Faculty

Donald Geman

Professor

Research Interests

  • Image Analysis
  • Statistical Learning
  • Bioinformatics

Donald Geman, a professor of applied mathematics and statistics, works at the foundation of widely used methods in machine vision, machine learning and transcription-based cancer phenotyping. He is a member of Johns Hopkins’ Center for Imaging Science and its Institute for Computational Medicine. He also is a visiting professor with École Normale Supérieure de Cachan and INRIA in France.

Geman is recognized for his work in stochastic processes, image analysis, machine learning and computational medicine. He is best known for his work on occupation densities for random functions, Markov random fields for image processing, and for introducing the Gibbs Sampler algorithm for Bayesian computation and randomized decision trees for classification.

He has made seminal contributions across multiple fields in applied mathematical sciences. His idea of randomized query selection (aka “random forests”) has become one of the most widely used classification methods in computational vision and biology, and the computational basis of Microsoft’s Kinect vision system. Geman also pioneered a “twenty questions” approach to pattern recognition that is the basis for diverse systems like road tracking and face detection. He proposed a highly novel method for predicting cancer phenotypes, including diagnosis, prognosis, and prediction of treatment response, from messenger RNA (mRNA) concentrations.

In work published in the Proceedings of the National Academy of Sciences in 2018, Geman and colleagues described a method to simplify complex biomolecular data about tumors, in principle making it easier to prescribe appropriate treatments for specific patients. The computational strategy transforms highly complex information into a simplified format that emphasizes patient-to-patient variation in the molecular signatures of cancer cells. Geman’s team found a way to greatly simplify the data on tens of thousands of molecular states by converting these data to binary labels, indicating whether a measurement falls within or beyond healthy levels.

His current projects in computational biology are driven by the objective of tailoring cancer treatment to an individual molecular profile by extracting information from gigantic amounts of data about normal functioning and abnormal perturbations in biological networks. These data are accumulated by new sequencing technologies and enable his group to learn algorithms to predict disease phenotypes, progression and treatment response for individuals.

He is a member of the National Academy of Sciences and a Fellow of the Society for Industrial and Applied Mathematics (SIAM) and the Institute of Mathematical Statistics (IMS).

Geman earned a BA in English literature from Northern Illinois University in 1965 and a PhD in mathematics from Northwestern University in 1970. He worked for the University of Massachusetts’ Department of Mathematics and Statistics from 1970 to 2001, before joining the faculty of the Whiting School of Engineering.

Education
  • Ph.D. 1970, Northwstrn University*
Experience
  • 2012 - Present:  Chair, Board of Review, Academic Council
  • 2007 - 2007:  Founder, BME/AMS
  • 2006 - 2007:  Chair, SIAM Activity Group, Imaging Science
Research Areas
  • COMPUTER vision
  • Computational Biology
  • Computational Molecular Medicine
  • Statistical Learning
Awards
  • 2015:  Member
  • 2011:  Fellow - SIAM (Society for Industrial and Applied Mathematicians)
  • 1998:  Fellow - IMS (Institute of Mathematical Statistics)
Journal Articles
  • Geman D, Geman S (2016).  Opinion: Science in the age of selfies.  Proceedings of the National Academy of Sciences.  113(34).  9384-9387.
  • Marchionni L, Geman D (2015).  Abstract 3754: Predicting cancer phenotypes with mechanism-driven multi-omics data integration.  Cancer Research.  75(15 Supplement).  3754-3754.
  • Geman D, Ochs M, Price ND, Tomasetti C, Younes L (2015).  An argument for mechanism-based statistical inference in cancer.  Human Genetics.  134(5).  479-495.
  • Geman D, Geman S, Hallonquist N, Younes L (2015).  Visual Turing test for computer vision systems.  Proceedings of the National Academy of Sciences of the United States of America.  112(12).  3618-3623.
  • Afsari B, Fertig EJ, Geman D, Marchionni L (2015).  SwitchBox: An R package for k-Top Scoring Pairs classifier development.  Bioinformatics.  31(2).  273-274.
  • Afsari B, Fertig EJ, Geman D, Marchionni L (2015).  switchBox: an R package for k-Top Scoring Pairs classifier development.  Bioinformatics.  31(2).  273-274.
