The ACM–IMS Journal of Data Science (JDS) is a new joint journal of the Association of Computing Machinery (ACM) and the IMS, publishing high-impact research from all areas of data science, across foundations, applications and systems. The scope of the journal is multi-disciplinary and broad, spanning statistics, machine learning, computer systems, and the societal implications of data science. JDS accepts original papers as well as novel surveys that summarize and organize critical subject areas.

The inaugural issue of the new journal is online now. Volume 1, issue 1 of JDS contains papers on “Batched Neural Bandits” by Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab Mirrokni, and Dongruo Zhou; “Identification and semiparametric efficiency theory of non-ignorable missing data with a shadow variable” by Wang Miao, Lan Liu, Yilin Li, Eric Tchetgen Tchetgen, and Zhi Geng; “Record Fusion via Inference and Data Augmentation” by Alireza Heidari, George Michalopoulos, Shrinu Kushagra, Ihab F. Ilyas, and Theodoros Rekatsinas; “Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression” by Lijia Zhou, Frederic Koehler, Danica J. Sutherland, and Nathan Srebro; “DNBP: Differentiable Nonparametric Belief Propagation” by Anthony Opipari, Jana Pavlasek, Chao Chen, Shoutian Wang, Karthik Desingh, and Odest Chadwicke Jenkins; and “Data Management for ML-based Analytics and Beyond” by Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, and Matei Zaharia.

The Editors-in-Chief of JDS are Jelena Bradic, Stratos Idreos, and John Lafferty.

Read the papers, and find out how to submit your paper to the journal, at http://jds.acm.org/