Energy-efficient and Privacy-preserving Embedded Intelligence



Title: Energy-efficient and Privacy-preserving Embedded Intelligence

Abstract: Machine learning, especially deep learning has become one of the most attractive research hotspots and proven to be effective to solve a wide variety of research challenges especially for pattern recognition and computer vision. However, the energy-efficiency and privacy-preserving issues arises when introducing the data-driven machine learning approaches on personal embedded devices like smartphones, smart glasses, smart watches etc. In this presentation, I will talk about my previous and current research efforts on solving the efficiency and privacy problems of embedded intelligence.

Bio: Yiran Shen is currently a research fellow at CSIRO, Australia. He obtained his PhD from University of New South Wales and Bachelor degree from Shandong University. His research spans different research communities including sensor networking and system, pervasive computing and computer vision and it has outputted over 30 peer-reviewed papers. He publishes regularly on top conferences like SenSys, IPSN, Ubicomp, Percom, CVPR and high impact journals like TMC, TON, TDSC, Computer Networks as leading authors (first or corresponding author). He was awardee of Google PhD Fellowship in 2013 and MIT Smart Scholar Fellowship in 2014.