He amended one of the three basic model types of the discipline, chance-constrained programming, by taking into account stochastic dependence among the random variables involved.

He introduced the concept of logarithmic concave measures and provided several fundamental theorems on logconcavity, which supplied proofs for the convexity of a wide class of probabilistically constrained stochastic programming problems.

These results had impact far beyond the area of mathematical programming, as they found applications in physics, economics, statistics, convex geometry and other fields.

In 1952, he became a Ph.D student (aspirant) at the Institute for Applied Mathematics of the Hungarian Academy of Sciences (HAS) and defended his thesis, entitled "On Stochastic Set Functions", in 1956.

He published more than a dozen books and about 350 papers alone and with co-authors and supervised 51 Ph.D students, many of whom are internationally known academics and industrial leaders.