About Statistical Learning and Data Science

Statistical Learning and Data Science

Overview and identity

Statistical Learning and Data Science, abbreviated SLADS, is an English-language, peer-reviewed, fully open-access journal based in Beijing, China. It is published through KeAi Communications, with publishing services provided by Elsevier, and its articles are hosted on ScienceDirect. The journal is owned by Science China Press, which retains copyright of the overall compiled journal and its compiled issues, while individual authors retain copyright in their own articles alongside scholarly usage rights. KeAi Communications Co., Ltd. and Science China Press are granted non-exclusive publishing and distribution rights under the journal's licensing agreement.

SLADS describes itself as a journal for the international statistics and data science community. Its stated ambition is to publish only the highest-quality, field-impacting research in statistics, machine learning and data science, and to foster discussion of future developments across all data-driven disciplines. A special-issue announcement on ScienceDirect describes SLADS as a newly launched journal sponsored by the Chinese Academy of Sciences, dedicated to high-quality research across statistics, machine learning, artificial intelligence and data science, and notes that it emphasises both rapid publication and rigorous peer review. The journal is published quarterly.

Aims and scope

The journal's aims and scope statement says that SLADS is dedicated to publishing cutting-edge research and fostering discussions on future developments across all data-driven disciplines, including but not limited to biology, economics, engineering, health sciences, humanities, physics and the social sciences. That breadth is deliberate: the journal is organised not around a single application area but around the methods by which modern science learns from data, and it expects contributions to speak to readers beyond one narrow specialty.

Rather than treating statistics, machine learning and applied data science as separate literatures, SLADS brings them together in one venue. Its scope therefore covers frontier statistical methodology and theory, new learning algorithms and their theoretical foundations, and domain applications in which data science methods are used to answer real-world questions. The journal also publishes resources — both data and tools — for data science research, recognising that reusable datasets and software are themselves scholarly contributions in this field.

The Statistics section

The first of the journal's three core sections is Statistics. This section focuses on frontier research in statistical methodology and theory with close connections to machine learning and data science, as well as innovative studies of fundamental statistical problems arising from all scientific disciplines. The journal welcomes contributions that advance theoretical understanding, develop new methodologies, or address emerging challenges in data-driven research.

In practice, this means work on topics such as high-dimensional inference, where the number of variables can exceed the number of observations, and causal inference, where the goal is to move from association to defensible claims about cause and effect. Those two themes were prominent enough to anchor one of the journal's early special issues, the Special Issue on High-Dimensional Inference and Causal Inference, with a submission deadline of 31 December 2026 and guest editorship including Runze Li of Pennsylvania State University, who also serves as one of the journal's Editors.

The Machine Learning and Artificial Intelligence section

The second core section, Machine Learning and Artificial Intelligence, is devoted to advances in the theory, methodology and practice of machine learning and AI. The journal welcomes contributions that develop novel algorithms, provide rigorous theoretical analysis, or establish new principles that deepen understanding of learning from data.

Topics of interest listed by the journal include large language models, deep learning, reinforcement learning, probabilistic generative models, optimisation, privacy, interpretability and fairness, together with the integration of statistical and computational perspectives. The journal particularly encourages papers that connect machine learning with statistical theory and methodology — a positioning that distinguishes SLADS from purely engineering-oriented AI venues and reflects the statistical training of much of its editorial leadership.

The Data Science section

The third core section, Data Science, focuses on the application of data science methods across a wide range of domains, including the natural and social sciences, health, medicine, engineering, economics, business and the humanities. Contributions to this section should be motivated by real-world problems and should leverage state-of-the-art approaches in data science, machine learning and artificial intelligence.

The journal sets clear expectations for applied work: authors are expected to give clear discussions of the problem, the datasets, the methodological choices and the insights gained. That requirement is intended to keep applied papers reproducible and useful to practitioners, rather than presenting a method in the abstract without the context needed to judge whether it worked and why.

Editorial leadership and board

SLADS is led by two Editors-in-Chief: Song Xi Chen of Tsinghua University in China and Jiashun Jin of Carnegie Mellon University in the United States. Its Editors are Runze Li of Pennsylvania State University, Weijie Su of the University of Pennsylvania and Hongyu Zhao of Yale University. The journal's own launch editorial, titled Statistical Learning and Data Science: A new home for an evolving field, was authored by Song Xi Chen and Jiashun Jin.

