The ACM–IMS Journal of Data Science (JDS) is a 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 second issue of the new journal is online now. Volume 1, issue 2 of JDS contains the following papers:

• “Identification and semiparametric efficiency theory of non-ignorable missing data with a shadow variable” by Wang Miao (Peking University), Lan Liu (University of Minnesota), Yilin Li (Peking University), Eric Tchetgen Tchetgen (University of Pennsylvania), and Zhi Geng (Peking University);

• “Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression” by Lijia Zhou (University of Chicago), Frederic Koehler (Stanford University), Danica J. Sutherland (University of British Columbia; Alberta Machine Intelligence Institute), and Nathan Srebro (Toyota Technological Institute at Chicago;

• “Language Models in the Loop: Incorporating Prompting into Weak Supervision” by Ryan Smith (Snorkel AI), Jason A. Fries (Stanford University and Snorkel AI), Braden Hancock (Snorkel AI), and Stephen H. Bach (Brown University and Snorkel AI);

• “Principal Component Networks: Utilizing Low-Rank Activation Structure to Reduce Parameters Early in Training” by Roger Waleffe (University of Wisconsin–Madison) and Theodoros Rekatsinas (ETH Zurich).

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

There are three submission deadlines per year: January 15, May 15, and September 15.

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