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Abstract: Machine learning (ML) methods have exploded in the past half-dozen years and is being applied to a huge range of problems in many areas. Initial results relied on server-oriented computations, but many applications will require deploying aspects of machine learning throughout the network hierarchy. Several factors motivate the development of Edge Intelligence architectures and algorithms: network bandwidth, power consumption, latency, privacy, etc. This talk will start the motivation for edge intelligence with several examples from manufacturing and health care and outline some important problems.Bio: Marilyn Wolf is Farmer Distinguished Chair in Embedded Computing Systems and GRA Eminent Scholar at the Georgia Institute of Technology. She received her BS, MS, and PhD in electrical engineering from Stanford University. She was with AT&T Bell Laboratories from 1984 to 1989 and was on the faculty of Princeton University from 1989 to 2007. Her research interests include cyber-physical systems, Internet-of-Things, embedded computing, embedded computer vision, and VLSI systems. She has received the ASEE Terman Award and IEEE Circuits and Systems Society Education Award. She is a Fellow of the IEEE and ACM.