New Faculty: Bin Li
URI Assistant Professor Bin Li feels right at home at URI, despite growing up and completing his Bachelor’s and Master’s Degrees more than 7,800 miles away in Xiamen, China.
“Xiamen University, where I studied, is very close to a beautiful beach and like many of the URI building that surround the Quad, their oldest buildings were also made of stone,” said Li.
Of course, architecture and proximity to the coast weren’t the only reasons why Li decided to join the URI faculty in August 2016.
“URI has quite a comprehensive set of research areas, which will provide lots of potential opportunities for me to conduct inter-disciplinary research,” stated Li. “I was impressed by several outstanding professors in our department who have significantly advanced their respective research areas.”
Currently, Li is pursuing funding from the National Science Foundation (NSF) for two research topics: big data processing networks and wireless networks.
For big data processing networks, Li is working on efficient and low-complexity algorithm design for processing big data applications, ranging from weather forecasting to health care.
“With the fast growth of big data applications, more and more data-intensive applications with high-performance computational needs are generated in the scale of terabytes and petabytes,” explained Li. “Therefore, it is increasingly important to develop efficient methods to process such huge amounts of data.”
With regard to wireless networks, Li is developing wireless algorithms with traffic-insensitive performance, meaning the network performance would not depend on any traffic characteristics except the mean traffic load.
“This is especially important in wireless networks such as cellular networks, Internet of things and cyber-physical systems, that support a significant amount of diversified traffic, ranging from sending warning messages to watching online videos,” said Li.
In the future, Li wished to continue to work on the cutting-edge problems that are at the intersection of big data, networking and learning.