In this issue, alongside a feature article on missing data that inspired the cover, we present several other timely contributions, including our first article in a series highlighting standards activities in our field. This two-part article [A1, A2] describes the technical leadership of the IEEE Signal Processing Society Synthetic Aperture Standards Committee (SPS-SASC) and its working groups in developing standards for the rapidly evolving field of computational imaging and sensing. This marks the first standards initiative led by the IEEE Signal Processing Society in partnership with the IEEE Standards Association. Thanks to the dedicated leadership of Peter Vouras and the commitment of many volunteers, SPS-SASC has grown into a vibrant standards committee with 14 working groups in less than five years, with the standard on sonar imaging now approaching publication.

The goal of this paper is to provide an overview of recent methods for handling missing data in signal processing methods, from their origins to the challenges ahead. Missing data approaches are grouped by three main categories: i) missing-data imputation, ii) estimation with missing values and iii) prediction with missing values. We focus on methodological and experimental results through specific case studies on real-world applications. Promising and future research directions, including a better integration of informative missingness, are also discussed. We believe that the proposed conceptual framework and the presentation of the main problems related to missing data will encourage researchers of the signal processing community to develop original methods for handling missing values and to deal with new applications involving missing data in an adequate manner.

Possessing a manipulable representation of the world is a requirement for intelligent machines to plan, reason, and act in the world. Endowing computational systems, e.g., deep networks (DNs), with artificial intelligence (AI) capabilities is the goal of self-supervised learning (SSL). Immense optimism, fueled by early successes, funneled vast resources into SSL, which led to fast-paced but fragmented early developments. 

Deep learning (DL) is a field of study within machine learning and signal processing that has been around for nearly 40 years. In the last 10 years, its progress on problems including speech-to-text, image recognition, image generation, and language generation has been phenomenal. This exponential progress has been driven by exciting engineering and algorithmic developments.

Deep neural networks (DNNs) trained via gradient descent (GD) with random initialization and without any regularization enjoy good generalization performance in practice despite being highly overparametrized. To theoretically understand this puzzling phenomenon, many works on convergence analysis for GD algorithms on NNs have been developed over the last half-decade.