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Alexander Barg receives NSF grant to study theoretic aspects of local data recovery

Professor Alexander Barg (ECE/ISR) is the recipient of a three-year, $250K National Science Foundation Computing and Communication Foundations grant for "Coding and Information: Theoretic Aspects of Local Data Recovery."

Barg will study fundamental problems in data coding that can improve the efficiency of distributed storage systems by increasing data reliability and availability while reducing storage overhead compared to existing industry standards. This research will benefit storage applications ranging from financial, scientific monitoring, and signal processing to social networks and sharing platforms.

Data coding with locality is a rapidly developing area of coding theory initially motivated by applications in distributed storage. It has links to many areas of network science (e.g., index coding and network coding) as well as to computer science. Barg's project advances the theory and practice of data coding with local recovery by investigating broad implications of the locality constraint in coding problems. These include studying new error-correcting code families and their decoding, fundamental limitations on the code parameters and capacity bounds under the requirements of local data recovery. The newly designed coding schemes developed in this project will be validated through implementation and evaluation in simulated computer environment, aiming at enhanced performance of data coding in current industry solutions.

Related Articles:
Barg receives NSF grant to develop better methods of storing large amounts of data
Arya Mazumdar wins information theory student paper prize
Ephremides leads new NSF Age of Information project
Barg is principal investigator for new NSF information recovery award
Alumnus Raef Bassily joins Ohio State as tenure-track faculty
Ulukus is PI for new NSF information-theoretic physical layer security grant
Alumnus Ravi Tandon receives NSF CAREER Award
Narayan, Zhou, Schlotfeldt, Strahan win ISR outstanding awards
Grad student Min Ye is finalist in Bell Labs Shannon competition
New NSF-funded project targets secure and private function computation

July 7, 2016


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