His work in the area of real-time computing has spanned decades and has had impact in a broad range of applications.
His work in the area of real-time computing has spanned decades and has had impact in a broad range of applications.
Prof. Mao and her students have played an important role in understanding the efficiency, security, and performance of a number of mobile systems.
A group of researchers at U-M is working on the full big data stack for training machine learning models on millions of devices worldwide.
His work is in complexity theory of distributed computing.
A new system called Leap earned a Best Paper award at USENIX ATC ‘20 for producing remote memory access speed on par with local machines over data center networks.
Their findings reduce average job completion time by up to 95% when the system load is high, while treating every job fairly.
The teams designed systems for faster and more efficient distributed and large-scale computing.
The team will develop a secure, data-intensive network solution to effectively transport extremely high volumes of research data on and off campus.
Akshitha Sriraman works to enable hyperscale computing on high-demand web services.
Edge Fabric offers providers real-time performance analysis and a way to incorporate this data into routing decisions.