  • Sfar AR, Boujemaa N, Geman D (2015).  Confidence sets for fine-grained categorization and plant species identification.  International Journal of Computer Vision.  111(3).  255-275.
  • Geman D, Geman H, Taleb NN (2015).  Tail risk constraints and maximum entropy.  Entropy.  17(6).  3724-3737.
  • Geman D, Geman S, Hallonquist N, Younes L (2015).  Visual turing test for computer vision systems.  Proceedings of the National Academy of Sciences.  112(12).  3618-3623.
  • Marchionni L, Geman D (2015).  Predicting cancer phenotypes with mechanism-driven multi-omics data integration.  Cancer Research.  75(15 Supplement).  3754-3754.
  • Chang L, Geman D (2015).  Tracking cross-validated estimates of prediction error as studies accumulate.  Journal of the American Statistical Association.  110(511).  1239-1247.
  • Geman D, Ochs M, Price ND, Tomasetti C, Younes L (2015).  An argument for mechanism-based statistical inference in cancer.  Human genetics.  134(5).  479-495.
  • Geman D, Geman H, Taleb NN (2015).  Tail risk constraints and maximum entropy.  Entropy.  17(6).  3724-3737.
  • Geman D, Ochs M, Price ND, Tomasetti C, Younes E (2014).  An argument for mechanism-based statistical inference in cancer..  Human genetics.
  • Geman D, Ochs M, Price ND, Tomasetti C, Younes L (2014).  An argument for mechanism-based statistical inference in cancer.  Human genetics.  1-17.
  • Afsari B, Fertig EJ, Younes L, Geman D, Marchionni L (2014).  Hardwiring mechanism into predicting cancer phenotypes by computational learning.  Cancer Research.  74(19 Supplement).  5342-5342.
  • Sfar AR, Boujemaa N, Geman D (2014).  Confidence Sets for Fine-Grained Categorization and Plant Species Identification.  International Journal of Computer Vision.  1-21.
  • Afsari B, Geman D, Fertig EJ (2014).  Learning Dysregulated Pathways in Cancers from Differential Variability Analysis.  Cancer informatics.  13(Suppl 5).  61.
  • Afsari B, Fertig EJ, Geman D, Marchionni L (2014).  switchBox: an R package for k-Top Scoring Pairs classifier development.  Bioinformatics.  btu622.
  • Ma S, Sung J, Magis AT, Wang Y, Geman D, Price ND (2014).  Measuring the Effect of Inter-Study Variability on Estimating Prediction Error.  PloS one.  9(10).  e110840.
  • Afsari B, Neto UB, Geman D (2014).  Rank Discriminants for Predicting Phenotypes from RNA Expression.  arXiv preprint arXiv:1401.1490.
  • Simcha DM, Younes L, Aryee MJ, Geman D (2013).  Identification of direction in gene networks from expression and methylation.  BMC Systems Biology.  7.
  • Simcha DM, Younes L, Aryee MJ, Geman D (2013).  Identification of direction in gene networks from expression and methylation..  BMC systems biology.  7.  118.
  • Marchionni L, Afsari B, Geman D, Leek JT (2013).  A simple and reproducible breast cancer prognostic test.  BMC Genomics.  14(1).
  • Sánchez-Vega F, Younes L, Geman D (2013).  Learning multivariate distributions by competitive assembly of marginals..  IEEE transactions on pattern analysis and machine intelligence.  35(2).  398-410.
  • Sánchez-Vega F, Eisner J, Younes L, Geman D (2013).  Learning multivariate distributions by competitive assembly of marginals.  IEEE Transactions on Pattern Analysis and Machine Intelligence.  35(2).  398-410.
  • Marchionni L, Afsari B, Geman D, Leek JT (2013).  A simple and reproducible breast cancer prognostic test.  BMC genomics.  14(1).  336.
  • Sung J, Kim P, Ma S, Funk CC, Magis AT, Wang Y, Hood L, Geman D, Price ND (2013).  Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.  PLoS computational biology.  9(7).  e1003148.
