, confidence and creativity to design and build a data assimilation system of their own.听The course adopt听project-based approaches.听Homework assignments designed to give students听the opportunities to apply the classroom curricula to realistic听estimation problems, and the mid-term exam is used to assess students鈥� theoretical grasp of the methodologies. 乐播传媒 are being asked to work collaboratively on听a specified听mid-term听project听and independently听on听a听final听project of their choice.

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ASEN 6337: Remote Sensing Data Analysis

Fall 2022 | Fall 2019听| Fall 2017 |

Course Overview

With an explosive increase in the availability of high-resolution听remote sensing data, analyzing it has become a big data problem. Increasingly, machine learning is being recognized as a powerful tool for addressing this challenge. The goal of this course is to introduce commonly used machine learning techniques and inverse methods in remote sensing data analysis, equipping students with the knowledge and skills to apply modern data analysis techniques to remotely sensed data on their own. 乐播传媒 will: (1) develop a deeper understanding of machine learning and inverse methods in the context of remote sensing data analysis; (2) actively apply their own understanding of the fundamentals and tradeoffs of different approaches in critiquing current remote sensing data analysis research; and (3) develop the skills, confidence and creativity to design and solve a remote sensing data analysis problem of their choice. This project-based course 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 regression. The course also covers inverse methods used to retrieve geophysical information from remote sensing data. Homework assignments designed as a small data analysis problem听give students听the opportunities to apply the classroom curricula to realistic remote sensing data analysis, and the mid-term exam is used to assess students鈥� theoretical grasp of the methodologies. 乐播传媒 are being asked to work collaboratively on听a specified Kaggle data science project听and independently听on听a听final听project of their choice.

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ASEN 5044: Statistical Estimation for Dynamical Systems

Spring 2020 |

Course Overview

This course introduces students to the theory and methods of state estimation for听general linear and nonlinear dynamical systems, with a particular emphasis on aerospace听and other engineering applications. Major topics include: (1) review of applied probability听and statistics, (2) modeling and optimal state estimation for stochastic dynamical systems,听(3) theory and design of Kalman lters for linear systems, and (4) linearized and extended听Kalman lters for non-linear systems. 听乐播传媒 will gain both a fundamental and practical understanding of estimation algorithms听from a general dynamical systems standpoint. This will prepare them to tackle challenging听estimation problems that they will eventually encounter in later courses and in their own听professional/research pursuits.

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ASEN 4057: Aerospace Software

Spring 2020听| Spring 2019 | Spring 2018 |

Course Overview

Aerospace engineers may go through their entire undergraduate education curriculum and have only a single formal course in computing, which often does not even cover formal programming, much less any details of the underlying processes by which the computing is accomplished. This is true despite an ever-increasing reliance on software by academia and industry for simulation and operational purposes. The goal听of this course is an attempt to fill that void.听The goal of this course is to (1) provide aerospace engineers with an overview of key software and hardware computing concepts utilized in academia and industry, and (2) give the background necessary to tackle programming projects confidently on different computing platforms with various software tools and programming languages. 乐播传媒 will听gain deeper and broad technical computing experience including debugging, code management and optimization, documentations, and collaborative software development,听actively apply these technical skills to solving relevant aerospace engineering problems,听and develop the key skills and traits to be a good programmer and software developer in academia and industry.

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ASEN 5210: Remote Sensing Seminar

Fall 2019 |

Course Overview

The Remote Sensing Seminar Series 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. The goal of this course is (1) to expose students to a wide range of current remote sensing research topics being conducted in academia and industry; (2) to provide students with opportunities to actively engage in technical discussion with faculty and researchers.听

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ASEN 1320: Aerospace Computing and Engineering Applications

Spring 2022 | Fall 2020 |听

Course Overview

Most aerospace engineering programs require literacy in some programming language (e.g.,听MATLAB, C++) for automating various types of numerical and symbolic computation.听The course is for students with little or no prior experience in programming and teaches听basic programming concepts and useful tools for solving engineering problems with an emphasis on aerospace applications.听The goal of this course is to build the basic foundation in computing and programing required to succeed in the sophomore and junior curriculum in aerospace engineering and听other related domains of engineering. 乐播传媒 will develop an understanding of the following concepts and skills in order to be able code in C++ and MATLAB to solve basic听computing problems: the overall structure of computing program, difference between a complied and an interpreted language, different primitive data types and arrays, fundamental programming constructs such as variables, assignments, conditionals, and looping, functional programming, file I/O, visualization in MATLAB, as well as concepts of class, object and object-oriented programing in C++.

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