{"id":306,"date":"2011-09-20T09:31:42","date_gmt":"2011-09-20T13:31:42","guid":{"rendered":"http:\/\/blogs.lib.purdue.edu\/grants\/?p=306"},"modified":"2011-09-20T09:31:42","modified_gmt":"2011-09-20T13:31:42","slug":"nsf-computational-and-data-enabled-science-and-engineering-in-mathematical-and-statistical-sciences","status":"publish","type":"post","link":"https:\/\/blogs.lib.purdue.edu\/grants\/2011\/09\/20\/nsf-computational-and-data-enabled-science-and-engineering-in-mathematical-and-statistical-sciences\/","title":{"rendered":"NSF: Computational and Data-Enabled Science and Engineering in Mathematical and Statistical Sciences"},"content":{"rendered":"<p>CFP: http:\/\/www.nsf.gov\/funding\/pgm_summ.jsp?pims_id=504687<\/p>\n<p>Date: January 23, 2012<\/p>\n<p>Growing out of scientific computation and the explosion  in production of digital and observational data, Computational and  Data-Enabled Science and Engineering (CDS&amp;E, <a href=\"http:\/\/www.nsf.gov\/mps\/cds-e\/\">http:\/\/www.nsf.gov\/mps\/cds-e\/<\/a>)  is clearly emerging as a distinct intellectual and technological  discipline lying at the interface of mathematics, statistics,  computational science, core sciences and engineering disciplines.  CDS&amp;E, broadly interpreted, now affects virtually every area of  science and technology, revolutionizing the way science and engineering  are done.<\/p>\n<p>The Division of Mathematical Sciences and the Office of  Cyberinfrastructure of the National Science Foundation recognize the  importance of research in CDS&amp;E and envision that the mathematical  and statistical research communities will play a leading role in the  future development of this emerging science. In partnership with the  Office of Cyberinfrastructure, the CDS&amp;E program in DMS supports  fundamental research at the core of this emerging discipline. \u00a0It  supports broadly innovative, ambitious and transformative research that  will lead to significant advancement in CDS&amp;E.\u00a0The emphasis will be  on mathematical, statistical, computational, and algorithmic  developments, as well as their applications in advancing modern  cyberinfrastructure and scientific discovery.\u00a0 \u00a0Multidisciplinary  collaboration and the training of the next generation data and  computational scientists firmly grounded and trained in mathematics and  statistics will be strongly encouraged. The research topics supported by  CDS&amp;E -MSS will be rooted in mathematics and statistics and will  address computational and big data challenges and promote directly  discoveries and innovations at the frontiers of science and engineering.  The overall impact in the mathematical and statistical sciences of the  proposed work will be a review criterion.<\/p>\n<p>Examples in which mathematical and statistical research enables advances in CDS&amp;E include, but are not limited to:<\/p>\n<p>\u2022\u00a0  Sophisticated computational\/statistical modeling for simulation,  prediction, and assessment in\u00a0large scale and data intensive scientific  problems that incorporate high performance and\/or distributed computing  that includes addressing challenges of scalability and heterogeneous  architectures<\/p>\n<p>\u2022\u00a0 State-of-the-art tools and theory in statistical  inference, statistical\u00a0learning and data mining for knowledge discovery  from massive, complex, and dynamic\u00a0data sets; or\u00a0novel usage of  knowledge in science to understand effective ways to exploit massive and  quickly growing data<\/p>\n<p>\u2022\u00a0 General theory and algorithms for  advancing large-scale modeling for complex problems such as those with  strong heterogeneities and anisotropies, multi physics coupling,  multiscale behavior, stochastic forcing, uncertain parameters or dynamic  data, and the subtle impact on a calculation of long-time integration<\/p>\n<p>\u2022\u00a0  Sophisticated computational methods for the elucidation of topological  theory, revealing and examining structures in algebraic and arithmetic  geometry and number theory, and design of cryptographic security and  cybersecured systems<\/p>\n<p>\u2022\u00a0 Innovative methodologies and theory for  large scale data acquisition through optimal designs, complex computer  experiments, and compressed sampling.<\/p>\n<p>\u2022\u00a0 Study of mathematical,  statistical\u00a0and stochastic properties of complex networks arising from  computational science, all other core sciences,\u00a0and engineering  disciplines that are supported by NSF<\/p>\n<p>\u2022\u00a0 Computational differential geometry for graphics and visualization, signal processing, analysis and compressed sensing.<\/p>\n<p>\u2022\u00a0  Advances in discretization methods and solvers, optimization,  validation and uncertainty quantification, and automated and  reproducible science through rigorous problem specification and code  generation<\/p>\n","protected":false},"excerpt":{"rendered":"<p>CFP: http:\/\/www.nsf.gov\/funding\/pgm_summ.jsp?pims_id=504687 Date: January 23, 2012 Growing out of scientific computation and the explosion in production of digital and observational data, Computational and Data-Enabled Science and Engineering (CDS&amp;E, http:\/\/www.nsf.gov\/mps\/cds-e\/) is clearly emerging as a distinct intellectual and technological discipline lying at the interface of mathematics, statistics, computational science, core sciences and engineering disciplines. CDS&amp;E, broadly [&hellip;]<\/p>\n","protected":false},"author":15,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[29],"tags":[147,195,3878,33,3879],"class_list":["post-306","post","type-post","status-publish","format-standard","hentry","category-extramural-grants","tag-cyberinfrastructure","tag-data-science","tag-mathematics","tag-nsf","tag-statistics"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/pY2Uk-4W","_links":{"self":[{"href":"https:\/\/blogs.lib.purdue.edu\/grants\/wp-json\/wp\/v2\/posts\/306","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.lib.purdue.edu\/grants\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.lib.purdue.edu\/grants\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.lib.purdue.edu\/grants\/wp-json\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.lib.purdue.edu\/grants\/wp-json\/wp\/v2\/comments?post=306"}],"version-history":[{"count":2,"href":"https:\/\/blogs.lib.purdue.edu\/grants\/wp-json\/wp\/v2\/posts\/306\/revisions"}],"predecessor-version":[{"id":308,"href":"https:\/\/blogs.lib.purdue.edu\/grants\/wp-json\/wp\/v2\/posts\/306\/revisions\/308"}],"wp:attachment":[{"href":"https:\/\/blogs.lib.purdue.edu\/grants\/wp-json\/wp\/v2\/media?parent=306"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.lib.purdue.edu\/grants\/wp-json\/wp\/v2\/categories?post=306"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.lib.purdue.edu\/grants\/wp-json\/wp\/v2\/tags?post=306"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}