**Rudolf E. Kalman **was born in Budapest, Hungary, on May 19, 1930. He received the bachelor's degree (S.B.) and the masterís degree (S.M.) in electrical engineering, from the Massachusetts Institute of Technology in 1953 and 1954, respectively. He received the doctorate degree (D.Sci.) from Columbia University in 1957. His major positions include that of Research Mathematician at the Research Institute for Advanced Study in Baltimore, 1958-1964; Professor at Stanford University 1964-1971; Graduate Research Professor at the Center for Mathematical System Theory, University of Florida, Gainesville 1971-1993. Moreover, since 1973 he has also held the chair for Mathematical System Theory at the ETH (Swiss Federal Institute of Technology) Zurich.

He is the recipient of numerous awards, including the IEEE Medal of Honor (1974), the IEEE Centennial Medal (1984), the Kyoto Prize in High Technology from the Inamori foundation, Japan (1985), the Steele Prize of the American Mathematical Society (1987). He is a member of the U.S. National Academy of Science, the U.S. National Academy of Engineering, a foreign member of the Hungarian and French Academies of Science, and has received a number of honorary doctorates. Kalman's first major contribution was the introduction of the self-tuning regulator in adaptive control. Between 1959 and 1964 Kalman wrote a series of seminal papers. First, the new approach to the filtering problem, known today as Kalman Filtering was put forward. In the meantime, the all pervasive concept of controllability and its dual, the concept of observability, were formulated. By combining the filtering and the control ideas, the first systematic theory for control synthesis, known today as the Linear-Quadratic-Gaussian or LQG theory, resulted. The next contribution was the solution of the black box modelling problem in the linear case, known as realization theory. This problem involves the construction of the state from input/output measurements. The next milestone in the sequence of contributions was the introduction of module theory to the study of linear systems.

Over the past 15 years Kalman has devoted his efforts to the understanding of the problem of identification from noisy data with particular attention to the connections with econometrics, statistics and probability theory.