Data compression, a major pillar of classical information theory, has undergone significant developments in recent years. The emergence of powerful machine learning methods has created new opportunities to approach traditional compression problems, while data compression itself now plays an increasingly important role in the storage and processing of large neural network models. Moreover, modern big-data applications demand more efficient compression and storage strategies, along with stronger security and privacy guarantees. Compression techniques are also expected to become integral components of next-generation communication paradigms. Against this backdrop, the proposed special issue aims to provide a platform where classical theories meet their modern advances. The goal of this special issue is to provide a venue for researchers to present recent developments in data compression, particularly those inspired by machine learning, big data applications, and security and privacy requirements.
Lead Guest Editors:
Chao Tian (Texas A&M University)
Jun Chen (McMaster University)
Guest Editors:
Deniz Gunduz (Imperial College London)
Pulkit Tandon (Granica.ai)
Lele Wang (University of British Columbia)
Senior Editor:
Elza Erkip (New York University)
Manuscript Due: Continuous submission until March 31, 2026.
Publication Date: Each accepted manuscript will be published on ÌÇÐÄlogo Xplore after finishing its peer review with a final deadline for publishing the complete special issue by November 2026.
More Information: /jsait/jsait-call-papers/data-compression-classical-theories-meet-modern-advances-1