  • Winslow RL, Trayanova N, Geman D, Miller MI (2012).  Computational medicine: Translating models to clinical care.  Science Translational Medicine.  4(158).
  • Sánchez-Vega F, Eisner J, Younes E, Geman D (2012).  Learning Multivariate Distributions by Competitive Assembly of Marginals.  IEEE Transactions on Pattern Analysis and Machine Intelligence.  99.
  • Winslow RL, Trayanova N, Geman D, Miller M (2012).  Computational medicine: translating models to clinical care.  Science translational medicine.  4(158).  158rv11-158rv11.
  • Simcha D, Price ND, Geman D (2012).  The limits of de novo DNA motif discovery.  PloS one.  7(11).  e47836.
  • Yörük E, Ochs MF, Geman D, Younes L (2011).  A comprehensive statistical model for cell signaling..  IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM.  8(3).  592-606.
  • Yörük E, Ochs MF, Geman D, Younes L (2011).  A comprehensive statistical model for cell signaling.  IEEE/ACM Transactions on Computational Biology and Bioinformatics.  8(3).  592-606.
  • Slama P, Geman D (2011).  Identification of family-determining residues in PHD fingers.  Nucleic acids research.  39(5).  1666-1679.
  • Fleuret F, Li T, Dubout C, Wampler EK, Yantis S, Geman D (2011).  Comparing machines and humans on a visual categorization test.  Proceedings of the National Academy of Sciences.  108(43).  17621-17625.
  • Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA (2010).  Tackling the widespread and critical impact of batch effects in high-throughput data.  Nature Reviews Genetics.  11(10).  733-739.
  • Geman D (2010).  STATISTICAL LEARNING IN COMPUTATIONAL BIOLOGY.  DESCRIPTIFS DES COURS DU M2 MVA MATH EMATIQUES VISION APPRENTISSAGE 2009-2010.
  • Eddy JA, Sung J, Geman D, Price ND (2010).  Relative expression analysis for molecular cancer diagnosis and prognosis.  Technology in cancer research & treatment.  9(2).  149.
  • Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson W, Geman D, Baggerly K, Irizarry RA (2010).  Tackling the widespread and critical impact of batch effects in high-throughput data.  Nature Reviews Genetics.  11(10).  733-739.
  • Eddy JA, Hood L, Price ND, Geman D (2010).  Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).  PLoS computational biology.  6(5).  e1000792.
  • Lin X, Afsari B, Marchionni L, Cope L, Parmigiani G, Naiman D, Geman D (2009).  The ordering of expression among a few genes can provide simple cancer biomarkers and signal BRCA1 mutations.  BMC Bioinformatics.  10.  256.
  • Edelman LB, Toia G, Geman D, Zhang W, Price ND (2009).  Two-transcript gene expression classifiers in the diagnosis and prognosis of human diseases.  BMC genomics.  10(1).  583.
  • Ferecatu M, Geman D (2009).  A statistical framework for image category search from a mental picture.  Pattern Analysis and Machine Intelligence, IEEE Transactions on.  31(6).  1087-1101.
  • Lin X, Afsari B, Marchionni L, Cope L, Parmigiani G, Naiman D, Geman D (2009).  The ordering of expression among a few genes can provide simple cancer biomarkers and signal BRCA1 mutations.  BMC bioinformatics.  10(1).  256.
  • Lin X, Afsari B, Marchionni L, Cope L, Parmigiani G, Naiman D, Geman D (2009).  BMC Bioinformatics.  10.  256.
  • Geman D, Afsari B, Tan AC, Naiman DQ (2008).  Microarray classification from several twogene expression comparisons.  Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008.  583-585.
  • Xu L, Tan AC, Winslow RL, Geman D (2008).  Merging microarray data from separate breast cancer studies provides a robust prognostic test.  BMC Bioinformatics.  9.
  • Fleuret F, Geman D (2008).  Stationary features and cat detection.  Journal of Machine Learning Research.  9(2549-2578).  16.
  • Xu L, Tan AC, Winslow RL, Geman D (2008).  Merging microarray data from separate breast cancer studies provides a robust prognostic test.  Bmc Bioinformatics.  9(1).  125.
