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Professor Vapniks' major achievements include the development of a general theory for minimizing the expected risk of losses using empirical data and a new type of learning machine that possesses a high level of generalization ability. His techniques have been used to solve many pattern recognition and regression estimation problems and have been applied to the problems of biomarker identification of genes and proteins in diagnostic and drug discovery.
From 1965 to 1990 he worked at the Institute of Control Sciences, Moscow, where he became Head of the Machine Learning Research Department. He then moved to the United States and joined AT&T Bell Laboratories and later AT&T Labs-Research. A teacher and researcher in theoretical and applied statistics for over thirty years, he has published seven books and over a hundred research papers. Professor Vapnik is presently a senior researcher at NEC Research and Professor at Princeton University.
Recently, Professor Vapnik was awarded the prestigious Humboldt Research Award for his academic achievements by the Alexander-von-Humboldt-Foundation. The Alexander von Humboldt Foundation grants awards annually to foreign scientists and scholars in recognition of their lifetime academic achievements. Thirty-four Humboldt winners have been awarded a Nobel Prize for their work. | 