Prof. Yury Polyanskiy

Professor Polyanskiy’s research is rooted in a fascination with the flow of information, initially in the traditional setting of point-to-point communications, then in the context of communication networks, and more recently in general types of networks (including neural networks). Beginning with his doctoral thesis, Polyanskiy developed a refinement of the classical approach, centered around a suitably defined concept of channel dispersion, which can be used to quantify the achievable communication rates in the finite block-length regime. This work led to multiple awards, and is considered by now as one of the core pieces of modern information theory. Polyanskiy has made an impressive number of deep contributions in several different domains in the last several years. Traditional information and communication theory has addressed either point-to-point transmission of information or network problems involving a moderate number of users. Consider now a setting involving ten thousand users who wish to transmit only 1 kbit per second, on the average. This latter regime is a much better reflection of current IoT trends, where there is a very large number of devices, each of which wakes up only occasionally and wishes to transmit a relatively short message. Polyanskiy, in his seminal 2017 ISIT paper (“A perspective on massive random-access”), proposed a mathematical model that captures this regime. This model has since become known as UMAC (Unsourced Multiple Access). At a high level, Polyanskiy essentially showed that traditional orthogonality-based coding schemes are bound to be inefficient, and developed a lower bound on the required energy per bit, as a function of the number of users. He also derived an upper bound (by showing the existence of a code), which is not too far from the lower bound.