Harold J. Kushner received the Ph.D. in Electrical Engineering from the University of Wisconsin in 1958. Since then, in ten books and more than two hundred papers, he has established a substantial part of modern stochastic systems theory. These include seminal developments of stochastic stability for both Markovian and non-Markovian systems, optimal nonlinear filtering and effective algorithms for approximating optimal nonlinear filters, stochastic variational methods and the stochastic maximum principle, numerical methods for jump-diffusion type control and game problems (the current methods of choice), efficient numerical methods for Markov chain models, methods for singularly perturbed stochastic systems, an extensive development of controlled stochastic networks such as queueing/communications systems under conditions of heavy traffic, methods for the analysis and approximation of systems driven by wideband noise, large-deviation methods for control problems with small noise effects, stochastic distributed and delay systems, and nearly optimal control and filtering for non-Markovian systems. His work on stochastic approximations and recursive algorithms has set much of the current framework, and he has contributed heavily to applications of control methods to communications problems. He is a past Chairman of the Applied Mathematics Department and past Director of the Lefschetz Center for Dynamical Systems, at Brown University, where he is currently a University Professor Emeritus.