  • Wang J, Geman D, Luo J, Gray R (2008).  SPECIAL SECTION ON REAL-WORLD IMAGE ANNOTATION AND RETRIEVAL-Real-World Image Annotation and Retrieval: An Introduction to the Special Section.  IEEE Transactions on Pattern Analysis and Machine Intelligence.  30(11).  1873.
  • Eddy JA, Geman D, Price ND (2008).  Pathway Expression Rank Analysis (p-XRAY): A Novel Tool for Gene Set Expression Analysis.  The 2008 Annual Meeting.
  • Wang JZ, Geman D, Luo J, Gray RM (2008).  Real-world image annotation and retrieval: an introduction to the special section.  Pattern Analysis and Machine Intelligence, IEEE Transactions on.  30(11).  1873-1876.
  • Xu L, Geman D, Winslow RL (2007).  Large-scale integration of cancer microarray data identifies a robust common cancer signature.  BMC Bioinformatics.  8.
  • Anderson TJ, Tchernyshyov I, Diez R, Cole RN, Geman D, Dang CV, Winslow RL (2007).  Discovering robust protein biomarkers for disease from relative expression reversals in 2-D DIGE data.  Proteomics.  7(8).  1197-1207.
  • Anderson TJ, Tchernyshyov I, Diez R, Cole RN, Geman D, Dang CV, Winslow RL (2007).  Discovering robust protein biomarkers for disease from relative expression reversals in 2-D DIGE data..  Proteomics.  7(8).  1197-1207.
  • Xu L, Geman D, Winslow RL (2007).  Large-scale integration of cancer microarray data identifies a robust common cancer signature.  BMC bioinformatics.  8(1).  275.
  • Sahbi H, Geman D (2006).  A hierarchy of support vector machines for pattern detection.  The Journal of Machine Learning Research.  7.  2087-2123.
  • Tan AC, Naiman DQ, Xu L, Winslow RL, Geman D (2005).  Simple decision rules for classifying human cancers from gene expression profiles.  Bioinformatics.  21(20).  3896-3904.
  • Xu L, Tan AC, Naiman DQ, Geman D, Winslow RL (2005).  Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data.  Bioinformatics.  21(20).  3905-3911.
  • Blanchard G, Geman D (2005).  Hierarchical testing designs for pattern recognition.  Annals of Statistics.  1155-1202.
  • Geman D, Koloydenko A (2005).  Spatial Adaptation in Coding Microstructure of Natural Images..
  • Blanchard G, Geman D (2005).  EPrint Removed.  Annals of Statistics.  33(3).  1155-1202.
  • Xu L, Tan AC, Naiman D, Geman D, Winslow RL (2005).  Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data.  Bioinformatics.  21(20).  3905-3911.
  • Tan AC, Naiman D, Xu L, Winslow RL, Geman D (2005).  Simple decision rules for classifying human cancers from gene expression profiles.  Bioinformatics.  21(20).  3896-3904.
  • Koloydenko A, Geman D (2005).  Ordinal coding of image microstructure.  Eurandom.
  • Geman D, D'Avignon C, Naiman DQ, Winslow RL (2004).  Classifying gene expression profiles from pairwise mRNA comparisons.  Statistical Applications in Genetics and Molecular Biology.  3(1).
  • Geman D (2004).  Study around path classification in a ternary tree.
  • Amit Y, Geman D, Fan X (2004).  A coarse-to-fine strategy for multiclass shape detection.  Pattern Analysis and Machine Intelligence, IEEE Transactions on.  26(12).  1606-1621.
  • Geman D, d’Avignon C, Naiman D, Winslow R, Zeboulon A (2004).  Gene expression comparisons for class prediction in cancer studies.  Proceedings 36'th Symposium on the Interface: Computing Science and Statistics.
  • Geman D, d’Avignon C, Naiman D, Winslow RL (2004).  Classifying gene expression profiles from pairwise mRNA comparisons.  Statistical applications in genetics and molecular biology.  3(1).
  • d’Avignon C, Geman D (2003).  Tree-structured neural decoding.  The Journal of Machine Learning Research.  4.  743-754.
  • Geman D (2003).  Mathematical Sciences 550: 437 Information, Statistics and Perception.
