| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 269 | 34 | 40 | 10 |
| Computer Science | 459 | 43 | 46 | 13 |
The journal focuses largely on the fields of Algorithm, Applied mathematics, Matrix (mathematics), Combinatorics and Compressed sensing. Information and Inference: A Journal of the IMA facilitates discussions on Algorithm that incorporate concepts from other fields like Range (mathematics), Subspace topology and Lasso (statistics). It connects research in Subspace topology with the related topic of Linear subspace.
Applied mathematics research presented in it encompasses a variety of subjects, including Inverse problem, Linear regression, Minimax, Estimator and Phase retrieval. Issues in Matrix (mathematics) were discussed, taking into consideration concepts from other disciplines like Random matrix and Rank (linear algebra). In Information and Inference: A Journal of the IMA, Function (mathematics) and Upper and lower bounds are investigated in conjunction with one another to address concerns in Combinatorics research.
The most cited publications investigate studies in Algorithm, Applied mathematics, Matrix (mathematics), Compressed sensing and Convex optimization. The journal papers focus on Applied mathematics but the discussions also offer insight into other areas such as Dimension (vector space), Estimator, Rounding and Least squares. While Matrix (mathematics) is the focus of the journal articles, it also provides insights into the studies of Random matrix and Combinatorics.
The journal focuses on Applied mathematics, Algorithm, Combinatorics, Estimator and Matrix (mathematics). The research on Applied mathematics tackled can also make contributions to studies in the areas of Inverse problem, Covariance, Estimation of covariance matrices, Rank (linear algebra) and Gradient descent. The Algorithm works featured in it incorporate elements from Simple (abstract algebra) and Noise (signal processing).
The work on Combinatorics tackled in it brings together disciplines like Function (mathematics) and Laplace operator. Information and Inference: A Journal of the IMA focuses on Estimator but the discussions also offer insight into other areas such as Entropy (information theory), Focus (optics) and Sample size determination. The research on Matrix (mathematics) featured in Information and Inference: A Journal of the IMA combines topics in other fields like Random matrix, Linear regression, Phase retrieval, Projector and Key (cryptography).
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Information and Inference: A Journal of the IMA (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Information and Inference: A Journal of the IMA (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 1.89% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 36.54% were posted by at least one author from the top 10 institutions publishing in the journal. Another 19.23% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 19.23% of all publications and 25.00% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Noah Golowich;Alexander Rakhlin;Ohad Shamir
(2020)Raphaël Berthier;Andrea Montanari;Phan-Minh Nguyen
(2020)Rina Foygel Barber;Emmanuel J Candès;Aaditya Ramdas;Ryan J Tibshirani
(2021)Zeyu Deng;Abla Kammoun;Christos Thrampoulidis
(2021)Yue M Lu;Gen Li
(2020)Mher Safaryan;Egor Shulgin;Peter Richtárik
(2021)Alessandro Achille;Giovanni Paolini;Glen Mbeng;Stefano Soatto
(2021)Sjoerd Dirksen;Hans Christian Jung;Holger Rauhut
(2020)Exploring related fields can enhance your career prospects when studying Computer Science. For those interested in the practical application of technology, an mechanical engineering cost of education is a critical consideration, as it often involves hands-on work combined with software skills. Online degrees in related disciplines can offer affordable options without compromising quality.
Students fascinated by the fundamentals of technology and matter may consider online physics degrees, which provide a strong analytical foundation that complements computational skills. Physics often overlaps with computer science in fields such as simulations and data modeling.
For those with a passion for data and analytics, pursuing a data scientist degree can open doors to lucrative and versatile career paths. This field leverages programming, statistics, and machine learning—core components of computer science education.
Additionally, advanced studies through an online master’s in electrical engineering degree can complement computer science skills, especially for careers in hardware development, embedded systems, and robotics.
