Mathematical and Scientific Foundations of Deep Learning

Dear Colleagues,

A new NSF program solicitation (NSF 21-561) is now available:

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

Please see for details.

-- Full Proposal Deadline Date: May 12, 2021

Important Note: For timely responses to your questions, please direct your email messages to the Program Directors on the MoDL Working Group at


From the Program Solicitation:

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 and programs by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.

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.

Award Information

Anticipated Type of Award: Continuing Grant

Estimated Number of Awards:  15 to  20

Fifteen to twenty awards of various sizes with up to $1,200,000 per award total for up to three years are anticipated, subject to the availability of funds and receipt of meritorious proposals. Award size is contingent upon the scope, scale and complexity of the proposed project.

Anticipated Funding Amount: $15,000,000

Estimated program budget, number of awards and average award size/duration are subject to the availability of funds.

Eligibility Information

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.


Funding Announcement Number
NSF 21-561
Posted Date
Expiration Date
Application Due Date
Participating Organizations
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