The U.S. – Israel Binational Science Foundation (BSF) is accepting applications in joint funding programs in Foundational Research in Stimulating Collaborative Advances Leveraging Expertise in the Mathematical and Scientific Foundations of Deep Learning, with the Mathematical and Physical Sciences; the Computer and Information Science and Engineering (CISE); Engineering (ENG) and Social, Behavioral and Economic Sciences Directorates of the U.S. National Science Foundation (NSF).
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.
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 applicationinspired 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.
3. These programs have no deadlines and submission is welcome throughout the year. Please note that the Israeli PIs have to submit their proposals to the BSF within 7 days after the U.S. PIs submit to the NSF.
Applications must be written jointly by an Israeli and a U.S. scientist from a U.S. research institution.
The NSF accepts applications only from U.S. scientists. Submission to the NSF should be made by the U.S. PI alone, but he/she has to note in the cover page that it is an “NSF-BSF Application” (the Israeli does not appear as a formal co-PI on the application). In the U.S.-Israeli applications, the role of the Israeli partner(s) must be described. Furthermore, it should be clearly explained why the contribution of the Israeli PI to the research project is important/essential.
The size of the BSF grant to the Israeli is expected to be up to $80,000/year for experimental programs and up to $55,000/year for theoretical or computer based research, subject to the availability of funds. If more than a single Israeli group is involved in the research, the budget may be increased by up to 50%.