
NSF Org: |
CCF Division of Computing and Communication Foundations |
Recipient: |
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Initial Amendment Date: | June 24, 2020 |
Latest Amendment Date: | June 24, 2020 |
Award Number: | 2006589 |
Award Instrument: | Standard Grant |
Program Manager: |
Phillip Regalia
pregalia@nsf.gov (703)292-2981 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
Start Date: | October 1, 2020 |
End Date: | September 30, 2024 (Estimated) |
Total Intended Award Amount: | $497,639.00 |
Total Awarded Amount to Date: | $497,639.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
7 LEBANON ST HANOVER NH US 03755-2170 (603)646-3007 |
Sponsor Congressional District: |
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Primary Place of Performance: |
Office of Sponsored Projects Hanover NH US 03755-1404 |
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): | Comm & Information Foundations |
Primary Program Source: |
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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
Communication is central to modern computing systems, which involve data distributed across multiple entities. Developing communication-efficient algorithmic strategies (protocols) to solve problems featuring distributed data is therefore an important practical goal. Correspondingly, communication complexity---which studies the possibilities and limitations of such protocols---is important to foundational research in computer science. In fact, the influence of communication complexity runs much deeper, because the operation of an algorithm can itself be seen as a careful orchestration of information flow from input bits to output bits, which gives rise to a communication protocol between abstract entities. Key questions in communication complexity are: how much efficiency is gained by allowing the communicating parties to (a) use randomized strategies that may err with some small probability, or (b) use a complex pattern of interactive communication as opposed to a simple one? This project will address some such questions, seeking theorems that provably require fresh mathematical ideas, forcing an expansion of our mathematical arsenal. The project aims to grow the foundations of communication complexity either through novel lower bounds or by obtaining surprising new protocols that may teach us algorithmic lessons.
Viewing communication as information flow and quantifying this using the machinery of Shannon entropy and Kullback-Leibler divergence is a powerful technique for proving lower bounds on the communication required to accomplish a task. This project's central goal is to make progress on problems where this general paradigm provably cannot work. The investigator has identified some concrete communication tasks where a deterministic solution plausibly requires considerably more resources than a randomized solution, and proposes to prove such separations by developing a more delicate quantification of information flow that is attuned to deterministic communication protocols. Additionally, the project will study a class of communication problems involving parties with asymmetric knowledge (the so-called Arthur-Merlin setting, where one super-player knows all of the distributed input but is not blindly trusted by the other, regular, players), where classical information theory fails to capture the communication from the super-player.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This research project sought to quantify the communication or space resources required for solving basic algorithmic problems involving large amounts of input data spread across two or more "players" working cooperatively. Such problems arise when proving mathematical theorems about the inherent limitations of many aspects of computing. In this project, the most important aspect was to understand streaming computation, which calls for processing a continuous flow of information. Imagine trying to make sense of a fast-moving river of information where decisions need to be made quickly based on what arrives, without the luxury of pausing to sort through everything.
During this project, the PI and his collaborators established several hardness results related to these problems. Additionally, the PI and collaborators designed new and more efficient algorithms for certain data streaming challenges. A key highlight of the project was the development of a well-rounded theory for streaming algorithms that function effectively despite adversarial feedback-loop effects caused by outputs influencing future inputs processed by the same algorithm. This theory provides insights into both the possibilities and limitations in the realm of data streaming and distributed computing, based on results published throughout the project.
In addition to advancing research, this project played a crucial role in establishing a new course at Dartmouth focused on Information Theory for Computer Science. Given that Dartmouth does not have a traditional electrical engineering department, this course fills an important gap, providing students with essential knowledge and skills in this vital area of study. By equipping the next generation of computer scientists with knowledge rooted in advanced research, this initiative will help shape the future of computing and its diverse applications.
Last Modified: 12/06/2024
Modified by: Amit Chakrabarti
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