Graduate (List)

Image restoration and enhancement problems have been treated as fundamental issues of image processing and computer vision. The image restoration estimates the original (clean) images from their corrupted and noisy image inputs in many forms of motion blur, noise and camera mis-focus etc. Therefore it is performed by reversing the degradation process that causes corrupted images. Different from image restoration, the image enhancement aims at improving the subjective perceptual quality of corrupted images, not necessarily producing the realistic data from a scientific point of view. In general, the image enhancement uses no a priori models of the processes that have created the images.
In recent years, the demand for ultra-high quality images has been increased with the advance in image restoration and enhancement. Especially, deep learning based approaches to image restoration and enhancement have made a great success in performance against the traditional approaches. In this class, we review the traditional approaches to image restoration and enhancement with the analysis of their limitation, and study very recent deep learning based methods using convolution neural networks, recurrent neural networks and generative networks. The students are exposed and experienced to very recent advanced methods in image restoration and enhancement via class lectures and homework assignments and terms projects.

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

In the course, basic model architecture and training principles of mostly advanced machine leaning algorithms and their applications will be introduced. The class will cover the Convolution Neural Network, Recurrent Neural Network, Reinforcement Learning as well as transformer network and generative models, especially used in chatGPT. In addition, these advanced transformer models will be used to estimate designs, and to optimize the high-speed and frequency electromagnetic systems, including antenna, circuits, devices, wireless power transfer systems, packages, and semiconductors. These application examples will be followed with discussions.

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.