Tara Paranjpe’s Honors Thesis: “Investigating Stress Among Police Training Cadets Using Machine Learning” Sang-Hun Sim’s Master Thesis: “Exploration of Edge Machine Learning-Based Stress Prediction Using Wearable Devices”
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[…]
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[…]
RTVirt is a new solution for enabling time-sensitive applications (such as emergency planning and management applications) on virtualized systems (such as public and private cloud systems) through cross-layer scheduling. It allows the two levels of schedulers on a virtualized system to communicate key scheduling information and coordinate on the scheduling decisions. It enables optimal multiprocessor[…]
GEARS is an enerGy-Efficient big-datA Research System at Arizona State University, for studying heterogeneous and dynamic data by employing heterogeneous computing and storage resources and co-designing the software and hardware components of the system. GEARS is sponsored by National Science Foundation award CNS-1629888. Read more about it here.