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Donald Geman (born September 20 1943) is an American statistician and a leader researcher in the realm of machine learning and pattern recognition. He and his brother, Stuart Geman, are very well known because of proposing gibbs sampler and the first proof of the convergence of the simulated annealing in one of their works which became a highly cited work in the realm of engineering. He is at the moment a professor at the Johns Hopkins University and simultaneously a visiting professor at École Normale Supérieure de Cachan.

Biography
Donald Geman was born in Chicago in 1943. He started studying English literature at Columbia University in 1961. In 1963, he transferred to University of Illinois at Urbana-Champaign where he graduated in 1965. He graduated from Northwestern University in the field of mathematics in 1970. His dissertation was entitled as "Horizontal-window conditioning and the zeros of stationary processes." He joined University of Massachusetts Amherst in 1970 where he retired as a distinguished professor in 2001. Thereafter, he became a professor at the Department of Applied Mathematics at the Johns Hopkins University. He has also been a visiting professor at the École Normale Supérieure de Cachan since 2001. He is now a fellow of Institute of Mathematical Statistics.

Work
D. Geman and J. Horowitz published a series of papers during the late 70s on local times and occupation densities of stochastic processes. A survey of this work and other related problems can be found in the Annals of Probability. In 1983 with his brother Stuart, he published a milestone paper which is at this date one of the most cited papers in the engineering literature. It introduces a Bayesian paradigm using Markov Random Fields for the analysis of images. This approach has been highly influential over the last 20 years and remains so today which is a rare tour de force in this rapidly evolving field. In another milestone paper, in collaboration with Y. Amit, he introduced the notion for randomized decision trees which have been called random forests and popularized by Leo Breiman. Some of his recent works include the introduction of coarse-to-fine hierarchical cascades for object detection in computer vision and contribution to the invention of the TSP (Top Scoring Pairs) classifier as a simple and robust rule for classifiers trained on high dimensional small sample datasets in bioinformatics.