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Partnerships between Science and Engineering Fields and the NSF TRIPODS Institutes Crosscutting Programs NSF Wide Programs

Name Dir/Div Name Dir/Div
Nandini  Kannan MPS/DMS Tracy  Kimbrel CISE/CCF
Rahul  T. Shah CISE/CCF Christopher  W. Stark MPS/DMS
Eva  Campo MPS/DMR Darleen  L. Fisher CISE/CNS
Lin  He MPS/CHE Kenneth  Land SBE/SES
Alexis  Lewis ENG/CMMI Eduardo  A. Misawa ENG/EEC
Nigel  A. Sharp MPS/AST Eva  Zanzerkia GEO/EAR
Aidong  Zhang    


Solicitation  18-542

Important Information for Proposers

A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 19-1), is effective for proposals submitted, or due, on or after February 25, 2019. Please be advised that, depending on the specified due date, the guidelines contained in NSF 19-1 may apply to proposals submitted in response to this funding opportunity.


The National Science Foundation's (NSF’s) Directorates for Computer & Information Science & Engineering (CISE) and Mathematical & Physical Sciences (MPS) recently launched the Transdisciplinary Research in Principles of Data Science (TRIPODS) Phase I program with the goal of promoting long-term, interdisciplinary research and training activities that engage theoretical computer scientists, statisticians, and mathematicians in developing the theoretical foundations of data science.  Twelve TRIPODS Phase I Institutes were established in FY17 (see

The Partnerships between Science and Engineering Fields and the NSF TRIPODS Institutes (TRIPODS + X) solicitation seeks to expand the scope of the TRIPODS program beyond the foundations community by engaging researchers across other NSF disciplines and the TRIPODS research teams in collaborative activities.  TRIPODS + X projects will foster relationships between researchers in science & engineering domains and foundational data scientists by leveraging existing NSF investments in the TRIPODS organizations.  Working in concert with a TRIPODS organization, a TRIPODS + X project would focus on data-driven research challenges motivated by applications in one or more science and engineering domains or other activities aimed at building robust data science communities.



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

Map of Recent Awards Made Through This Program