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Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF ARIZONA
Doing Business As Name:University of Arizona
PD/PI:
  • Gregory C Ditzler
  • (520) 626-6000
  • ditzler@email.arizona.edu
Award Date:12/12/2019
Estimated Total Award Amount: $ 500,000
Funds Obligated to Date: $ 399,061
  • FY 2020=$399,061
Start Date:02/01/2020
End Date:01/31/2025
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CAREER: Learning in Adversarial and Nonstationary Environments
Federal Award ID Number:1943552
DUNS ID:806345617
Parent DUNS ID:072459266
Program:EPCN-Energy-Power-Ctrl-Netwrks
Program Officer:
  • Anthony Kuh
  • (703) 292-2210
  • akuh@nsf.gov

Awardee Location

Street:888 N Euclid Ave
City:Tucson
State:AZ
ZIP:85719-4824
County:Tucson
Country:US
Awardee Cong. District:03

Primary Place of Performance

Organization Name:University of Arizona
Street:888 N Euclid Ave
City:Tucson
State:AZ
ZIP:85719-4824
County:Tucson
Country:US
Cong. District:03

Abstract at Time of Award

The majority of machine learning algorithms rely on the assumption that data are sampled from a fixed probability distribution. This assumption is often violated in practice, which results in classification and regression strategies that are far from optimal or even reliable. Recent work has shown that an adversary can significantly compromise the outcome of preprocessing techniques and classification. Unfortunately, a unified framework for learning in the presence of an adversary from streaming data has not been addressed despite the growing number of applications that need such techniques. This CAREER will study to understand when and why feature selection fails with an adversary. Not only will this research focus on understanding why feature selection fails, but also the transferability of black and white box attacks on feature selection. This project also proposes to develop novel methods to attack information-theoretic algorithms and approaches for resilient information-theoretic feature selection. This CAREER also addresses the problem of learning in a nonstationary environment with the presence of an adversary. A comprehensive set of synthetic and real-world benchmarks will be performed for each of the tasks. The research focuses on this unmet need and tackles a variety of adversarial learning problems drawn from different subfields of machine learning: specifically, algorithms for feature selection and learning in nonstationary environments. A successful implementation of the proposed research plan will have broader impacts on machine learning and application-driven domains. The education plan includes mentoring and training the future workforce for data scientists, who are currently in high demand, by introducing machine learning through multiple levels of education in a collaborative learning environment at the university. The CAREER project also includes integrated then integration research, revise research and learning with a community-based integration of research in education to draw more students at all levels for STEM and machine learning. This CAREER will engage K-12 students in Tucson to promote STEM education and also machine learning through hands-on teaching techniques. There will also be public talks to the data science community based on the CAREER research outcomes and the most recent trends in machine learning. 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.

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