Mathematical and Scientific Foundations of Deep Learning and Related Areas (MoDL+)
Funding Agency:
- National Science Foundation
This Dear Colleague Letter (DCL) is to encourage the submission of proposals from interdisciplinary teams comprised of computer scientists, electrical engineers, mathematicians and statisticians, and social, behavioral, and economic scientists to address the most challenging theoretical and foundational questions in machine learning.
The National Science Foundation (NSF) Directorates for Computer and Information Science and Engineering (CISE), Engineering (ENG), Mathematical and Physical Sciences (MPS), and Social, Behavioral and Economic Sciences (SBE) promote interdisciplinary research in Mathematical and Scientific Foundations of Deep Learning and related areas (MoDL+). Deep learning and other related modern machine learning technologies have met with impressive empirical success, fueling fundamental scientific discoveries, and transforming numerous application domains of artificial intelligence. The incomplete theoretical understanding of the field, however, impedes the use of machine learning techniques by a wider range of participants. Confronting this incomplete understanding of the mechanisms underlying the successes and failures of machine learning is essential to overcoming its limitations and expanding its applicability.
Various; Please Contact the Program Director
Please Contact the Program Director
CISE/CCF: Funda Ergun; Tracy Kimbrel; Phillip A. Regalia
CISE/IIS: Wei Ding; Kenneth C. Whang
ENG/ECCS: Zhengdao Wang; Donald Wunsch
MPS/DMS: Marian Gidea; Stacey Levine; Huixia Wang
SBE/SES: Joseph M. Whitmeyer