ACM and IMS publish first issues of Journal of Data Science – High Performance Computing News Analysis

New York, March 27, 2024 – ACM, the Society for Computing Machinery, and IMS, the Institute for Mathematical Statistics, have announced the publication of the first issues of ACM/IMS Journal of Data Science (JDS)a peer-reviewed publication.

The scope of the journal is multi-disciplinary and broad, including statistics, machine learning, computer systems and the societal implications of data science. JDS accepts original papers as well as new surveys that summarize and organize critical topic areas. ACM/IMS Journal of Data Science is a Gold Open Access edition, permanently and freely available online for anyone, anywhere to read.

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

The editors-in-chief of the 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 fundamental issues in data science,” explained Co-EiC John Lafferty. “The journal builds on this desire and will provide a forum where a wide 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 invited distinguished experts to submit papers on specific topics. Following JDS guidelines, the documents then went through a rigorous review process. The inaugural issues also represent the breadth of work being done in the field. We thank all the editors and researchers whose efforts have made this launch possible.”

“As should be expected from a collaborative effort between two major scientific societies, the first issues of 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 cross the fields of machine learning, statistics, artificial intelligence, databases, and data management systems. We have carefully planned JDS in order to lay a strong foundation for what we expect to be the premier 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: Diverse 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 Increments: 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 Incentives into Weak Supervision,” by Ryan Smith, Jason A. Fries, Braden Hancock, and Stephen H. Bach

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

“Anytime Valuable Conclusion Outside of Politics for Contextual Thugs,” by Ian Waudby-Smith, Lili Wu, Aaditya Ramdas, Nikos Karampatziakis, and Paul Mineiro

In addition to EiCs, ACM/IMS JDS The editorial board includes nine senior editors: Jeffrey A. Blimes, Alexandra Chouldechova, Jianqing Fan, Kevin P. Murphy, Tamer Ozsu, Dimitris Politis, Alkis Polyzotis, Cynthia Rudin; 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, Timothy Sellis, Alkis Simitsis, Julia Stoyanovich, Alexander Volfovsky, Stefan Wager and Zhuoran Yang.

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

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