  • Geman D, Jedynak B (2001).  Model-based classification trees.  IEEE Transactions on Information Theory.  47(3).  1075-1082.
  • Geman D, Jedynak B (2001).  Model-based classification trees.  IEEE Transactions on Information Theory.  47(3).  1075-1082.
  • Fleuret F, Geman D (2001).  Coarse-to-fine face detection.  International Journal of computer vision.  41(1-2).  85-107.
  • Geman D, Moquet R (2000).  A stochastic feedback model for image retrieval.  Proc. RFIA.  3.  173-180.
  • cois Fleuret F, Geman D (2000).  Coarse-to-Fine Face Detection.  Citeseer.
  • Geman D, Moquet R (2000).  Q & a models for interactive search.  Preprint, December.
  • Fleuret F, Geman D (1999).  Graded learning for object detection.  Proc. IEEE Workshop Statistical and Computational Theories of Vision.  544-549.
  • Geman D, Koloydenko A (1999).  Invariant statistics and coding of natural microimages.  IEEE Workshop on Statistical and Computational Theories of Vision.
  • Fleuret F, Geman D (1999).  Coarse-to-fine visual selection.  Citeseer.
  • cois Fleuret F, Geman D (1999).  Coarse-to-Fine Visual Selection.  Citeseer.
  • Amit Y, Geman D (1999).  A computational model for visual selection.  Neural computation.  11(7).  1691-1715.
  • cois Fleuret F, Geman D (1999).  Graded Learning for Object Detection.  Citeseer.
  • Geman D (1999).  Statistical Learning and Coarse-to-fine Object Detection.  COMPUTING SCIENCE AND STATISTICS.  60-60.
  • Amit Y, Geman D (1998).  Discussion: Arcing Classifiers.  Annals of Statistics.  833-837.
  • Amit Y, Geman D, Wilder K (1997).  Joint induction of shape features and tree classifiers.  Pattern Analysis and Machine Intelligence, IEEE Transactions on.  19(11).  1300-1305.
  • Amit Y, Geman D, Wilder K (1997).  Joint induction of shape features and tree classifiers.  Citeseer.
  • Amit Y, Geman D (1997).  Shape quantization and recognition with randomized trees.  Neural computation.  9(7).  1545-1588.
  • Geman D, Jedynak B (1996).  An active testing model for tracking roads in satellite images.  IEEE Transactions on Pattern Analysis and Machine Intelligence.  18(1).  1-14.
  • Geman D, Jedynak B (1996).  An active testing model for tracking roads in satellite images.  Pattern Analysis and Machine Intelligence, IEEE Transactions on.  18(1).  1-14.
  • Geman D (1996).  Wiley & Sons, 1989. 1181 C. Goad," Special purpose automatic programming for three-dimensional model-based vision," Proc. ARPA Image Understand-ing Workshop, pp. 94-104, 1983. 1191 C. Graffigne and I. Herlin," Mod6lisation de reseaux pour.  IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE.  18(1).
  • Geman D, Yang C (1995).  Nonlinear image recovery with half-quadratic regularization.  Image Processing, IEEE Transactions on.  4(7).  932-946.
  • Geman D, Jedynak B, others (1993).  Shape recognition and twenty questions.
  • Geman D, Geman S (1993).  Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images*.  Journal of Applied Statistics.  20(5-6).  25-62.
  • Geman D, Reynolds G (1992).  Constrained restoration and the recovery of discontinuities.  IEEE Transactions on pattern analysis and machine intelligence.  14(3).  367-383.
  • Geman D, Geman S, McClure DE (1992).  A nonlinear filter for film restoration and other problems in image processing.  CVGIP: Graphical models and image processing.  54(4).  281-289.
  • Geman D, GEMAN S (1992).  Reprinted with permission from IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. PAMI-6 (6), pp. 721-741 (November 1984). 1984 IEEE..  Selected papers on digital image restoration.  47.  263.
  • Geman D, Gidas B (1991).  Image analysis and computer vision.  Spatial statistics and digital image analysis.  9-36.
  • Dong P, Geman D, Geman S, Graffigne C (1990).  Boundary detection by constrained optimization.  Pattern Analysis and Machine Intelligence, IEEE Transactions on.  12(7).  609-628.
