text-only page produced automatically by LIFT Text Transcoder Skip all navigation and go to page contentSkip top navigation and go to directorate navigationSkip top navigation and go to page navigation
National Science Foundation Home National Science Foundation - Education & Human Resources (EHR)
Research on Learning in Formal and Informal Settings (DRL)
design element
DRL Home
About DRL
Funding Opportunities
Awards
News
Events
Discoveries
Publications
Career Opportunities
View DRL Staff
EHR Organizations
Graduate Education (DGE)
Research on Learning in Formal and Informal Settings (DRL)
Undergraduate Education (DUE)
Human Resource Development (HRD)
Proposals and Awards
Proposal and Award Policies and Procedures Guide
  Introduction
Proposal Preparation and Submission
bullet Grant Proposal Guide
  bullet Grants.gov Application Guide
Award and Administration
bullet Award and Administration Guide
Award Conditions
Other Types of Proposals
Merit Review
NSF Outreach
Policy Office
Other Site Features
Special Reports
Research Overviews
Multimedia Gallery
Classroom Resources
NSF-Wide Investments

Email this pagePrint this page

Mathematical Sciences: Innovations at the Interface with Computer Sciences  Crosscutting Programs

This program has been archived.

CONTACTS

Name Dir/Div Name Dir/Div
Tie  . Luo MPS/DMS  Lawrence  Rosenblum  
Sankar  Basu   Grace  Yang  

This solicitation covers three categories of activities. Please see Section II of this solicitation for contact information on each of the three categories .

PROGRAM GUIDELINES

Solicitation  07-534

DUE DATES

Archived

SYNOPSIS

This solicitation describes the opportunities available for support through the Foundation’s Mathematical Sciences Priority Area in the following category:

  •  Interactions between Mathematical Sciences and Computer Sciences (MSPA-MCS)

Investments in the MSPA-MCS program aim to deepen support of collaborative research in fundamental mathematics and statistics, and computer science with a focus primarily on mathematical and statistical challenges posed by large data sets, managing and modeling uncertainty, and modeling complex nonlinear systems.

News



Email this pagePrint this page
Back to Top of page