Computational and Data-Enabled Science and Engineering in Mathematical and Statistical Sciences
This column highlights research activities that may be of interest to ASA members. This article includes information about new research solicitations and the federal budget for statistics. Comments or suggestions for future articles may be sent to the Amstat News managing editor at email@example.com.
Xiaoming Huo is a program officer in the division of mathematical sciences at the National Science Foundation. He is on leave from the Georgia Institute of Technology, where he is a professor in the School of Industrial and Systems Engineering. His research interests are in statistics.
Thomas F. Russell, program director for the Division of Advanced Cyberinfrastructure, has worked at the National Science Foundation since 2003, mainly on interdisciplinary initiatives and computational science. His research interests include numerical solution of PDEs, particularly with applications to subsurface flows in porous media.
Computational and Data-Enabled Science and Engineering in Mathematical and Statistical Sciences (CDS&E-MSS) is a relatively new funding opportunity managed by the Division of Mathematical Sciences (DMS) of the National Science Foundation (NSF). The CDS&E-MSS program supports research that confronts the mathematical and statistical challenges presented to the scientific and engineering communities by the ever-expanding role of computational modeling and simulation on the one hand and the explosion in production of digital and observational data on the other. The goal of the program is to promote the creation and development of the next generation of mathematical and statistical theories and methodologies that will be essential for addressing such issues. To this end, the program supports fundamental research in mathematics and statistics whose primary emphasis is on meeting these challenges.
The next submission window for CDS&E-MSS is November 25 to December 9. CDS&E-MSS was launched in 2011, and the first two rounds of awards were made in 2012 and 2013, respectively. To learn about existing CDS&E-MSS–supported projects, visit the website and locate the link to CDS&E-MSS. After arriving at the CDS&E-MSS website, click “What Has Been Funded…” at the bottom of the page to reach the NSF Award Search website, where all active projects funded by the CDS&E-MSS program are listed. Alternatively, browse the NSF Award Search website directly and then use the CDS&E program element code 8069 in a search.
Awards from the first two CDS&E-MSS competitions cover a wide range of topics (e.g., stochastic partial differential equations, Lie groups and representation theory, manifold learning, sparse optimization, data assimilation, partially observed Markov processes, and high-dimensional learning). Many emerging methodologies have been proposed for development (e.g., efficient parallel iterative Monte Carlo methods, accelerated Monte Carlo schemes, solving large-scale Eigen-related problems, and measurement model specification search). Some projects are dealing with newly emerged data sets (e.g., algebraic, geometric, and computational tools for data cloud and data array; LiDAR point cloud data; and data with network structure). A wide range of applications can be found in the current awards, including tumor microenvironment, genetic association, brain connectivity, coastal ocean modeling, and subsurface imaging.
A successful statistics proposal will address the data-enabling component of the program description and argue convincingly for its application(s) in science and engineering. A project that appears to fit into other, traditional programs will have low funding priority in the CDS&E-MSS program. Many CDS&E-MSS awards support interdisciplinary research, and existing awards are jointly supported by several divisions within NSF, with significant participation by the Division of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering.
More information about this new program can be found in an October 2012 Amstat News article by Jia Li.