Foundations of Data Science invites submissions focusing on advances in mathematical, statistical, and computational methods for data science. Results should significantly advance current understanding of data science, by algorithm development, analysis, and/or computational implementation which demonstrates behavior and applicability of the algorithm. Fields covered by the journal include, but are not limited to Bayesian Statistics, High Performance Computing, Inverse Problems, Data Assimilation, Machine Learning, Optimization, Topological Data Analysis, Spatial Statistics, Nonparametric Statistics, Uncertainty Quantification, and Data Centric Engineering. Expository and review articles are welcome. Papers which focus on applications in science and engineering are also encouraged, however the method(s) used should be applicable outside of one specific application domain.
- AIMS is a member of COPE. All AIMS journals adhere to the publication ethics and malpractice policies outlined by COPE.
- Publishes 4 issues a year in March, June, September and December.
- Publishes online only.
- Archived in Portico and CLOCKSS.
- FoDS is a publication of the American Institute of Mathematical Sciences. All rights reserved.
The Guest Editors are: Gunnar Carlsson, Kathryn Hess, Facundo Memoli, Raul Rabadan, and Primosz Skraba
A special issue on Data Assimilation will be featured in Foundations of Data Science. Its aim will be to collect a set of high-level papers that expose the state-of-the-art ideas and techniques for assimilating data into models in the many areas of science where it has become an essential tool. Submission is by invitation. If you have some interest in submitting to the issue, please contact Chris Jones (email@example.com)
Marc Bocquet, Jana de Wiljes, John Harlim, Chris Jones, Matthias Morzfeld, Elaine Spiller and Xin T. Tong
The special issue on SMC has been postponed and will open for submission in January, 2021.
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