# Stuart Geman

> Mediated Wiki article. Canonical URL: https://mediated.wiki/source/Stuart_Geman
> Markdown URL: https://mediated.wiki/source/Stuart_Geman.md
> Source: https://en.wikipedia.org/wiki/Stuart_Geman
> Source revision: 1346434979
> License: Creative Commons Attribution-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-sa/4.0/)

**Stuart Alan Geman** (born March 23, 1949) is an American [mathematician](/source/Mathematician), known for influential contributions to computer vision, statistics, probability theory, [machine learning](/source/Machine_learning), and the neurosciences.[1][2][3][4] He and his brother, [Donald Geman](/source/Donald_Geman), are well known for proposing the [Gibbs sampler](/source/Gibbs_sampling), and for the first proof of convergence of the [simulated annealing algorithm](/source/Simulated_annealing).[5][6]

## Biography

Geman was born and raised in Chicago. He was educated at the [University of Michigan](/source/University_of_Michigan) (B.S., Physics, 1971), Dartmouth Medical College (MS, Neurophysiology, 1973), and the Massachusetts Institute of Technology (Ph.D, Applied Mathematics, 1977).

Since 1977, he has been a member of the faculty at [Brown University](/source/Brown_University), where he has worked in the [Pattern Theory](/source/Pattern_Theory) group, and is currently the James Manning Professor of Applied Mathematics. He has received many honors and awards, including selection as a Presidential Young Investigator and as an ISI Highly Cited researcher. He is an elected member of the [International Statistical Institute](/source/International_Statistical_Institute), and a fellow of the Institute of Mathematical Statistics and of the American Mathematical Society.[7] He was elected to the US [National Academy of Sciences](/source/National_Academy_of_Sciences) in 2011. In 2024, he received an Engineering, Science & Technology Emmy Award for his role in the development of the DRS™Nova Film and Video Restoration Software.[8]

## Work

Geman's scientific contributions span work in probabilistic and statistical approaches to [artificial intelligence](/source/Artificial_intelligence), [Markov random fields](/source/Markov_random_fields), [Markov chain Monte Carlo](/source/Markov_chain_Monte_Carlo) (MCMC) methods, [nonparametric inference](/source/Sieve_estimator), random matrices, random dynamical systems, neural networks, neurophysiology, financial markets, and natural image statistics. Particularly notable works include: the development of the [Gibbs sampler](/source/Gibbs_sampling), proof of convergence of [simulated annealing](/source/Simulated_annealing),[9][10] foundational contributions to the [Markov random field](/source/Markov_random_field) ("graphical model") approach to inference in vision and machine learning,[3][11] and work on the compositional foundations of vision and cognition.[12][13]

## Notes

1. Thomas P. Ryan & William H. Woodall (2005). "The Most-Cited Statistical Papers". *Journal of Applied Statistics*. **32** (5): 461–474. [Bibcode:2005JApSt..32..461R](https://ui.adsabs.harvard.edu/abs/2005JApSt..32..461R). [doi:10.1080/02664760500079373](https://doi.org/10.1080/02664760500079373). [S2CID 109615204](https://api.semanticscholar.org/CorpusID:109615204)

1. S. Kotz & N.L. Johnson (1997). *Breakthroughs in Statistics, Volume III*. New York, NY: Springer Verlag.

1. [\[Wikipedia\] List of important publications in computer science.](/source/List_of_important_publications_in_computer_science)

1. Sharon Bertsch Mcgrayne (2011). [*The theory that would not die*](https://archive.org/details/theorythatwouldn0000mcgr). New York and London: Yale University Press.

1. S. Geman & D. Geman (1984). "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images". *[IEEE Transactions on Pattern Analysis and Machine Intelligence](/source/IEEE_Transactions_on_Pattern_Analysis_and_Machine_Intelligence)*. **6** (6): 721–741. [Bibcode:1984ITPAM...6..721G](https://ui.adsabs.harvard.edu/abs/1984ITPAM...6..721G). [doi:10.1109/TPAMI.1984.4767596](https://doi.org/10.1109/TPAMI.1984.4767596). [PMID 22499653](https://pubmed.ncbi.nlm.nih.gov/22499653). [S2CID 5837272](https://api.semanticscholar.org/CorpusID:5837272)

1. Google Scholar: [Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration](https://scholar.google.com/scholar?cites=12922359299324378570&as_sdt=20000005&sciodt=0,21&hl=en).

1. [List of Fellows of the American Mathematical Society](http://www.ams.org/profession/fellows-list), retrieved 2013-08-27.

1. [\[1\]](https://appliedmath.brown.edu/news/2024-10-24/congratulations-emmy-award-winner-stuart-geman)

1. P.J. van Laarhoven & E.H. Aarts (1987). *Simulated annealing: Theory and applications*. Netherlands: Kluwer. [Bibcode:1987sata.book.....L](https://ui.adsabs.harvard.edu/abs/1987sata.book.....L)

1. P. Salamon; P. Sibani; R. Frost (2002). *Facts, Conjectures, and Improvements for Simulated Annealing*. Philadelphia, PA: Society for Industrial and Applied Mathematics.

1. C. Bishop (2006). *Pattern recognition and machine learning*. New York: Springer.

1. N. Chater; J.B. Tenenbaum; & A. Yuille (2005). ["Probabilistic models of cognition: Conceptual foundations"](https://escholarship.org/content/qt1g84199d/qt1g84199d.pdf?t=lrgwg6). *Trends in Cognitive Sciences*. **10** (7): 287–291. [doi:10.1016/j.tics.2006.05.007](https://doi.org/10.1016/j.tics.2006.05.007). [PMID 16807064](https://pubmed.ncbi.nlm.nih.gov/16807064). [S2CID 7547910](https://api.semanticscholar.org/CorpusID:7547910)

1. B. Ommer & J.M. Buhmann (2010). "Learning the compositional structure of visual object categories for recognition". *IEEE Transactions on Pattern Analysis and Machine Intelligence*. **32** (3): 501–516. [CiteSeerX 10.1.1.297.2474](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.2474). [doi:10.1109/tpami.2009.22](https://doi.org/10.1109/tpami.2009.22). [PMID 20075474](https://pubmed.ncbi.nlm.nih.gov/20075474). [S2CID 11002928](https://api.semanticscholar.org/CorpusID:11002928)

---
Adapted from the Wikipedia article [Stuart Geman](https://en.wikipedia.org/wiki/Stuart_Geman) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/Stuart_Geman?action=history)). Available under [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). Changes may have been made.