Tomoko Matsuo's teaching philosophy is guided by the same enthusiasm for interdisciplinary ideas that motivate her research. As Norbert Wiener (1894-1964) stated, “the most fruitful areas for growth of the sciences are those between established fields…it is these boundary regions of science that offer the richest opportunities to the qualified investigator”. She supports students thrive in interdisciplinary fields of aerospace engineering sciences by learning together how to overcome the gap between the development of fundamental scientific understanding and the search for tangible solutions to real-world problems in classroom. Her teaching philosophy is also informed by a growing body of research demonstrating the benefits of student-centered teaching approaches and Active Learning and Deeper Learning pedagogical strategies, which help students engage with the material more actively and develop their critical-thinking and self-reflection skills. The Deeper Learning framework advances student-centered learning goals with its emphasis on six competencies: mastering rigorous academic content, learning how to think critically and solve problems, working collaboratively, communicating effectively, directing one’s own learning by learning how to learn, and developing an academic mindset, which fosters 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 educationally advantaged and disadvantaged. The courses she has taught in the past are as follows.
ASEN 6055: Data Assimilation and Inverse Methods for Earth and Geospace Observations
Fall 2020 | Fall 2018 | Spring 2017 |
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. The goal of this course is to provide the background on probability, spatial statistics, statistical estimation, numeric optimization, as well as geophysical nonlinear dynamics, that form the foundation of commonly used data assimilation and inverse methods in the Earth and space sciences, and to equip students with the knowledge and skills to construct a data assimilation system on their own. Students 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. The course adopt project-based approaches. Homework assignments designed to give students the opportunities to apply the classroom curricula to realistic estimation problems, and the mid-term exam is used to assess students’ theoretical grasp of the methodologies. Students are being asked to work collaboratively on a specified mid-term project and independently on a final project of their choice.
ASEN 6337: Remote Sensing Data Analysis
Fall 2022 | Fall 2019 | Fall 2017 |
With an explosive increase in the availability of high-resolution 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. Students 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 give students the opportunities to apply the classroom curricula to realistic remote sensing data analysis, and the mid-term exam is used to assess students’ theoretical grasp of the methodologies. Students are being asked to work collaboratively on a specified Kaggle data science project and independently on a final project of their choice.
ASEN 5044: Statistical Estimation for Dynamical Systems
Spring 2020 |
This course introduces students to the theory and methods of state estimation for general linear and nonlinear dynamical systems, with a particular emphasis on aerospace and other engineering applications. Major topics include: (1) review of applied probability and statistics, (2) modeling and optimal state estimation for stochastic dynamical systems, (3) theory and design of Kalman lters for linear systems, and (4) linearized and extended Kalman lters for non-linear systems. Students will gain both a fundamental and practical understanding of estimation algorithms from a general dynamical systems standpoint. This will prepare them to tackle challenging estimation problems that they will eventually encounter in later courses and in their own professional/research pursuits.
ASEN 4057: Aerospace Software
Spring 2020 | Spring 2019 | Spring 2018 |
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 of this course is an attempt to fill that void. 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. Students will gain deeper and broad technical computing experience including debugging, code management and optimization, documentations, and collaborative software development, actively apply these technical skills to solving relevant aerospace engineering problems, and develop the key skills and traits to be a good programmer and software developer in academia and industry.
ASEN 5210: Remote Sensing Seminar
Fall 2019 |
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.
ASEN 1320: Aerospace Computing and Engineering Applications
Spring 2022 | Fall 2020 |
Most aerospace engineering programs require literacy in some programming language (e.g., MATLAB, C++) for automating various types of numerical and symbolic computation. The course is for students with little or no prior experience in programming and teaches basic programming concepts and useful tools for solving engineering problems with an emphasis on aerospace applications. 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 other related domains of engineering. Students will develop an understanding of the following concepts and skills in order to be able code in C++ and MATLAB to solve basic 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++.