Communications and Information Foundations (CIF)
See program guidelines for contact information.
The Communications and Information Foundations (CIF) program supports potentially transformative research that addresses the theoretical underpinnings of information acquisition, transmission, and processing in communications and information processing systems. As a result, CIF research and education projects strengthen the intellectual foundations of communications, information theory, signal processing, and statistical learning in a variety of types of networks such as wireless and multimedia networks, sensor networks, social networks, and biological and quantum networks. Research outcomes are expected to lead to more secure and reliable communications and advanced mathematical capabilities that are applicable throughout science and engineering.
The program supports basic research in communication theory, information theory, and signal processing. Included in the CIF program is the reliable transmission of information in the presence of a variety of resource constraints (e.g., energy, bandwidth, computation, time, and privacy) and channel impairments (e.g., noise, multipath, interference, and eavesdroppers). CIF likewise has a strong interest in the role of signal processing, coding, and information theory in distributed processing systems handling massive amounts of data and impacting the control, operation and robustness of real-time devices and networks, including human-in-the-loop modeling, processing, and learning.
The CIF program also supports fundamental research in networking including network information theory and cross-layer research at the lower layers. The CIF program in networking emphasizes research in which the physical-layer attributes play an important role in overall network design and performance. This includes cross-layer approaches that consider the impact of physical-layer characteristics on higher network layers. Examples include secure communication, sensor networks with applications including environmental monitoring, crowd-sourcing, and smart grids, and other application scenarios that feature massive data aggregation from distributed sensing, such as the Internet of Things.
In addition to the contemporary signal processing topics that have enabled the information revolution, there is growing interest within the CIF program in new paradigms that enlarge the scope of signal processing and information theory such as advances in statistical learning and inference, signal processing on graphs, distributed processing for multi-terminal communication problems, information-theoretic security, the all-pervasive role of geometric methods in signal processing and machine learning, new mathematical frameworks for addressing new problems, communication-theoretic challenges in terahertz and millimeter-wave frequencies, and machine learning for network optimization. Research that will develop efficient power-aware and hardware-friendly algorithms and research on signal/information processing algorithms for the new network science of distributed, decentralized, and cooperative algorithms is encouraged. The derivation of efficient algorithms and fundamental limits for extracting information from massive and possibly corrupted data sets, including compressive sampling/sensing and active learning, and exploring new application domains, also promise advances in the field, especially in the face of information overload, where one has too much information rather than too little.
The CIF program is particularly interested in the study of signal/information processing in complex systems, as signal processing and information theory may be viewed more broadly and holistically by other areas in machine learning and data science. Some examples of complex systems and applications include monitoring the Nation's critical infrastructures, signal processing and information theory in biological systems, and information flow in socio-technical networks. While advances in these areas have the potential for broad societal impact, the study of these and other emerging application domains is expected to lead to new insights in the underlying theory by posing new constraints and challenges and leading to the reexamination of old questions and assumptions, e.g., new mathematical approaches to deep learning.
More information on topics of interest within this program is available at:
Funding Opportunities for the Communications and Information Foundations Program: