Teaching

This course introduces the concepts of signals and systems and teaches the mathematical and computational tools for the analysis of engineering signals and systems. The course content covers Fourier representations of continuous-time and discrete-time signals, frequency spectrum, linear and time-invariant systems, impulse response, convolution sum and convolution integral, frequency response, Laplace transform and z-transform representations of signals and systems. Sampling theorem and the relation between continuous-time and discrete-time signals will be discussed. This course includes mandatory laboratory modules.

Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The capability to interpret data in a visual way has become an essential skill. Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable. This course introduces practical methods and tools to visualize data to communicate complex information clearly and efficiently. Students will learn how to present, visualize, and communicate data in various forms clearly and concisely. The ideas and principles in both aesthetic form and functionality will be emphasized.

Data mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in dataset, perform predictions and generally improve the performance through interaction with data. It is currently regarded as the key element of a more general knowledge discovery process that deals with extracting useful knowledge from raw data. This course will offer students advanced algorithms for mining various types of complex data, especially imaging data. The curriculum will start with the classical data mining methods for tabular and graph data and next move into data-driven imaging data mining with advanced algorithms. We will review different model architectures and learning algorithms such as clustering, classification and graph neural networks. We will go into a few research topics including self-supervised machine learning and various real-world applications.