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Atlanta, GA | Posted: April 7, 2016
Summary Sentence:
Polo Chau nets $1.2M NSF grant to help bring scientific literature review into the age of Big Data
Full Summary:
Assistant Professor Polo Chau nets $1.2M NSF grant to help bring scientific literature review into the age of Big Data.
Polo ChauResearchers at Georgia Tech’s School of Computational Science & Engineering and Rutgers University have been awarded $1.2 million from the National Science Foundation to create prototype systems that will allow users to make sense of vast amounts of data through intuitive new ways to view and interact with it.
Assistant Professor Polo Chau at Georgia Tech and James Abello of Rutgers University’s Center for Discrete Mathematics and Theoretical Computer Science were jointly awarded the grant to help realize some of “Big Data’s” analytic potential.
As data sets increase in size and complexity, interacting with them quickly escalates into an arduous undertaking. For example, every day scientists publish their findings in journals in their different disciplines. Wading through all recent publications in an area like heart disease research is daunting enough, but in an interdisciplinary world, how could that medical researcher also learn of relevant breakthroughs in physics, evolutionary biology, or computational science?
Similarly, analysts must spot abnormal activities in computer networks composed of tons of data of different types, and patients need to understand their puzzling symptoms.
"From a non-specialist user's point of view," Chau said, "the concern is certainly not about storage, or computing power, or large-scale data processing. It is about how to make sense of a large amount of different kinds of data using ‘natural’ ways to interact and explore their representations."
With this award, Abello and Chau will take on the challenge of computer-human interactive exploration of information-rich, billion-scale network data sets. These include relationships on online social networks, who is buying what on online auctions, and intelligence analysis of communication patterns and network traffic.
They plan to develop prototype systems, in which users will gradually build up an understanding of billion-scale network datasets. This research could fundamentally change how people make sense of data in many areas like scientific literature, cybersecurity, and consumer decision making.
"The findings could increase education effectiveness, the rate of scientific discovery, and allow us all to make better informed decisions without each needing several Ph.D. degrees,” Abello said. “We could stand on the shoulders of a lot more giants."