  • Geman D, Grenander U, Piccioni M, Presutti E (1990).  Fees and registration.  Signal Processing.  20.  189-191.
  • Branch B, Geman D (1988).  The Valuation of Stochastic Cash Flows.  Quarterly Journal of Business and Economics.  148-178.
  • Geman D (1987).  Stochastic model for boundary detection.  Image and Vision Computing.  5(2).  61-65.
  • Geman D (1985).  Bayesian image analysis by adaptive annealing.  IEEE Transactions on Geoscience and Remote Sensing.  1.  269-276.
  • Geman D, Geman S (1984).  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images.  Pattern Analysis and Machine Intelligence, IEEE Transactions on.  (6).  721-741.
  • Geman D, Horowitz J, Rosen J (1984).  A local time analysis of intersections of Brownian paths in the plane.  The Annals of Probability.  86-107.
  • Cristi R, Derin H, Elliott H, Geman D (1984).  Bayes smoothing algorithms for segmentation of binary images modeled by Markov random fields.  Pattern Analysis and Machine Intelligence, IEEE Transactions on.  (6).  707-720.
  • Geman D, Horowitz J, Karatzas I, Mason DM, Rosen J, Stout QF, Warren B, Yamato H (1982).  THE ANNALS.  The Annals of Statistics.  10(2).
  • Geman D, Horowitz J (1981).  Smooth perturbations of a function with a smooth local time.  Transactions of the American Mathematical Society.  267(2).  517-530.
  • Geman D (1980).  Confluent Brownian Motions.  Advances in Applied Probability.  306-306.
  • Densities O, Diaconis P, Freedman D, Geman D, Ghoussoub N, Horowitz J, Metivier M, others , Pellaumail J, Philipp W, Steele JM (1980).  Special Invited Paper.
  • Geman D, HOROWITZ J (1980).  SPECIAL INVITED PAPER.  The Annals of Probability.  8(1).  1-67.
  • Geman D, Horowitz J (1980).  Occupation densities.  The Annals of Probability.  1-67.
  • Geman D (1979).  Dispersion points for linear sets and approximate moduli for some stochastic processes.  Transactions of the American Mathematical Society.  253.  257-272.
  • Geman D, Zinn J (1978).  On the increments of multidimensional random fields.  The Annals of Probability.  151-158.
  • Cuzick J, Davisson L, Geman D, Hosoya Y, Ledrappier F, Neuhoff D, Portnoy S, Shields P, Steele JM, Zinn J (1978).  VOLUME 6 February 1978 No..  THE ANNALS OF PROBABILITY.  6(1).
  • Geman D (1977).  On the approximate local growth of multidimensional random fields.  Probability Theory and Related Fields.  38(3).  237-251.
  • Geman D (1976).  A note on the continuity of local times.  Proceedings of the American Mathematical Society.  321-326.
  • Geman D, Horowitz J, Zinn J (1976).  Recurrence of stationary sequences.  The Annals of Probability.  372-381.
  • Geman D, Gianini J, Horowitz J, Hwang J, Mitt-vl Y, Samuels SM, Tomkins IJ, Ylvisaker D, Zinn J (1976).  THE ANNALS.  The Annals of Probability.  4(3).
  • Geman D, Horowitz J (1976).  Local times for real and random functions.  Duke Mathematical Journal.  43(4).  809-828.
  • Geman D, Horowitz J (1976).  Occupation-times for functions with countable level sets and the regeneration of stationary processes.  Probability Theory and Related Fields.  35(3).  189-211.
  • Geman D, Horowitz J (1975).  RANDOM SHIFTS WHICH PRESERVE MEASURE1.  AMERICAN MATHEMATICAL SOCIETY.  49(1).
  • Geman D, Horowitz J (1975).  Polar sets and Palm measures in the theory of flows.  Transactions of the American Mathematical Society.  208.  141-159.
  • Geman D, Horowitz J (1974).  Transformations of flows by discrete random measures.  INDIANA UNIVERSITY MATHEMATICS JOURNAL.  24(4).  291-306.
  • Geman D, Horowitz J (1974).  Local times and supermartingales.  Probability Theory and Related Fields.  29(4).  273-293.
