This special issue of the IEEE Journal on Selected Areas in Information Theory is dedicated to the memory of Alexander Vardy, a pioneer in the theory and practice of channel coding. His ground-breaking contributions ranged from unexpected solutions of longstanding theoretical conjectures to ingenious decoding algorithms that broke seemingly insurmountable barriers to code performance. Inspired not just by the mathematical beauty of coding theory but also by its engineering utility, Alexander Vardy developed novel coding techniques that have had a profound impact on modern information technology, including computer memories, data storage systems, satellite communications, and wireless cellular networks. At the same time, his innovations left their imprint on other scientific disciplines, such as information theory, computer science, and discrete mathematics.

Causal determinism, is deeply ingrained with our ability to understand the physical sciences and their explanatory ambitions. Besides understanding phenomena, identifying causal networks is important for effective policy design in nearly any avenue of interest, be it epidemiology, financial regulation, management of climate, etc. In recent years, many approaches to causal discovery have been proposed predominantly for two settings: a) for independent and identically distributed data and b) time series data. Furthermore, causality- inspired machine learning which harnesses ideas from causality to improve areas such as transfer learning, reinforcement learning, imitation learning, etc is attracting more and more interest in the research community. Yet fundamental problems in causal discovery such as how to deal with latent confounders, improve sample and computational complexity, and robustness remain open for the most part. This special issue aims at reporting progress in fundamental theoretical and algorithmic limits of causal discovery, impact of causal discovery on other machine learning tasks, and its applications in sciences and engineering.

Modern networks rely on a variety of technologies to sense the environment for static or locomotive objects, in particular their shapes, distances, directions, or velocities. Sensing is a key feature in these networks and enables for example autonomous driving, motion sensing in health applications, target detection in smart cities, or optimal beam selections in millimeter wave communication. Besides these exciting new applications, sensing remains an important feature also for traditional applications such as temperature monitoring, or earthquake or fire detection, where new technologies are exploited including continuous feature monitoring over the entire range of an optical fiber network. The purpose of this special issue is to report on new exciting applications of sensing in modern networks, novel sensing architectures, innovative signal processing mechanisms related to sensing, as well as new results on the fundamental performance limits (resolution, sample complexity, robustness) of sensing systems. Particular focus will be on joint systems that integrate sensing with other tasks, for example communication, information retrieval (estimation, feature extraction, localization), super-resolution.

Deep learning methods have emerged as highly successful tools for solving inverse problems. They achieve state-of-the-art performance on tasks such as image denoising, inpainting, super-resolution, and compressive sensing. They are also starting to be used in inverse problems beyond imaging, including for solving inverse problems arising in communications, signal processing, and even on non-Euclidean data such as graphs. However, a wide range of important theoretical and practical questions remain unsolved or even completely open, including precise theoretical guarantees for signal recovery, robustness and out-of-distribution generalization, architecture design, and domain-specific applications and challenges. This special issue aims to advance cutting-edge research in this area, with an emphasis on its intersection with information theory.

Modern computation environments are struggling to store, communicate and process data in unprecedented volumes. These data, which come in new and evolving structures and formats, necessitate compression, lossless and lossy. Recent years have witnessed the emergence of new techniques, approaches, and modes for data compression. This special issue will focus on cutting edge research in this space.