The Advisory Board brings together five senior figures in the field: David Donoho of Stanford University, Jianqing Fan of Princeton University, Michael I. Jordan of the University of California, Berkeley, Jun S. Liu of Tsinghua University and Wing Hung Wong of Stanford University. Below them sits a large Associate Editor board — including scholars at institutions such as Harvard University, Columbia University, Stanford University, Duke University, Cornell University, the London School of Economics and Ghent University, alongside Chinese universities including Peking University, Tsinghua University and Fudan University — and a Youth Editorial Board of more than fifty early- and mid-career researchers. The Managing Editor is Ruiyan Zhang, and the editorial office is the SLADS Editorial Office at Science China Press, No. 16 Dong-huang-cheng-gen North Street, Beijing 100717, China.

Submission and peer review

SLADS uses an unusual two-step submission process, explained on its own submission site. Authors first submit through the journal's initial channel, where executive editors carry out an editorial prescreening that typically takes up to seven days. The prescreening verifies that a manuscript falls within the journal's scope and meets its basic standards. Authors then receive one of two outcomes: rejection at that stage, or an invitation to resubmit the manuscript in full through the OpenReview platform, where it enters the formal peer review process.

According to the journal's special-issue guidance, SLADS uses the OpenReview system to support transparency and quality in review, and its editorial goal is to reach an accept or reject decision within three and a half months of submission while maintaining high scholarly standards. All submissions must use the journal's LaTeX manuscript template, which provides pre-formatted styles for headings, references, figures and tables. Questions about submission can be directed to the editorial office at slads@scichina.com.

Open access and publication charges

SLADS is a fully open-access journal: all articles are immediately and permanently free for everyone to read, download, copy and distribute. Articles are published under one of two Creative Commons licences according to the author's choice — Creative Commons Attribution (CC BY), which permits copying, adaptation and commercial use with appropriate credit, or Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND), which permits copying and distribution for non-commercial purposes without alteration.

The open-access publication fee, or article processing charge, is USD 1,000 plus taxes. However, all article processing charges for articles submitted to the journal before 31 December 2028 will be fully waived, so authors submitting during the journal's launch period pay no fee. Peer review is conducted under the responsibility of the Editorial Board of Statistical Learning and Data Science.

Early content and special issues

Although newly launched, the journal's early article record was already visible on ScienceDirect, with research articles carrying 2026 dates and DOIs in the journal's own series. Examples include work on non-asymptotic analysis of median-of-means estimation for high-dimensional time series, on the limiting spectral distribution of sample covariances of superposition of matrices with a separable covariance structure, and on quantitative analysis of the rightmost eigenvalue for a large chiral non-Hermitian random matrix — titles that illustrate the journal's emphasis on rigorous, theory-driven statistics.

Alongside regular submissions, SLADS runs special issues. In addition to the special issue on high-dimensional inference and causal inference, the journal announced a Special Issue in Memory of Professor George C. Tiao (1934–2026), dedicated to a statistician whose pioneering contributions shaped time series analysis and related fields. Special issues of this kind signal the community the journal intends to serve: researchers working at the frontier where statistical theory meets modern, data-intensive science.

Publisher and institutional context

Science China Press, the journal's owner, is one of China's most important professional academic publishers, affiliated with China Science Publishing & Media Ltd. and operating under the Chinese Academy of Sciences. Its established products include the Science China series of journals, Science Bulletin, National Science Review and the Journal of Energy Chemistry, and its journals aim to disseminate original, cutting-edge advances in fundamental and applied research to scientists worldwide. SLADS extends that portfolio into statistics and data science.

Publishing services come through KeAi Communications, a Beijing-based joint venture co-founded by China Science Publishing & Media and Elsevier and repurposed in 2013 as an open-access journal publishing service provider. KeAi has since developed a portfolio of more than 280 scholarly journals across all scientific disciplines, leveraging Elsevier platforms such as ScienceDirect and Editorial Manager. For a new journal, that infrastructure provides immediate global distribution, while the Science China Press ownership and the Beijing editorial office anchor SLADS firmly in China's research community even as its board and authorship are international.