American Institute of Mathematical Sciences

FoDS Flyer: showing all essential information of the journal.
Foundations of Data Science (FoDS) 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.

Call for Papers: Special Issue on "Data Science Education Research" of Foundations of Data Science (click to view details)

Call for Papers: Special Issue on "Topological methods in data analysis, machine learning and artificial intelligence" of Foundations of Data Science (click to view details)

Call for Papers: Special Issue on "Sequential Monte Carlo Methods" of Foundations of Data Science (click to view details)

Call for Papers: Special Issue on "Scientific Machine Learning" of Foundations of Data Science (click to view details)