Research in the Mathematical Sciences is an international, peer-reviewed hybrid journal covering the full scope of Theoretical Mathematics, Applied Mathematics, and Theoretical Computer Science. The Mission of the Journal will be to publish high-quality original articles that make a significant contribution to the research areas of both theoretical and applied mathematics and theoretical computer science.

This journal is an efficient enterprise where the editors play a central role in soliciting the best research papers, and where editorial decisions are reached in a timely fashion. Research in the Mathematical Sciences does not have a length restriction and encourages the submission of longer articles in which more complex and detailed analysis and proofing of theorems is required. It also publishes shorter research communications (Letters) covering nascent research in some of the hottest areas of mathematical research. This journal will publish the highest quality papers in all of the traditional areas of applied and theoretical areas of mathematics and computer science, and it will actively seek to publish seminal papers in the most emerging and interdisciplinary areas in all of the mathematical sciences. Research in the Mathematical Sciences wishes to lead the way by promoting the highest quality research of this type.

Journal information

  • Alejandro Adem,
  • Thomas Y. Hou,
  • Lisa Jeffrey,
  • Ken Ono,
  • Fadil Santosa,
  • Yuri Tschinkel
Publishing model
Hybrid (Transformative Journal). Learn about publishing Open Access with us

Journal metrics

0.831 (2019)
Impact factor
1.311 (2019)
Five year impact factor
55 days
Submission to first decision
179 days
Submission to acceptance
31,836 (2020)

Latest articles

Journal updates

  • Transmission Eigenvalues and Related Spectral Problems in Scattering Theory (Submission Deadline: April 30, 2021)

    This special issue will feature recent developments in the theory and applications of transmission eigenvalues and related spectral problems in direct and inverse scattering theory. The transmission eigenvalue problem is at the heart of inverse scattering theory for inhomogeneous media. It has a deceptively simple formulation but presents a perplexing mathematical structure; in particular it is a non-self-adjoint eigenvalue problem. This subject is rich, active and in the past decade has taken a multitude of directions, including developments in the spectral theory for various operators related to scattering, as well as many applications in inverse scattering problems and imaging. We solicit high quality original research papers targeting results on the theory, computations and applications of these topics.

    Guest Editor: Fioralba Cakoni, Rutgers University and Houssem Haddar, CMAP Ecole Polytechnique

    Submission Deadline: April 30, 2021

    Download full details here: Transmission eigenvalues and Related Spectral Problems in Scattering Theory (PDF, 19.17 kB)

  • Mathematical Theory of Machine Learning and Applications (Submission Deadline: 31st August, 2021)

    In the past decade, deep learning as a branch of machine learning has influenced scientific computing in a fundamental way. This computational breakthrough presents tremendous opportunities and needs for new perspectives on computational mathematics and related emerging fields, such as approximation theory, operator estimation, numerical PDEs, inverse problems, data-driven modeling of dynamical systems, unsupervised and semi-supervised learnings. This special issue will feature high-quality original research, including (but not limited to) the theoretical and computational developments in these topics.
    Guest Editors: John Harlim, Thomas Hou, Jinchao Xu
    Submission Deadline: August 31, 2021
    Download full details here: Mathematical Theory of Machine Learning and Applications

  • PDE Methods for Machine Learning (Submission Deadline: 31st August 2021)

    This special issue will feature recent developments in the application of partial differential equations (PDE) to problems in machine learning. We solicit high quality original research papers targeting the analysis and applications of PDEs to problems in machine learning and data science.

    Guest Editors: Jeff Calder (University of Minnesota), Xiuyuan Cheng (Duke University), Adam Oberman (McGill University), Lars Ruthotto (Rutgers University)

    Submission Deadline: 31st August 2021

    Download Full Details Here: 

    PDE Methods for Machine Learning

  • Developments in Commutative Algebra: In honor of Jürgen Herzog on the occasion of his 80th Birthday

    Jürgen Herzog is one of the most accomplished researchers in the modern developments of commutative algebra. He has produced more than 230 original research papers and is cited more than 6650 times by approximately 2150 authors. In honor of his great achievements, we look forward to publishing a special issue commemorating his 80th birthday and honoring his influence on the field of commutative algebra and mathematics in general.

    Guest Editor: Takayuki Hibi 
    Submission Deadline: 31st December 2021
    Download Full Details Here: Developments in Commutative Algebra

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