Join us in San Jose, CA, on December 14 – 17, 2021 at the Symposium on Edge Computing (SEC’21) to learn about the latest research and development on edge computing. The preliminary program is now available, featuring an outstanding combination of 5 keynotes, 2 panels, 25 peer-reviewed papers, 1 poster/demo session, 1 PhD forum, and[…]
Saman Biookaghazadeh successfully defended his PhD thesis on FPGA-Based Edge-Computing Acceleration. Congtulations! Dr. Biookaghazadeh!
The spread of the Corona Virus Disease 2019 (COVID-19) has reached pandemic levels across the globe. There is an urgent need to develop CT apps that not only monitor but also intervene to limit COVID-19 spread while respecting user security and privacy. Sponsored by National Science Foundation, this project addresses this challenge via Federated Analytics[…]
The Fifth ACM/IEEE Symposium on Edge Computing (SEC) seeks to present exciting, innovative research related to the design, implementation, analysis, evaluation, and deployment of computer systems and applications at the network edge. SEC is a forum for top researchers, engineers, students, entrepreneurs, and government officials come together under one roof to discuss the opportunities and[…]
Call for Papers for the 3rd USENIX Workshop on Hot Topics in Edge Computing: due Thursday, February 20, 2020 HotEdge ‘20 (#HotEdge20)(https://lnkd.in/gBH_DKf) will take place on April 30, 2020, in Santa Clara, CA, co-located with the 2020 USENIX Conference on Operational Machine Learning. HotEdge ‘20 is the flagship workshop on edge computing (#edgecomputing) and a hot venue for researchers and practitioners from both academia and industry to discuss[…]
Congratulations to Ryunu for her presentation of USENIX ATC ’19 papers on “SmartDedup: Optimizing Deduplication for Resource-constrained Devices” and Yitao for his presentation of HotEdge ’19 workshop on “Exploring the Use of Synthetic Gradients for Distributed Deep Learning across Cloud and Edge Resources”.
Arizona State University Associate Professor Ming Zhao leads the development of GEARS, a big data computing infrastructure designed for today’s demanding big data challenges.
Dr. Ming Zhao brings cloud computing service companies a step closer to providing reliable performance guarantees.
Ragini’s EDGE’18 paper, “Are Existing Knowledge Transfer Techniques Effective For Deep Learning on Edge Devices?” studies distributed deep learning techniques that exploit the knowledge trained from a deep network in the cloud to improve the speed and accuracy of small networks on the devices.
Ragini Sharma successfully defended her master thesis on “A Study on Knowledge Transfer Techniques to Support Deep Learning on Edge Devices”. Her work studies the use of knowledge transfer techniques to support distributed deep learning that exploits the knowledge from deep networks trained in the cloud to improve the accuracy and speed of small networks[…]