  • Geman D, Horowitz J (1973).  Occupation times for smooth stationary processes.  The Annals of Probability.  1(1).  131-137.
  • Geman D, Horowitz J (1973).  Remarks on Palm measures.  Annales de l’institut Henri Poincaré (B) Probabilités et Statistiques.  9(3).  215-232.
  • Geman D (1973).  A note on the distribution of hitting times.  The Annals of Probability.  1(5).  854-856.
  • Geman D, Horowitz J (1973).  Remarks on Palm measures.  Annales de l’institut Henri Poincaré (B) Probabilités et Statistiques.  9(3).  215-232.
  • Geman D (1972).  On the variance of the number of zeros of a stationary Gaussian process.  The Annals of Mathematical Statistics.  977-982.
Books
  • Ancona A, Geman D, Ikeda N (1991).  École d'été de probabilités de Saint-Flour XVIII-1988.  Springer.  18.
  • Ancona A, Geman D, Hennequin PL, Ikeda N (1990).  Probabilites.  Springer-Verlag.
  • Geman D, Geman SA (1987).  Relaxation and annealing with constraints.  Center for Intelligent Control Systems.
Book Chapters
  • Gangaputra S, Geman D (2006).  The trace model for object detection and tracking.  Toward Category-Level Object Recognition.  Springer.  401-420.
  • Fang Y, Geman D (2005).  Experiments in mental face retrieval.  Audio-and Video-Based Biometric Person Authentication.  Springer.  637-646.
  • Geman D (2003).  Coarse-to-fine classification and scene labeling.  Nonlinear Estimation and Classification.  Springer New York.  31-48.
  • Amit Y, Geman D, Jedynak B (1998).  Efficient focusing and face detection.  Face Recognition.  Springer Berlin Heidelberg.  157-173.
  • Geman D, Jedynak B, Jung F (1997).  Recognizing buildings in aerial images.  Automatic Extraction of Man-Made Objects from Aerial and Space Images (II).  Birkhäuser Basel.  173-182.
  • Geman D, Horowitz J, Kepner J (1994).  Computation of IRAS Fluxes via a Priori Astrometry.  Infrared Astronomy with Arrays.  Springer.  179-180.
  • Geman D (1990).  Random fields and inverse problems in imaging.  École d’été de probabilités de Saint-Flour XVIII-1988.  Springer Berlin Heidelberg.  113-193.
  • Geman D, Geman S, Graffigne C (1987).  Locating texture and object boundaries.  Pattern Recognition Theory and Applications.  Springer.  165-177.
  • Geman D, Geman S (1986).  Bayesian image analysis.  Disordered Systems and Biological Organization.  Springer.  301-319.
Other Publications
  • Amit Y, Geman D, Fan X (2003).  Computational strategies for model-based scene interpretation.  Technical report, The Johns Hopkins University, 2003. http://www. cis. jhu. edu/ xdfan/strategies. pdf.
  • Krempp S, Geman D, Amit Y (2002).  Sequential learning of reusable parts for object detection.  Technical report, CS Johns Hopkins.
  • Amit Y, Geman D (1994).  Randomized Inquiries About Shape: An Application to Handwritten Digit Recognition..  DTIC Document.
  • Geman D, Geman S, Gidas B, Grenander U, McClure DE (1991).  A Mathematical Framework for Image Analysis.  DTIC Document.
  • Bienenstock E, Geman D, Geman S, McClure DE (1990).  Development of Laser Radar ATR Algorithms: Phase 2. Military Objects.  DTIC Document.
  • Geman D, Geman S, Grenander U, McClure DE (1987).  A Unified Mathematical Approach to Image Analysis..  DTIC Document.
  • Geman D, Geman S, Grenander U, McClure DE (1987).  Image Modeling: A Mathematical Framework for Segmentation and Object Detection..  DTIC Document.
  • Cristi R, Derin H, Elliott H, Geman D (1983).  Application of the Gibbs Distribution to Image Segmentation..  MASSACHUSETTS UNIV AMHERST DEPT OF ELECTRICAL AND COMPUTER ENGINEERING.
