SIAM Journal on Mathematics of Data Science (SIMODS)

SIAM Journal on Mathematics of Data Science (SIMODS) is a peer reviewed scholarly journal established in 2019 by the Society for Industrial and Applied Mathematics. The journal focuses on the mathematical, computational, and statistical foundations of modern data science. It provides an interdisciplinary platform for researchers working at the intersection of applied mathematics, machine learning, optimization, statistics, and computational science. The journal publishes original research articles addressing theoretical developments, algorithmic methods, and practical applications related to data driven scientific discovery. Topics include machine learning theory, optimization methods, inverse problems, uncertainty quantification, high dimensional statistics, network analysis, signal processing, and artificial intelligence. SIMODS emphasizes mathematically rigorous contributions that advance understanding of data science methodologies and computational frameworks. The journal is intended for mathematicians, statisticians, computer scientists, engineers, and researchers involved in modern analytical and computational approaches to large scale data problems. Published by SIAM, the journal maintains high academic standards and supports open scientific communication within the rapidly growing field of data science. Since its launch, SIMODS has become an important venue for mathematically oriented data science research and interdisciplinary collaboration.

SIAM Journal on Mathematics of Data Science (SIMODS) is :-

• International, Peer-Reviewed, Open Access, Refereed, Mathematical foundations of data science, Machine learning theory, Statistical learning, Optimization and algorithms, High dimensional data analysis, Computational mathematics, Signal processing, Scientific computing and AI methods, Network science and data analytics, machine learning theory, optimization methods, inverse problems, uncertainty quantification, high dimensional statistics, network analysis, signal processing, artificial intelligence , Online , Quarterly Journal

• UGC Approved, ISSN Approved: P-ISSN E-ISSN: 2577-0187, Established: 2019,
• Does Not Provide Crossref DOI

• Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE