Professor Alexander Barg (ECE/ISR) is the principal investigator for a new NSF grant, New Approaches to the Design and Analysis of Graphical Models for Linear Codes and Secret Sharing Schemes. The three-year, $350K grant will fund new approaches to the design and analysis of graphical models for linear codes and secret sharing schemes.
Error-correcting coding enables the design of reliable information transmission and storage systems. It also is universally used for sending packets of information over the internet, writing data on CDs and flash memory devices, and other similar means of modern communication.
Barg?s research will devise new ways of constructing and analyzing the error-correcting schemes that are used for transmission. Graphical models have been successfully used for analyzing algorithms that optimize functions on networks such as dynamic programming on trees. The research will analyze graphical models for error correcting codes to come up with faster ways to correct errors that arise in transmission of information. These ideas also could be used to analyze the schemes used for distributed secure information storage, also known as "secret sharing schemes.?
Technical details of the research are available at NSF?s web page for this grant.
Barg is principal investigator for new NSF information recovery award
Alexander Barg receives NSF grant to study theoretic aspects of local data recovery
Barg receives NSF grant to develop better methods of storing large amounts of data
Alum Ara Mazumdar begins position at University of Minnesota
Alexander Barg receives NSF grant for polar code research
Alexander Barg awarded NSF grant to develop genotyping theory
Arya Mazumdar wins information theory student paper prize
September 10, 2009