The field of information theory - dating back to the seminal work by Claude E. Shannon in 1948 - is considered to be one of the landmark intellectual achievements of the 20^th century, underpinning advances in compression and communication of data that underpins the information age. In particular, information-theoretic methods have been used to illuminate fundamental limits and gauge the effectiveness of algorithms for various problems in statistical decision theory, data communications, data compression, security, and networking. This has led to a series of technological breakthroughs in areas such as data storage, optical communications and networks, wireless communications and networks, and Internet technology.

Information theory – including its key ideas, methods and tools – has also played an important role in the general area of data science, most notably in information acquisition, information analysis and processing, statistics, probability, and learning. In particular, recent years have witnessed a renaissance in the use of information-theoretic methods to address problems beyond data compression, data communications, and networking, such as, e.g., compressive sensing, dictionary learning, supervised and unsupervised learning, reinforcement learning, graph mining, community detection, privacy, and fairness.

The special issue on Information-Theoretic Methods in Data Acquisition, Analysis, and Processing published in IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING in Oct. 2018 explores applications of information theoretic methods to emerging data science problems. In particular, the papers cover a wide range of topics that can broadly be organized into four themes:

(1) data acquisition

(2) data analysis and processing

(3) statistics and machine learning

(4) privacy and fairness.