Association for Computing Machinery and Institute of Mathematical Statistics Release First Issues of ACM/IMS Journal of Data Science

Gold Open Access Journal Publishes High-Impact Research from All Areas of Data Science

New York, NY, March 27, 2024 – ACM, the Association for Computing machinery, and IMS, the Institute of Mathematical Statistics, have announced the publication of the first issues of the ACM/IMS Journal of Data Science  (JDS), a new peer-reviewed publication. 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 ACM/IMS Journal of Data Science is a Gold Open Access publication, permanently and freely available online for anyone, anywhere to read.

Data science is increasingly central to science and businesses world-wide. JDS aims to serve a diverse community of scientists and practitioners, helping to develop a common language and make the best research results easily accessible and broadly recognized. ACM/IMS JDS publishes high-impact research from all areas of data science, across foundations, applications and systems. By combining elements of journal and conference publishing, the journal aims to serve the needs of a rapidly evolving research landscape.

The Co-Editor-in-Chiefs of ACM/IMS Journal of Data Science are Jelena Bradic, University of California, San Diego; Stratos Idreos, Harvard University; and John Lafferty, Yale University.

“The publication of this new journal grew out of a desire to bring together related communities working on foundational issues in data science,” explained Co-EiC John Lafferty. “The journal builds on that desire and will provide a forum where a broad range of data scientists can share the best research, exchange ideas and be inspired.”

“Our vision for ACM/IMS JDS is to publish research that meets the highest standards,” added Co-EiC Stratos Idreos. “To this end, for the inaugural issues, the EiC’s invited preeminent experts to submit papers on specific topics. Following JDS guidelines, the papers then went through a rigorous review process. The inaugural issues also represent the breadth of work being done in the field. We thank all of the editors and researchers whose efforts have made this launch possible.”

“As should be expected of a collaborative effort between two leading scientific societies, the first issues of the ACM/IMS Journal of Data Science contain several interdisciplinary articles,” said Co-EIC Jelena Bradic. “For example, the inaugural issue of the journal includes four research papers that intersect the areas of machine learning, statistics, artificial intelligence, databases, and data management systems. We’ve carefully planned the JDS in order to lay a strong foundation for what we expect will be the preeminent publication of its kind.”

Articles in the inaugural issues include:

Batched Neural Bandits,” by Quanquan Gu, Armin Karbasi, Khashayar Khosravi, Vahab Mirrokni, and Dongruo Zhou

Record Fusion via Inference and Data Augmentation,” by Alireza Heidari, George Michalopoulos, Shrinu Kushagra, Ihab F. Ilyas, and Theodoros Rekatsinas

DNBP: Differentiable Nonparametric Belief Propagation,” by Anthony Opipari, Jana Pavlasek, Chao Chen, Shoutian Wang, Karthik Desingh, and Odest Chadwicke Jenkins

Data Management for ML-based Analytics and Beyond,” by Daniel Kang,John Guibas, Peter Bailis,Tatsunori Hashimoto, Yi Sun, and Matei Zaharia

Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression,” by Lijia Zhou, Frederic Koehler, Danica J. Sutherland, and Nathan Srebro

Language Models in the Loop: Incorporating Prompting into Weak Supervision,” by Ryan Smith, Jason A. Fries, Braden Hancock, and Stephen H. Bach

Principal Component Networks: Utilizing Low-Rank Activation Structure to Reduce Parameters Early in Training,” by Roger Waleffe and Theodoros Rekatsinas

Anytime-valid off-policy inference for contextual bandits,” by Ian Waudby-Smith, Lili Wu, Aaditya Ramdas, Nikos Karampatziakis, and Paul Mineiro

In addition to the EiC’s, the ACM/IMS JDS Editorial Board includes nine Senior Editors: Jeffrey A. Blimes, Alexandra Chouldechova, Jianqing Fan, Kevin P. Murphy, Tamer Ozsu, Dimitris Politis, Alkis Polyzotis, Cynthia Rudin, and Eric Tchetgen Tchetgen; as well as 22 Associate Editors: Iavor Bojinov, Renata Borovica-Gajic, Peng Ding, Barbara Engelhardt, Sharad Goel, Nathan Kallus, Ed Kennedy, Georgia Koutrika, Sanjay Krishnan, Guoliang Li, Po-Ling Loh, Tengyu Ma, Jonas Peters, Mert Pilanci, Aaditya Ramdas, Andrej Risteski, Timos Sellis, Alkis Simitsis, Julia Stoyanovich, Alexander Volfovsky, Stefan Wager and Zhuoran Yang.

ACM publishes more than 70 scholarly peer-reviewed journals in dozens of computing and information technology disciplines. Available online through the ACM Digital Library, ACM’s high-impact journals constitute a vast and comprehensive archive of computing innovation, covering emerging and established computing research for both practical and theoretical applications.

About ACM

ACM, the Association for Computing Machinery, is the world's largest educational and scientific computing society, uniting educators, researchers, and professionals to inspire dialogue, share resources, and address the field's challenges. ACM strengthens the computing profession's collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.

About IMS

IMS, the Institute of Mathematical Statistics, is the leading organization fostering the development and dissemination of the theory and applications of statistics.

Jim Ormond
[email protected]

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