Information theory, communication theory, and statistical signal processing have proven spectacularly successful as innovation engines for communication, data compression, and information processing technologies. Although in principle these theories are quite general, in practice they are most useful for a particular family of models, such as discrete Markov sources and channels with additive Gaussian noise. Although this family is quite rich and encompasses many practical technologies, there are several important scenarios that fall outside of these models. In particular, the timing of discrete events is the modality of interest in many applications, such as neuroscience and certain problems in network security, and this modality does not fit neatly into the standard classes. This research involves extending information theory, communication theory, and statistical signal processing to develop a general theory of information transfer via timing.
The first phase of this research involves solving several concrete problems involving information transfer via timing, such as the secrecy capacity of timing channels and how to minimize information transfer via timing side channels in both wireless and wireline networks. The second phase involves interconnecting these disparate problems to form a general theory of information transfer via timing. By elevating the role of timing in information transfer, this research better positions the fields of information theory, communication theory, and statistical signal processing to impact allied fields such as networking, neuroscience, and operations research, where timing and delay are of fundamental importance. This impact is facilitated through special topics courses and invited sessions at conferences, both of which are designed to attract participants from a range of areas.
