| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | March 1, 2006 |
| Latest Amendment Date: | August 24, 2009 |
| Award Number: | 0545862 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
John Cozzens
CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | March 15, 2006 |
| End Date: | February 29, 2012 (Estimated) |
| Total Intended Award Amount: | $399,999.00 |
| Total Awarded Amount to Date: | $399,999.00 |
| Funds Obligated to Date: |
FY 2008 = $163,539.00 FY 2009 = $91,062.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
1608 4TH ST STE 201 BERKELEY CA US 94710-1749 (510)643-3891 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1608 4TH ST STE 201 BERKELEY CA US 94710-1749 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Special Projects - CCF, SIGNAL PROCESSING SYS PROGRAM |
| Primary Program Source: |
01000809DB NSF RESEARCH & RELATED ACTIVIT 01000910DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Many real-world scientific and engineering systems consist of a large number of interacting subsystems. Examples include wireless sensor networks, in which low-power devices are used to monitor and detect events over an extended spatial region; and data compression in network settings where data is stored in a distributed manner (e.g., large databases distributed over multiple computer servers). Graphical models provide a powerful set of tools for modeling, analyzing, and designing systems of this nature. These models derive their power by combining a probabilistic model (i.e., one in which there is uncertainty or stochasticity in the system operation) with graphs that capture the dependencies among the systems. This research involves developing new algorithms for applying these graphical models to large-scale systems like sensor networks and data compression.
Leveraging the full power of graphical models requires efficient methods for solving a core set of challenges. In this research, the investigator characterizes the limitations of existing algorithms, and moreover develops alternative and ultimately more powerful message-passing algorithms for solving these core computational problems. The following four projects address related aspects of this high-level goal: (a) analysis of provably effective algorithms based on linear programming; (b) novel message-passing algorithms for performing near-optimal lossy data compression; (c) fundamental research on issues of stability and robustness in message-passing; and (d) new methods for automated learning of models from data. These research thrusts are closely coupled with educational initiatives, including recruitment of undergraduates into research; broad dissemination of publicly-available survey papers, tutorial slides and software for graphical models; and the fostering of interaction between Engineering and Statistics via the Designated Emphasis in Communication, Computation and Statistics at UC Berkeley.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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