Faculty

Donald Geman

Professor

Research Interests

  • Image Analysis
  • Statistical Learning
  • Bioinformatics

Donald Geman is a professor in the Department of Applied Mathematics and Statistics and a member of the National Academy of Sciences. His research focuses on image analysis, statistical learning, and bioinformatics. He is a member of the Center for Imaging Science and the Institute for Computational Medicine. He also is affiliated with Ecole Normale Supérieure and INRIA. He earned his doctorate at Northwestern University in 1970.

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).
  • Afsari B, Fertig EJ, Geman D, Marchionni L (2015).  SwitchBox: An R package for k-Top Scoring Pairs classifier development.  Bioinformatics.  31(2).
  • Marchionni L, Geman D (2015).  Predicting cancer phenotypes with mechanism-driven multi-omics data integration.  Cancer Research.  75(15 Supplement).  3754-3754.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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, 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.
  • Afsari B, Geman D, Fertig EJ (2014).  Learning Dysregulated Pathways in Cancers from Differential Variability Analysis.  Cancer informatics.  13(Suppl 5).  61.
  • 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.
  • 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.
  • Geman D, Ochs M, Price ND, Tomasetti C, Younes L (2014).  An argument for mechanism-based statistical inference in cancer.  Human genetics.  1-17.
  • 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.
  • 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).
  • 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).
  • Geman D (2010).  STATISTICAL LEARNING IN COMPUTATIONAL BIOLOGY.  DESCRIPTIFS DES COURS DU M2 MVA MATH EMATIQUES VISION APPRENTISSAGE 2009-2010.
  • 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, Sung J, Geman D, Price ND (2010).  Relative expression analysis for molecular cancer diagnosis and prognosis.  Technology in cancer research & treatment.  9(2).  149.
  • 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.
  • Lin X, Afsari B, Marchionni L, Cope L, Parmigiani G, Naiman D, Geman D (2009).  BMC Bioinformatics.  10.  256.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Fleuret F, Geman D (2008).  Stationary features and cat detection.  Journal of Machine Learning Research.  9(2549-2578).  16.
  • 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.
  • 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).
  • 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).
  • 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.
  • Koloydenko A, Geman D (2005).  Ordinal coding of image microstructure.  Eurandom.
  • 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.
  • Geman D, Koloydenko A (2005).  Spatial Adaptation in Coding Microstructure of Natural Images..
  • 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.
  • 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, 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 (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 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).
  • 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.
  • Geman D, Moquet R (2000).  Q & a models for interactive search.  Preprint, December.
  • cois Fleuret F, Geman D (2000).  Coarse-to-Fine Face Detection.  Citeseer.
  • 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).  Interactive image retrieval by mental matching.  Proceedings of the 8th ACM international workshop on Multimedia information retrieval.  1-2.
  • 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.  2.  1877-1884.
  • 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.
  • 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.
  • 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.
  • Fleuret F, Geman D (2002).  Fast face detection with precise pose estimation.  Pattern Recognition, 2002. Proceedings. 16th International Conference on.  1.  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.  3.  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 (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
Back to top