Graduate (List)

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.

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 GPT. In addition, these advanced transformer models will be used to estimate designs, and to optimize the high performance engineering systems, including antenna, circuits, devices, wireless power transfer systems, packages, and semiconductors. These application examples will be followed with discussions.

This course examines optical and photonic systems inspired by biological structures in nature. Topics include bioinspired vision systems, structural color mechanisms, and thermal radiation control strategies. Emphasis is placed on both fundamental principles and recent advances in materials and device applications.

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

This course is designed to provide understanding of quantum information processing hardware and protocols in a comprehensive fashion. Based on the very basic treatment of quantum information processing. Considering the nature of the course, the evaluation will be based on student presentation. Presentation topics will be provided by the lecturer and the proper guidance will be given.

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.