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Division of Mathematical Sciences

NSF-Simons Research Collaborations on the Mathematical and Scientific Foundations of Deep Learning  (MoDL)

Name Email Phone Room
Huixia  Wang (703) 292-2279  MPS/DMS  
Radhakisan  S. Baheti (703) 292-8339  ENG/ECCS  
Funda  Ergun (703) 292-2216  CISE/CCF  
Tracy  Kimbrel (703) 292-7924  CISE/CCF  
Anthony  Kuh (703) 292-2210  ENG/ECCS  
Elizabeth  Roy (212) 524-6966  Simons Foundation  
Christopher  W. Stark (703) 292-4869  MPS/DMS  


Solicitation  20-540

Important Information for Proposers

A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 22-1), is effective for proposals submitted, or due, on or after October 4, 2021. Please be advised that, depending on the specified due date, the guidelines contained in NSF 22-1 may apply to proposals submitted in response to this funding opportunity.




The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators (“PIs”) and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.


What Has Been Funded (Recent Awards Made Through This Program, with Abstracts)

Map of Recent Awards Made Through This Program