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.
The current era of artificial intelligence (AI) is characterized by the scaling of data and computational resources as the primary driver of emergent capabilities in AI models. However, this trend faces fundamental constraints on data availability and computing power. Recent breakthroughs suggest an alternative path forward—leveraging novel statistical and information-theoretic tools to enhance reliability and data efficiency, while enhancing intelligence per joule and per data point/token via hardware-software co-design principles. Understanding the fundamental limits of AI through the lens of information and physical principles, such as Landauer's principle, is crucial for developing sustainable and efficient learning systems. This special issue aims to advance theoretical and algorithmically motivated approaches to optimizing AI performance while reducing reliance on extensive data and energy resources.
Information theory has been a mainstay of wireless communications ever since these became digital in nature, directly or indirectly influencing most of the constituent technologies. Not only has information theory provided fundamental bounds that enable gauging the performance of specific techniques, but it has yielded insights on transmitter/receiver structures and the air interface at large, revealed essential tradeoffs, and delineated regimes where operating conditions and channel mechanisms are fundamentally different. This special issue aims to investigate the role of information theory moving forward, in the age of satellites, drones, self-driving vehicles, robots, and AI.
This Special Issue targets original work pushing the boundary of DNA-based data storage and emphasizes the consideration of data privacy and security therein. Researchers from all the scientific communities investigating the topic are encouraged to submit their work.
We will follow a continuous publication model.
The goal of this special issue is to invite previously unpublished work in the broad areas of quantum error correction and fault tolerance with connections to classical and quantum information theory.