Sensing AI for Digital Healthcare

Prof. Chenshu Wu, Assistant Professor, The University of Hong Kong

Date: 13 June 2024 (Thursday)
Time: 4:20-4:45pm HKT
Venue: Cheung On Tak Lecture Theater (LT-E), HKUST
Title: Sensing AI for Digital Healthcare
Speaker: Prof. Chenshu Wu, Assistant Professor, The University of Hong Kong

Abstract

Sensing AI is revolutionizing digital health by translating ubiquitous healthcare "from benchside to bedside". In this talk, we will introduce cutting-edge technologies in wireless sensing using radio and acoustic signals and their diverse applications in healthcare. We will show how wireless sensing enables accurate and continuous measurement of human-centric health and behavioral information, such as motion, respiration, heartbeats, sleep, gait, stress levels, etc., all in a non-contact, non-intrusive, and privacy-preserving manner. We will conclude the talk by discussing interdisciplinary opportunities and directions in sensing AI for health, a rapidly evolving field to shape the future of personalized health and precision medicine.

Biography

Dr. Chenshu Wu is an Assistant Professor in the Department of Computer Science, The University of Hong Kong, where he leads the HKU AIoT Lab. He received his B.E. and Ph.D. degrees both from Tsinghua University. He served as the Chief Scientist and currently consults for Origin AI. His research focuses on wireless sensing, ubiquitous computing, smart healthcare. He has published 3 books and 100+ papers in prestigious conferences and journals like SIGCOMM, NSDI, MobiCom, MobiSys, UbiComp, with several best paper awards. He holds 90+ filed/granted US and international patents. His research on WiFi sensing has been deployed as award-winning products worldwide. He is a recipient of NSFC Excellent Young Scientists Fund (HK & Macau), NAM Healthy Longevity Catalyst Award, and CCF Outstanding Doctoral Dissertation Award. He is currently an associate editor of ACM IMWUT and serves the TPC Co-Chair of IEEE ICPADS 2024. More information at https://cswu.me/