Donald Geman

{{Infobox scientist

| name = Donald J. Geman

| image = DonaldGeman.jpg

| caption = Donald Geman (right), Fall 1983, Paris

| birth_date = {{Birth date and age|1943|9|20}}

| birth_place = Chicago, Illinois, United States

| field = Mathematics
Statistics

| work_institution = University of Massachusetts
Johns Hopkins University
École Normale Supérieure de Cachan

| alma_mater = Columbia University
University of Illinois Urbana-Champaign
Northwestern University

| doctoral_advisor = Michael Marcus

| relatives = Stuart Geman (brother)

| awards = ISI highly cited researcher

}}

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,{{Cite journal

|author1=S. Geman |author2=D. Geman | title = Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

| journal = IEEE Transactions on Pattern Analysis and Machine Intelligence

| volume = 6

| pages = 721–741

| year = 1984

| doi = 10.1109/TPAMI.1984.4767596

| issue = 6

| pmid = 22499653

|s2cid=5837272 }} in an article that became a highly cited reference in engineering (over 21K citations according to Google Scholar, as of January 2018).Google Scholar: [https://scholar.google.com/scholar?cites=12922359299324378570&as_sdt=20000005&sciodt=0,21&hl=en Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration]. 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.{{cite web |title=Donald Geman elected to NAS |url=https://imstat.org/2015/05/18/donald-geman-elected-to-nas/ |website=Institute of Mathematical Statistics |access-date=5 June 2024 |language=en |date=18 May 2015}} 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 [http://www.ams.jhu.edu/ 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.{{Cite journal

|author1=D. Geman |author2=J. Horowitz | title = Occupation Densities

| journal = Annals of Probability

| volume = 8

| year = 1980

| doi = 10.1214/aop/1176994824

| pages = 1–67

| issue = 1

| doi-access = free

}} In 1984 with his brother Stuart, he published a milestone paper which is still today one of the most cited papersISI 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 {{Webarchive|url=https://web.archive.org/web/20070519223724/http://hcr3.isiknowledge.com/author.cgi |date=2007-05-19 }} 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,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.{{Cite journal

|author1=Y. Amit |author2=D. Geman |title = Shape Quantization and Recognition with Randomized Trees

|year =1997

|journal = Neural Computation|volume = 9|issue=7 |pages=1545–1588

|doi=10.1162/neco.1997.9.7.1545|citeseerx=10.1.1.57.6069 |s2cid=12470146 }} in collaboration with Y. Amit, he introduced the notion for randomized decision trees,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.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). 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{{Cite journal

|author1=F. Fleuret |author2=D. Geman |title = Coarse-to-Fine Face Detection

|journal = International Journal of Computer Vision

|year = 2001

|doi = 10.1023/a:1011113216584

|volume=41

|pages=85–107|s2cid=6754141 }} 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.{{Cite journal

|journal = Statistical Applications in Genetics and Molecular Biology

|year = 2004

|doi = 10.2202/1544-6115.1071

|volume = 3

|pmid = 16646797

|pages = 1–19

|pmc = 1989150}}{{Cite journal

|journal = Bioinformatics

| doi = 10.1093/bioinformatics/bti631

|pmid=16105897 |pmc=1987374 |year = 2005

|volume=21

|issue=20 |pages=3896–3904}}