Graduate (List)

In the course, basics of machine leaning algorithms will be introduced including Deep Neural Network, Convolution Neural Network, Recurrent Neural Network and Reinforcement Learning. Especially, basic principles and the key applications of reinforcement learning (RL) will be introduced and discussed. In addition, AI accelerator computing schemes such as GPU, HBM, and AI chips will be discussed as well. Finally, these RL methods will be applied to estimate, design, and optimization of the electromagnetic systems, including antenna, circuits, devices and semiconductors.

In this course, we will introduce the basic model algorithms, the architectures, and the training principles of mostly advanced ultra large-scale AI models, and their engineering applications. The class will cover the ultra large-scale AI models including Convolution Neural Network, Recurrent Neural Network, Reinforcement Learning as well as transformer based network, diffusion models, VAE, and other multimodal (LLM and VLLM) generative models. In addition, these advanced models will be used for engineering purposes such as estimations, design optimizations, and decision makings.

This course is designed to cover the special topics of current interests in optical engineering.

This course covers topics of interest in physical electronics at the graduate level students. The course content is specifically designed by the instructor.

This course covers topics of interest in solid-state physics for students at the graduate level. The content is specifically designed by the instructor.

In this course, I will lecture to electrical engineering students on the engineering applications of nanostructured semiconductors. The focus is primarily on the electronic structure of two-dimensional semiconductors, their modifications, and low-dimensional nanostructure transistors. In the latter part of the course, I also cover some quantum materials beyond semiconductors. Finally, we read and discuss relevant research papers.

This course covers topics of interest in integrated circuits for students at the graduate level. The course content is specifically designed by the instructor.

This course covers recent issues related with the VLSI System design.

This course introduces a basic concept, definition and computational models of Intelligence System. In addition, this course covers a relationship between the computational model with the real brain in a psychology and brain anatomy perspectives. As an example of Intelligence System on a Chip, Visual Attention Model and its multi-core processor implementation are studied with machine learning algorithms.

This course is an advanced project-based learning (PBL) course mainly targeting the students with “Interdisciplinary Incubation Center for Biomedical and System Semiconductor”. In the course, a student selects his/her own research project and practice leading the project, under supervision of the instructor and other professors/researchers with the center.