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Stimulating Collaborative Advances Leveraging Expertise in the Mathematical and Scientific Foundations of Deep Learning (SCALE MoDL)

Status: Archived

Archived funding opportunity

This document has been archived.

Important information for proposers

All proposals must be submitted in accordance with the requirements specified in this funding opportunity and in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. It is the responsibility of the proposer to ensure that the proposal meets these requirements. Submitting a proposal prior to a specified deadline does not negate this requirement.


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), and Social, 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's National Artificial Intelligence Research Institutes and Harnessing the Data Revolution programs 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 the Directorate 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 DirectorsPI 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.  

Program contacts

General inquiries may be addressed to modl@nsf.gov

Huixia Wang
Program Director
huiwang@nsf.gov (703) 292-2279
Aranya Chakrabortty
Program Director
achakrab@nsf.gov (703) 292-8113 ENG/ECCS
Wei Ding
Program Director
weiding@nsf.gov (703) 292-8017
Funda Ergun
Program Director
fergun@nsf.gov (703) 292-8910
Eun Heui Kim
Program Director
eukim@nsf.gov (703) 292-2091
Tracy Kimbrel
Program Director
tkimbrel@nsf.gov (703) 292-7924 CISE/CCF
Phillip A. Regalia
Program Director
pregalia@nsf.gov (703) 292-2981 CISE/CCF
Christopher W. Stark
Program Director
cstark@nsf.gov (703) 292-4869 MPS/DMS
Zhengdao Wang
Program Director
zwang@nsf.gov (703) 292-7823
Kenneth C. Whang
Program Director
kwhang@nsf.gov (703) 292-5149 CISE/IIS
Joseph M. Whitmeyer
Program Director
jwhitmey@nsf.gov (703) 292-7808 SBE/SES

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