Contemporary Theoretical Advances in High-Dimensional and Multivariate Statistics in Data Science and Machine Learning
Deadline 2026-09-30Taylor & FrancisIndexed (Active)
Journal: Research in Mathematics
Mathematics provides the rigorous language through which statistical models and methods are formulated and understood. High-Dimensional and Multivariate Statistics, has laid the foundation for many of today’s most impactful advances, including deep learning, random network theory, and high-dimensional inference. Theoretical developments in high-dimensional and multivariate statistics offer essential tools for analys…
Fixed Point Theory and Algorithms in Computational Mathematics
Deadline 2026-10-31Taylor & FrancisIndexed (Active)
Journal: Research in Mathematics
Fixed Point Theory and Contraction-based Algorithms constitute a fundamental pillar of modern applied mathematics, underpinning a wide range of theoretical and computational developments in nonlinear analysis. These methodologies play a crucial role in the study of nonlinear equations, optimization problems, variational inequalities, and dynamical systems. In recent years, the rapid growth of artificial intelligence…
Harmonic Analysis: Theory, Computation, and Applications
Deadline 2027-03-31Taylor & FrancisIndexed (Active)
Journal: Research in Mathematics
Harmonic analysis has evolved into a prominent branch of modern mathematics, offering unique and powerful frameworks for analysing functions through their spectral and structural properties. Though the field of harmonic analysis primarily emerged from Fourier analysis, it has gradually transformed into a broader discipline within the context of mathematical analysis subsuming the field of Fourier Analysis. The field…
Advances in Exponential Sums and Power Mean Estimates: Analytic Techniques and Computational Approaches
Deadline 2027-04-30Taylor & FrancisIndexed (Active)
Journal: Research in Mathematics
Exponential sums and mean value estimates stand at the core of modern analytic number theory, serving as indispensable bridges connecting pure mathematics with dynamical systems and computational modelling. Classical problems—ranging from the distribution of prime numbers to the analysis of arithmetic progressions—rely heavily on refined estimates of trigonometric sums and higher-order moments. In recent years, the…
Computational Partial Differential Equations and Scientific Machine Learning: Theory, Algorithms, and Applications
Deadline 2027-05-31Taylor & FrancisIndexed (Active)
Journal: Research in Mathematics
Partial Differential Equations (PDEs) are fundamental mathematical tools for describing and analysing complex phenomena in science, engineering, and technology. They govern a wide range of processes, including fluid flow, heat and mass transfer, wave propagation, material deformation, biological dynamics, and environmental systems. Over the past decades, significant advances in computational methods have enabled the…
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