Donald Jay Geman (born September 20, 1943) is an American applied mathematician and a leading researcher in the field of machine learning and pattern recognition.

He and his brother, Stuart Geman, are very well known for proposing the Gibbs sampler and for the first proof of the convergence of the simulated annealing algorithm,[1] in an article that became a highly cited reference in engineering (over 21K citations according to Google Scholar, as of January 2018).

D. Geman and J. Horowitz published a series of papers during the late 1970s on local times and occupation densities of stochastic processes.

This approach has been highly influential over the last 20 years and remains a rare tour de force in this rapidly evolving field.

Some of his recent works include the introduction of coarse-to-fine hierarchical cascades for object detection[10] in computer vision and the TSP (Top Scoring Pairs) classifier as a simple and robust rule for classifiers trained on high dimensional small sample datasets in bioinformatics.