  • Cristi R, Derin H, Elliott H, Geman D (1983).  Bayes Smoothing Algorithms for Segmentation of Images Modelled by Markov Random Fields..  DTIC Document.
  • Geman D (1977).  Singularities in the Distribution of the Increments of a Smooth Function..  DTIC Document.
Conference Proceedings
  • Rejeb Sfar A, Boujemaa N, Geman D (2013).  Identification of plants from multiple images and botanical idkeys.  Proceedings of the 3rd ACM conference on International conference on multimedia retrieval.  191-198.
  • Sfar AR, Boujemaa N, Geman D (2013).  Vantage Feature Frames For Fine-Grained Categorization.  Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on.  835-842.
  • Eddy JA, Geman D, Price ND (2009).  Relative expression analysis for identifying perturbed pathways.  Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE.  5456-5459.
  • Ferecatu M, Geman D (2007).  Interactive search for image categories by mental matching.  Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on.  1-8.
  • Wang JZ, Boujemaa N, Del Bimbo A, Geman D, Hauptmann AG, Tesic J (2006).  Diversity in multimedia information retrieval research.  Proceedings of the 8th ACM international workshop on Multimedia information retrieval.  5-12.
  • Geman D (2006).  In Search of a Unifying Theory for Image Interpretation.  Information Theory, 2006 IEEE International Symposium on.  xliv-xliv.
  • Gangaputra S, Geman D (2006).  A design principle for coarse-to-fine classification.  Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on.  1877-1884.
  • Geman D (2006).  Interactive image retrieval by mental matching.  Proceedings of the 8th ACM international workshop on Multimedia information retrieval.  1-2.
  • Fang Y, Geman D, Boujemaa N (2005).  An interactive system for mental face retrieval.  Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval.  193-200.
  • Gangaputra S, Geman D (2005).  A unified stochastic model for detecting and tracking faces.  Computer and Robot Vision, 2005. Proceedings. The 2nd Canadian Conference on.  306-313.
  • Gangputra S, Geman D (2004).  Self-normalized linear tests.  Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on.  II-616.
  • Fan X, Geman D (2004).  Hierarchical object indexing and sequential learning.  Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on.  65-68.
  • Fleuret F, Geman D (2002).  Fast face detection with precise pose estimation.  Pattern Recognition, 2002. Proceedings. 16th International Conference on.  235-238.
  • Sahbi H, Geman D, Boujemaa N (2002).  Face detection using coarse-to-fine support vector classifiers.  Image Processing. 2002. Proceedings. 2002 International Conference on.  925-928.
  • Geman D (1994).  The entropy strategy for shape recognition.  Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on.  8.
  • Geman D, Horowitz J (1993).  Searching for Circumstellar Disks with Space Telescope Observations.  Information Theory, 1993. Proceedings. 1993 IEEE International Symposium on.  133-133.
  • Geman D, Jedynak B (1991).  Detection of roads in satellite images.  Geoscience and Remote Sensing Symposium, 1991. IGARSS'91. Remote Sensing: Global Monitoring for Earth Management., International.  2473-2477.
  • Geman D (1991).  Remarks on Hard Modeling vs. Image Processing, Circumstellar Disks and Model Validation.  The Restoration of HST Images and Spectra.  74.
  • Geman D, GEMAN S, GRENANDER U, MCCLURE D (1984).  THE PARALLEL REALIZATION OF MARKOV RANDOM-FIELDS WITH APPLICATIONS TO PROBLEMS IN INFERENCE AND OPTIMIZATION.  STOCHASTIC PROCESSES AND THEIR APPLICATIONS.  (1).  34-35.
  • Geman D, HOROWITZ J (1971).  PALM PROBABILITIES AND ADDITIVE FUNCTIONALS. 1. PRELIMINARY REPORT.  NOTICES OF THE AMERICAN MATHEMATICAL SOCIETY.  (6).  972.
  • Geman D (1971).  VARIANCE OF NUMBER OF ZEROS OF A STATIONARY GAUSSIAN PROCESS.  ANNALS OF MATHEMATICAL STATISTICS.  (5).  1794.
  • "A visual Turing test for computer vision systems", ONR Workshop.  Durham, North Carolina.  October 25, 2015
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