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Donald Geman

Donald J. Geman
Donald Geman (right), Fall 1983, Paris
Born (1943-09-20) September 20, 1943 (age 81)
Chicago, Illinois, United States
Alma materColumbia University
University of Illinois Urbana-Champaign
Northwestern University
RelativesStuart Geman (brother)
AwardsISI highly cited researcher
Scientific career
FieldsMathematics
Statistics
InstitutionsUniversity of Massachusetts
Johns Hopkins University
École Normale Supérieure de Cachan
Doctoral advisorMichael Marcus

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).[2] He is a professor at the Johns Hopkins University and simultaneously a visiting professor at École Normale Supérieure de Cachan.

Biography

Geman was born in Chicago in 1943. He graduated from the University of Illinois Urbana-Champaign in 1965 with a B.A. degree in English Literature and from Northwestern University in 1970 with a Ph.D. in mathematics.[3] 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 Johns Hopkins University. He has also been a visiting professor at the École Normale Supérieure de Cachan since 2001. He is a member of the National Academy of Sciences, and Fellow of the Institute of Mathematical Statistics and the Society for Industrial and Applied Mathematics.

Work

D. Geman and J. Horowitz published a series of papers during the late 1970s 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.[4] In 1984 with his brother Stuart, he published a milestone paper which is still today one of the most cited papers[5] 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 a rare tour de force in this rapidly evolving field. In another milestone paper,[6][7] in collaboration with Y. Amit, he introduced the notion for randomized decision trees,[8][9] 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[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.[11][12]

References

  1. ^ S. Geman; D. Geman (1984). "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images". IEEE Transactions on Pattern Analysis and Machine Intelligence. 6 (6): 721–741. doi:10.1109/TPAMI.1984.4767596. PMID 22499653. S2CID 5837272.
  2. ^ Google Scholar: Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration.
  3. ^ "Donald Geman elected to NAS". Institute of Mathematical Statistics. 18 May 2015. Retrieved 5 June 2024.
  4. ^ D. Geman; J. Horowitz (1980). "Occupation Densities". Annals of Probability. 8 (1): 1–67. doi:10.1214/aop/1176994824.
  5. ^ ISI Highly Cited:Donald Geman http://hcr3.isiknowledge.com/author.cgi?&link1=Search&link2=Search%20Results&AuthLastName=geman&AuthFirstName=&AuthMiddleName=&AuthMailnstName=&CountryID=-1&DisciplineID=0&id=519 Archived 2007-05-19 at the Wayback Machine
  6. ^ Y. Amit and D. Geman, "Randomized inquiries about shape; an application to handwritten digit recognition," Technical Report 401, Department of Statistics, University of Chicago, IL, 1994.
  7. ^ Y. Amit; D. Geman (1997). "Shape Quantization and Recognition with Randomized Trees". Neural Computation. 9 (7): 1545–1588. CiteSeerX 10.1.1.57.6069. doi:10.1162/neco.1997.9.7.1545. S2CID 12470146.
  8. ^ Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning Found. Trends. Comput. Graph. Vis., Vol. 7, Nos. 2–3 (2011) 81–227. (February 2012), pp. 81-227,doi:10.1561/0600000035 by Antonio Criminisi, Jamie Shotton and Ender Konukoglu.
  9. ^ Decision Forests for Computer Vision and Medical Image Analysis. Editors: A. Criminisi, J. Shotton. Springer, 2013. ISBN 978-1-4471-4928-6 (Print) 978-1-4471-4929-3 (Online).
  10. ^ F. Fleuret; D. Geman (2001). "Coarse-to-Fine Face Detection". International Journal of Computer Vision. 41: 85–107. doi:10.1023/a:1011113216584. S2CID 6754141.
  11. ^ D. Geman; C. d'Avignon; D. Naiman; R. Winslow (2004). "Classifying gene expression profiles from pairwise mRNA comparisons". Statistical Applications in Genetics and Molecular Biology. 3: 1–19. doi:10.2202/1544-6115.1071. PMC 1989150. PMID 16646797.
  12. ^ A-C Tan; D. Naiman; L. Xu; R. Winslow; D. Geman (2005). "Simple decision rules for classifying human cancers from gene expression profiles". Bioinformatics. 21 (20): 3896–3904. doi:10.1093/bioinformatics/bti631. PMC 1987374. PMID 16105897.
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