Short Bio:
Yuantao Gu received the B.E. degree from Xi'an Jiaotong University in 1998, and the Ph.D. degree with honor from Tsinghua University in 2003, both in Electronic Engineering. He joined the faculty of Tsinghua University in 2003 and is now a professor with Department of Electronic Engineering. He was a visiting scientist at Research Laboratory of Electronics at Massachusetts Institute of Technology during 2012 to 2013 and Department of Electrical Engineering and Computer Science at the University of Michigan in Ann Arbor during 2015. His research interests include high-dimensional signal processing, dimensionality reduction, optimization, sparse signal recovery, temporal-space and graph signal processing. He is a Senior Area Editor for IEEE Transactions on Signal Processing and an Elected Member of both IEEE Machine Learning for Signal Processing (MLSP) Technical Committee and IEEE Signal Processing Theory and Methods (SPTM) Technical Committee. He received the Best Paper Award of IEEE GlobalSIP in 2015, the Award for Best Presentation of Journal Paper of IEEE ChinaSIP in 2015, and Zhang Si-Ying (CCDC) Outstanding Youth Paper Award (with his student) in 2017.

Speech Title: Communication-efficient Decentralized Signal Detection

Abstract:
Distributed systems like Wireless Sensor Network (WSN) and Internet of Things (IoT) have been applied in various fields such as quality control and environment monitoring. In these systems, change-point detection (CPD) is a fundamental component, which monitors whether a system is in its normal state and detects promptly when the system catastrophically drops into abnormal states. Such a component naturally entails operating with energy-limited sensors in a distributed manner. Due to this energy limitation, how to detect change-points efficiently becomes a key issue. In this keynote, we will first review the basic ingredients of change-point detection, including the problem formulation, online algorithms, and applications. Then we will introduce our recent work, an energy-efficient change-point detection algorithm based on the request-response and censoring scheme. These two schemes help sensors extract the most useful information from their neighbors and avoids radiating inessential information. In this way, the new algorithm greatly reduces energy cost resulting from communication without deteriorating the detection performance. Finally, we will demonstrate the efficiency and validity of the new algorithm by applying it on a real-world task of physical activity detection.