报告主题：Diffusion Models in Generative AI Enabled Network Optimization

报 告 人：Dusit Niyato

报告时间：2023年11月3日（周五），上午9:30-11:30

腾讯会议：841358518

报告简介：

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a variety of applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This presentation gives an introduction on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains. The presentation first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), Semantic Communications (SemCom), and Internet of Vehicles (IoV) networks. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design.

报告人简介：

Dusit Niyato is currently a President's Chair Professor in Computer Science and Engineering in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. His research interests are in the areas of distributed collaborative machine learning, Internet of Things (IoT), edge intelligent metaverse, mobile and distributed computing, and wireless networks. He won the IEEE Communications Society (ComSoc) Best Survey Paper Award, IEEE Asia-Pacific Board (APB) Outstanding Paper Award, the IEEE Computer Society Middle Career Researcher Award for Excellence in Scalable Computing. Currently, Dusit is serving as Editor-in-Chief of IEEE Communications Surveys and Tutorials (impact factor of 35.6), an area editor of IEEE Transactions on Vehicular Technology, editor of IEEE Transactions on Wireless Communications, associate editor of IEEE Internet of Things Journal, IEEE Transactions on Mobile Computing, IEEE Wireless Communications, IEEE Network, IEEE Transactions on Information Forensics and Security (TIFS), and ACM Computing Surveys. He was a guest editor of IEEE Journal on Selected Areas on Communications. He was a Distinguished Lecturer of the IEEE Communications Society for 2016-2017. He was named the 2017-2022 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET. 

联系人：卢为党 教授

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