Faculty

Donald Geman

Professor

Research Interests

  • Image Analysis
  • Statistical Learning
  • Bioinformatics
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).
  • 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).
  • 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.
  • 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).
  • 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).
  • Geman D, Geman H, Taleb NN (2015).  Tail risk constraints and maximum entropy.  Entropy.  17(6).  3724-3737.
  • 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.
  • Marchionni L, Geman D (2015).  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, Ochs M, Price ND, Tomasetti C, Younes E (2014).  An argument for mechanism-based statistical inference in cancer..  Human genetics.
  • Afsari B, Geman D, Fertig EJ (2014).  Learning Dysregulated Pathways in Cancers from Differential Variability Analysis.  Cancer informatics.  13(Suppl 5).  61.
  • Geman D, Ochs M, Price ND, Tomasetti C, Younes L (2014).  An argument for mechanism-based statistical inference in cancer.  Human genetics.  1-17.
  • Sfar AR, Boujemaa N, Geman D (2014).  Confidence Sets for Fine-Grained Categorization and Plant Species Identification.  International Journal of Computer Vision.  1-21.
  • 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.
  • Afsari B, Fertig EJ, Geman D, Marchionni L (2014).  switchBox: an R package for k-Top Scoring Pairs classifier development.  Bioinformatics.  btu622.
  • 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.
  • 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).
  • 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.
  • Marchionni L, Afsari B, Geman D, Leek JT (2013).  A simple and reproducible breast cancer prognostic test.  BMC genomics.  14(1).  336.
  • 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.
  • Simcha D, Price ND, Geman D (2012).  The limits of de novo DNA motif discovery.  PloS one.  7(11).  e47836.
  • Winslow RL, Trayanova N, Geman D, Miller M (2012).  Computational medicine: translating models to clinical care.  Science translational medicine.  4(158).  158rv11-158rv11.
  • 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).
  • 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.
  • Slama P, Geman D (2011).  Identification of family-determining residues in PHD fingers.  Nucleic acids research.  39(5).  1666-1679.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Lin X, Afsari B, Marchionni L, Cope L, Parmigiani G, Naiman D, Geman D (2009).  BMC Bioinformatics.  10.  256.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Fleuret F, Geman D (2008).  Stationary features and cat detection.  Journal of Machine Learning Research.  9(2549-2578).  16.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • Sahbi H, Geman D (2006).  A hierarchy of support vector machines for pattern detection.  The Journal of Machine Learning Research.  7.  2087-2123.
  • 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).
  • 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).
  • Blanchard G, Geman D (2005).  EPrint Removed.  Annals of Statistics.  33(3).  1155-1202.
  • Blanchard G, Geman D (2005).  Hierarchical testing designs for pattern recognition.  Annals of Statistics.  1155-1202.
  • 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.
  • 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.
  • Koloydenko A, Geman D (2005).  Ordinal coding of image microstructure.  Eurandom.
  • Geman D, Koloydenko A (2005).  Spatial Adaptation in Coding Microstructure of Natural Images..
  • 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).
  • 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 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.
  • 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.
  • 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).
  • Fleuret F, Geman D (2001).  Coarse-to-fine face detection.  International Journal of computer vision.  41(1-2).  85-107.
  • Geman D, Jedynak B (2001).  Model-based classification trees.  IEEE Transactions on Information Theory.  47(3).  1075-1082.
  • cois Fleuret F, Geman D (2000).  Coarse-to-Fine Face Detection.  Citeseer.
  • Geman D, Moquet R (2000).  A stochastic feedback model for image retrieval.  Proc. RFIA.  3.  173-180.
  • 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 (1999).  Statistical Learning and Coarse-to-fine Object Detection.  COMPUTING SCIENCE AND STATISTICS.  60-60.
  • 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.
  • Geman D, Koloydenko A (1999).  Invariant statistics and coding of natural microimages.  IEEE Workshop on Statistical and Computational Theories of Vision.
  • cois Fleuret F, Geman D (1999).  Graded Learning for Object Detection.  Citeseer.
  • 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).
  • 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 (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, 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, 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
  • 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.
  • 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.
  • 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.
  • Geman D (2006).  In Search of a Unifying Theory for Image Interpretation.  Information Theory, 2006 IEEE International Symposium on.  xliv-xliv.
  • Geman D (2006).  Interactive image retrieval by mental matching.  Proceedings of the 8th ACM international workshop on Multimedia information retrieval.  1-2.
  • Gangaputra S, Geman D (2006).  A design principle for coarse-to-fine classification.  Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on.  2.  1877-1884.
  • 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.
  • 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.
  • 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.
  • Fan X, Geman D (2004).  Hierarchical object indexing and sequential learning.  Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on.  3.  65-68.
  • 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.  2.  II-616.
  • Sahbi H, Geman D, Boujemaa N (2002).  Face detection using coarse-to-fine support vector classifiers.  Image Processing. 2002. Proceedings. 2002 International Conference on.  3.  925-928.
  • Fleuret F, Geman D (2002).  Fast face detection with precise pose estimation.  Pattern Recognition, 2002. Proceedings. 16th International Conference on.  1.  235-238.
  • 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 (1991).  Remarks on Hard Modeling vs. Image Processing, Circumstellar Disks and Model Validation.  The Restoration of HST Images and Spectra.  1.  74.
  • 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.  4.  2473-2477.
  • 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.  17(1).  34-35.
  • Geman D, HOROWITZ J (1971).  PALM PROBABILITIES AND ADDITIVE FUNCTIONALS. 1. PRELIMINARY REPORT.  NOTICES OF THE AMERICAN MATHEMATICAL SOCIETY.  18(6).  972.
  • Geman D (1971).  VARIANCE OF NUMBER OF ZEROS OF A STATIONARY GAUSSIAN PROCESS.  ANNALS OF MATHEMATICAL STATISTICS.  42(5).  1794.
  • "A visual Turing test for computer vision systems", ONR Workshop.  Durham, North Carolina.  October 25, 2015
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