CIF: Small: Adaptive Information: Sequential Sensing and Active Learning Theory, Methods and Applications

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Complex systems, such as living cells and the Internet, involve interactions and associations between large numbers of individual components. Specific examples include interactions between proteins in biological cells and connections between users in communication networks. Developing theory and methods to model and analyze large-scale networked systems is a grand challenge for the 21st century. For instance, new mathematical methods will lead to better models of biological systems and thus improvements in disease prevention. Measuring the signals of such systems is a crucial step in developing good models, and a major hurdle is that it is often impractical or impossible to measure all variables in large systems. This project addresses the hurdle by developing adaptive methods that automatically adjust the measurement process by using information gleaned from previously collected data in order to focus and optimize the gathering of new information. Ultimately, these methods will dramatically accelerate the pace of discovery in science and engineering.The main theme of the project is an investigation of the role of adaptive measurement, sensing and experimentation in large complex systems of many variables. Adaptive methods are sequential procedures that optimize the selection of the next measurements or experiments based on previously gathered data. The research involves the development of a general theory for adaptive measurement that is applicable to various domains of engineering and science. The main goals are to mathematically characterize and quantify the advantages of adaptive measurements relative to non-adaptive methods and to design optimal adaptive measurement procedures. The investigation also explores the potential of adaptive methods by studying specific problems and applications in biology, national security, and human-computer interaction.