Division of Mathematical Sciences
|Gabor Szekelyemail@example.com||(703) 292-8869||E 9457|
|Nandini Kannanfirstname.lastname@example.org||(703) 292-8104||E 9455|
|Robert B. Lundemail@example.com||(703) 292-2279||E 9412|
|David S. Stofferfirstname.lastname@example.org||(703) 292-7428||E 9436|
|Yong Zengemail@example.com||(703) 292-2301||E 8414|
Apply to PD 18-1269 as follows:
For full proposals submitted via FastLane: standard NSF Proposal & Award Policies & Procedures Guide proposal preparation guidelines apply.
For full proposals submitted via Grants.gov: the NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov Guidelines applies. (Note: The NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: http://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide)
Important Information for Proposers
ATTENTION: Proposers using the Collaborators and Other Affiliations template for more than 10 senior project personnel will encounter proposal print preview issues. Please see the Collaborators and Other Affiliations Information website for updated guidance.
A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 18-1), is effective for proposals submitted, or due, on or after January 29, 2018. Please be advised that, depending on the specified due date, the guidelines contained in NSF 18-1 may apply to proposals submitted in response to this funding opportunity.
Full Proposal Window
December 1, 2018 - December 17, 2018
December 1 - December 15, Annually Thereafter
Proposals submitted outside this window will be returned without review. Conference and workshop proposals should be submitted eight months before the requested starting date.
The Statistics Program supports research in statistical theory and methods, including research in statistical methods for applications to any domain of science and engineering. The theory forms the base for statistical science. The methods are used for stochastic modeling, and the collection, analysis and interpretation of data. The methods characterize uncertainty in the data and facilitate advancement in science and engineering. The Program encourages proposals ranging from single-investigator projects to interdisciplinary team projects.