2017, 2018, 2019
Lecture Topic: Support Vector Machines as part of Special Topics: Sensing and Data Mining for Smart Structures and Systems (12-761)
Course description: Support Vector Machine (SVM) is a deterministic supervised learning approach used for regression and classification. In this lecture, I intuitively discuss the main concepts of SVM and provide its mathematical formulation.
Lecture Topic: Model Performance, Bias and Variance as part of Special Topics: Sensing and Data Mining for Smart Structures and Systems (12-761)
Course description: Assessing the machine learning model performance is an important step in developing accurate ML models. Furthermore, understanding bias, variance, and noise are very important in understanding basic ML concepts such as overfitting which are necessary for model training and troubleshooting. In this course, I discuss these concepts intuitively and through real-life examples (and provide mathematical formulations).
Lecture Topic: Tutorial for Data Collection using Arduino as part of Special Topics: Sensing and Data Mining for Smart Structures and Systems (12-761)
Course description: Arduino is an easy-to-use and cheap microcontroller which can be used for data collection. In this hands-on course, I have walked the students through the process of setting up their arduinos, making and connecting their sensing boards, and collecting the data. I have also touched upon some sensing concepts such as resolution and sampling frequency when using the Arduino.
Lecture Topic: Basics of Signals and Systems and Stochastic Processes as part of Special Topics: Sensing and Data Mining for Smart Structures and Systems (12-761)
Course description: This course discusses the basics of signals and Systems and stochastic processes which are important in understanding, characterizing, and modelling the dynamic systems. Some of the main concepts discussed in this lecture are: Linear Time-Invariant (LTI) systems, Time vs. Frequency domain analysis, Stochastic Processes and their characteristics, Stationarity, Auto-correlation and Cross-correlation Function, Auto and Power Spectral Densities, Cross-Spectral Density, and Coherence Function.
2017, 2018, 2019
Course Title: Special Topics: Sensing and Data Mining for Smart Structures and Systems (12-761)
Teaching Faculties: Professor Hae Young Noh
Course description: This course introduces smart monitoring systems for physical structures and systems using sensing and data analytics. This is a project-based course and the objective is for students to understand the overall process from obtaining data to specific application performance in a systematic way. For this course, I have guided multiple groups in finding and formulating interesting and unique problems and developing suitable and novel solutions.
Course Title: Civil and Environmental Engineering Design (12-401)
Teaching Faculties: Professor Larry Cartwright, Professor Jim Thompson, Professor Sarah Christian
Course description: A project-based course concentrating on methodology for formulating and solving design problems, characterized by incomplete specifications, open-ended solution space, and partial evaluations.
Future Faculty Candidate
Program description: The Future Faculty Program helps graduate students develop and document their teaching skills in preparation for a faculty career. Participants in this program learn the principles of effective course design and pedagogy through the seminars, receive feedback on their teaching through teaching feedback consultations, and apply what they have learned in completing a course and syllabus design project and a statement of teaching philosophy project.