Donald Geman

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Donald Geman
DonaldGeman.jpg
Donald Geman (right), Fall 1983, Paris
Born (1943-09-20) September 20, 1943 (age 70)
Chicago, Illinois, USA
Residence United States, France
Nationality American
Fields Mathematics
Statistics
Institutions University of Massachusetts
Johns Hopkins University
École Normale Supérieure de Cachan
Alma mater Columbia University
University of Illinois at Urbana-Champaign
Northwestern University
Doctoral advisor Michael Marcus
Notable awards ISI highly cited researcher

Donald 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 14K citations according to Google Scholar, as of July 2013 [2]). He is a Professor at the Johns Hopkins University and simultaneously a visiting professor at École Normale Supérieure de Cachan.

Biography[edit]

Geman was born in Chicago in 1943. He graduated from the University of Illinois at Urbana-Champaign in 1965 with a B.A. degree in English Literature and from Northwestern University in 1970 with a Ph.D. in Mathematics. 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 Fellow of both the Institute of Mathematical Statistics and the Society for Industrial and Applied Mathematics.

Work[edit]

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.[3] In 1984 with his brother Stuart, he published a milestone paper which is still today one of the most cited papers [4] 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,[5][6] in collaboration with Y. Amit, he introduced the notion for randomized decision trees,[7][8] 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[9] 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.[10][11]

References[edit]

  1. ^ S. Geman and 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. 
  2. ^ Google Scholar: Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration.
  3. ^ D. Geman and J. Horowitz (1980). "Occupation Densities". Annals of Probability 8 (1): 1. doi:10.1214/aop/1176994824. 
  4. ^ 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
  5. ^ 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.
  6. ^ Y. Amit and D. Geman (1997). "Shape Quantization and Recognition with Randomized Trees". Neural Computation 9: pp. 1545–1588. doi:10.1162/neco.1997.9.7.1545. 
  7. ^ 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.
  8. ^ 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).
  9. ^ F. Fleuret and D. Geman (2001). "Coarse-to-Fine Face Detection". International Journal of Computer Vision. 
  10. ^ D. Geman, C. d'Avignon, D. Naiman and R. Winslow (2004). "Classifying gene expression profiles from pairwise mRNA comparisons". Statistical Applications in Genetics and Molecular Biology 3: Article19. doi:10.2202/1544-6115.1071. PMC 1989150. PMID 16646797. 
  11. ^ A-C Tan, D. Naiman, L. Xu, R. Winslow and D. Geman (2005). "Simple decision rules for classifying human cancers from gene expression profiles". Bioinformatics. doi:10.1093/bioinformatics/bti631. 

External links[edit]