Stimulating Collaborative Advances Leveraging Expertise in the Mathematical and Scientific Foundations of Deep Learning

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Funding Opportunity ID:331520
Opportunity Number:21-561
Opportunity Title:Stimulating Collaborative Advances Leveraging Expertise in the Mathematical and Scientific Foundations of Deep Learning
Opportunity Category:Discretionary
Opportunity Category Explanation:
Funding Instrument Type:Grant
Category of Funding Activity:Science and Technology and other Research and Development
Category Explanation:
CFDA Number(s):47.041
47.049
47.070
47.075
Eligible Applicants:Others (see text field entitled “Additional Information on Eligibility” for clarification)
Additional Information on Eligibility:*Who May Serve as PI: PI teams must collectively possess appropriate expertise in three disciplines – computer science, electrical engineering, and mathematics/statistics. Each project must clearly demonstrate substantial collaborative contributions from members of their respective communities; projects that increase diversity and broaden participation are encouraged. Teams may be composed of members at multiple institutions or a single institution. There are no other restrictions or limits for the allowable organizations listed above.
Agency Code:NSF
Agency Name:National Science Foundation
Posted Date:Feb 13, 2021
Close Date:May 12, 2021
Last Updated Date:Feb 13, 2021
Award Ceiling:$1,200,000
Award Floor:$0
Estimated Total Program Funding:$15,000,000
Expected Number of Awards:20
Description:Deep learning has met with impressive empirical success that has fueled fundamental scientific discoveries and transformed numerous application domains of artificial intelligence. Our incomplete theoretical understanding of the field, however, impedes accessibility to deep learning technology by a wider range of participants. Confronting our incomplete understanding of the mechanisms underlying the success of deep learning should serve to overcome its limitations and expand its applicability. The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), andSocial, Behavioral and Economic Sciences (SBE)will jointly sponsor new research collaborations consisting of mathematicians, statisticians, electrical engineers, and computer scientists. Research activities should be focused on explicit topics involving some of the most challenging theoretical questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration should conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum.This program complements NSF'sNational Artificial Intelligence Research InstitutesandHarnessing the Data Revolutionprograms by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale. When responding to this solicitation, even though proposals must be submitted through theDirectorate for Mathematical and Physical Sciences, Division of Mathematical Sciences (MPS/DMS), once received, the proposals will be managed by a cross-disciplinary team of NSF Program Directors.PI teams must collectively possess appropriate expertise in three disciplines – computer science, electrical engineering, and mathematics/statistics. Each project must clearly demonstrate substantial collaborative contributions from members of their respective communities; projects that increase diversity and broaden participation are encouraged. A wide range of scientific themes on theoretical foundations of deep learning may be addressed in these proposals. Likely topics include but are not limited to geometric, topological, Bayesian, or game-theoretic formulations, to analysis approaches exploiting optimal transport theory, optimization theory, approximation theory, information theory, dynamical systems, partial differential equations, or mean field theory, to application-inspired viewpoints exploring efficient training with small data sets, adversarial learning, and closing the decision-action loop,not to mention foundational work on understanding success metrics, privacy safeguards, causal inference, and algorithmic fairness.
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