We are experiencing a pivotal moment in the history of our profession. Our community’s technology finds applications in various fields, including machine learning, integrated sensing, processing, and communication; robotics and autonomous systems; medical and computational imaging; communication systems; and speech and language processing systems, among others. As signal processing technology continues to advance, these applications will likely expand. The need to narrow the gap between academia and industry has seldom been greater.

With the advent of large language models (LLMs), the concept of artificial intelligence (AI) agents capable of decision making and action execution has gained prominence, particularly as LLMs demonstrate increasing proficiency in tool use and task planning. Following these advancements, the established methods used for task-oriented dialogue (TOD) systems have undergone a paradigm shift by integrating LLMs’ revolutionary language understanding and reasoning skills with enhanced instruction following and response generation abilities.

The Golden Jubilee edition of the IEEE Signal Processing Society’s (SPS’s) flagship conference, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), was held on 6–11 April 2025 at the Hyderabad International Convention Center in Hyderabad, India. This was the first time an ICASSP was organized in India. Here, we provide a brief summary of the event. The goal of the organizers was to provide an affordable, inclusive, memorable, and sustainable (AIMS) event that lived up to the lofty technical standards of an ICASSP.

Fractional programming (FP) is a branch of mathematical optimization that deals with the optimization of ratios. It is an invaluable tool for signal processing and machine learning, because many key metrics in these fields are fractionally structured, e.g., the signal-to-interference-plus-noise ratio (SINR) in wireless communications, the Cramér-Rao bound (CRB) in radar sensing, the normalized cut in graph clustering, and the margin in support vector machine (SVM).