| NSF Org: |
OAC Office of Advanced Cyberinfrastructure (OAC) |
| Recipient: |
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| Initial Amendment Date: | March 22, 2021 |
| Latest Amendment Date: | May 21, 2021 |
| Award Number: | 2103832 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Marlon Pierce
mpierce@nsf.gov (703)292-7743 OAC Office of Advanced Cyberinfrastructure (OAC) CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | May 1, 2021 |
| End Date: | April 30, 2026 (Estimated) |
| Total Intended Award Amount: | $259,651.00 |
| Total Awarded Amount to Date: | $259,651.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
100 INSTITUTE RD WORCESTER MA US 01609-2280 (508)831-5000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
100 Institute Road Worcester MA US 01609-2247 |
| 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): |
Data Cyberinfrastructure, Software Institutes |
| 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
Finding and understanding anomalous behavior in data is important in many applications. A large number of anomaly detection algorithms exist, and it can be difficult to determine which algorithm is best suited to a particular domain. And once an algorithm is selected, users must tune many parameters manually to get the algorithm to perform well; this requires in-depth knowledge of the machine learning process and an understanding of the trade-offs among different algorithms to select the best performing approach. To address these difficulties, this team develops a package that can test a range of unsupervised anomaly detection techniques on a dataset, explore options to identify best-fit, and classify anomalies with higher accuracy than manual tuning.
The project will automatically test a range of unsupervised anomaly techniques on a data set, extract knowledge from the combined detection results to reliably distinguish between anomalies and normal data, and use this knowledge as labels to train an anomaly classifier; the goal is to classify anomalies with an accuracy higher than what is achievable by thorough manual tuning. The approach can be applied across of a range of data types and domains. The resulting cyberinfrastructure provides tuning-free anomaly detection capabilities while making it easy to incorporate domain-specific requirements. It enables scientists and engineers having little experience with anomaly detection techniques to steer the anomaly detection process with domain expertise. Evaluation of the unsupervised anomaly detection package will use data sets and partnerships with collaborators from the Massachusetts General Hospital/Harvard Medical School, Cyber Security research, and Signify (formerly Philips Lighting) to ensure that the utility and usability of the package is verified throughout the development process.
This award by the Office of Advanced Cyberinfrastructure is jointly supported by the NSF Division of Information and Intelligent Systems within the Directorate for Computer and Information Science and Engineering.
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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