日期：2023/03/29 - 2023/03/29
主讲人：Dr. Zhenyu Liu, postdoctoral associate in the Laboratory for Information and Decision Systems at MIT
Decentralized inference is critical in several emerging applications such as Internet of Things, intelligent connected vehicles, and network navigation. The aim of decentralized inference is to estimate time-varying states via multi-modal sensing and inter-node communication in networked systems.
In this seminar, the speaker first introduces a theoretical framework for decentralized inference, where multiple nodes in a network aim to infer their unknown states in real time under sensing and communication constraints. Specifically, conditions on the network’s sensing and communication capabilities for achieving desirable inference accuracy are derived. Next, the speaker presents systems for network navigation, where commercially available devices are used for inferring users’ positions in real time. These systems employ efficient information fusion and resource management techniques for improving the positioning accuracy.
Zhenyu Liu received the Ph.D. degree in networks and statistics from the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 2022. He received the B.S. degree (with honor) and M.S. degree in electronic engineering from Tsinghua University, Beijing, China, in 2011 and 2014, respectively. Currently, he is a postdoctoral associate in the Laboratory for Information and Decision Systems at MIT. His research interests include Internet of Things, network localization, decentralized inference, wireless communication, networked control, and quantum information science. He received the First Prize of the IEEE Communications Society’s Student Competition in 2016 and 2019, the R&D 100 Award in 2018, and the Best Paper Award at the IEEE Latin- American Conference on Communications in 2017.