Wang Wensi

照片 王文思


Personal profile

GenderMale

DegreesPh.D.

TitleProfessor

E-mail:wensi.wang@bjut.edu.cn

Current Professional Societies:IEEE

Research Areas:IC design and A.I. Edge Computing

Honors:Beijing Nova-Star Programme

Publications:http://yanzhao.bjut.edu.cn/ds/2/1/201639/14574887971912313_1.html

Personal Statement:

Prof. Wensi Wang graduated from Beijing University of Technology with a Bachelor's Degree in 2005, and graduated from the Tyndall National Institute in Ireland with a Ph.D. in Microelectronics in 2012. He served as a Postdoctoral Fellow and Assistant Researcher at Tyndall National Institute in Ireland from 2012 to 2015. He has been teaching at Beijing University of Technology since September 2015.

Main research areas: Field 1. Medical implantable device power management and other analog IC design; Field 2. Application of A.I. edge computing technology in various applications

Field 1 Analog integrated circuit design: Mainly focuses on the power supply of medical implantable devices (such as cardiac pacemakers and DBS). The research content includes but not limited to milliwatt-level high-efficiency DC-DC converter, Wireless energy transmission IC and antenna, energy harvesting technology and its modeling technology, highly integrated power supply design on Active Interposer, artificial intelligence algorithm assisted power IC design automation etc.,

(Master students who apply are required to have good analog integrated circuit design basics. Undergraduates majoring in Electronics and Microelectronics are also welcome to apply.)

Field 2 Algorithm research of edge intelligence technology: mainly use advanced AI chips to conduct research around sensor and multimedia data analysis, and the research content is application oriented for practical problems in the industry. Such as the use of edge computing technology to carry out attribution analysis of environmental big data; classification analysis of aquatic plants such as algae; acoustic fault diagnosis of gear machines; fire evacuation analysis of large buildings, etc.

(Master students who apply are required to have good programming ability and algorithm analysis ability. Undergraduates majoring in Computer Science or Software are also welcome to apply)