CURRICULUM VITAE

Contact

Affiliations

Department of Applied Mathematics and Statistics
Institute for Computational Medicine
Center for Imaging Science
Ecole Normale Superieure, Cachan, France

Research Areas

• Computational Vision
• Scene interpretation
  
• Image retrieval
• Bioinformatics
  
• Molecular cancer diagnosis
• Modeling gene and protein networks
• Biomarker discovery
  
• Statistical Learning
  
• Small-sample learning
• Hierarchical testing designs
• Twenty questions theory

Education

Professional Experience

Visiting Positions

• Department of Statistics, University of North Carolina, Chapel Hill, Fall, 1976 - Spring, 1977.
• Division of Applied Mathematics, Brown University, Providence, R.I., Spring, 1984.
• Departement de Mathematique, Universite de Paris-Sud, Orsay, France, May-June, 1986.
• Universite Clermont-Ferrand, Ecole d'Ete de Probabilite de Saint-Flour, France, August-September, 1988.
• Division of Applied Mathematics, Brown University, Providence, R.I., 1991 - 1993.
• Forschungsinstitut fur Mathematik, ETH, Zurich, Switzerland, May-July, 1993.
• Newton Institute for Mathematical Sciences, Cambridge, England, August, 1993.
• Departement de Physique, Universite de Cergy, Cergy-Pontoise, France, June, 1996.
• Institut National de Recherche en Informatique et en Automatique (INRIA), Paris, France, periodic visits, 1990-present.
• Departement de Mathematiques Appliquees, Ecole Polytechnique, Palaiseau, France, September-November, 1997-99.
• Department of Statistics, University of Chicago, April-May, 2000.
• Centre de Mathematiques et Leurs Applications, ENS-Cachan, France, June, 1997; 1999-2000; Spring, 2001-2006.

Honors

• ISI Highly Cited Researcher List (Top 100 in Engineering)
• "Distinguished University Professor" at the University of Massachusetts
• Fellow, Institute of Mathematical Statistics
• Plenary or Keynote Speaker: Annual CSNA Meeting, Amherst, MA, 1996; ICIP, Lausanne, Switzerland, 1996; Annual NESS Meeting, Univ.of Connecticut, 1999; Annual French Statistical Society Meeting, Nantes, France, 2001; Biannual EMMCVPR, Sophia Antipolis, France, 2001; MAA Meeting, Baltimore, MD, 2003; ACIVS, Brussels, Belgium, 2004; Snowbird Learning Workshop, Snowbird, UT, 2006; International Symposium on Information Theory (ISIT06), Seattle, WA, 2006; Norwegian Society for Image Processing and Pattern Recognition, Oslo, Norway, 2006; Multimedia Image Retrieval (MIR'06), Santa Barbara, 2006.

Professional Societies

• Institute of Mathematical Statistics 
• Institute of Electrical and Electronics Engineers
• American Mathematical Society
• Society for Industrial and Applied Mathematics; Chair, SIAM Imaging Science Activity Group

Doctoral Students

• Carmen Acuna, "Parameter estimation for stochastic texture models," Univ. of Mass., 1988.
• Chengda Yang, "Stochastic methods for image restoration," Univ. of Mass., 1991.
• Keith Hartt, "Bayesian estimation of surface information from radar images," Univ. of Mass., 1993.
• Bruno Jedynak, "Stochastic models and deterministic methods for finding roads in remotely-sensed images," Univ. de Paris - Sud, 1995.
• Decheng Wang, "Stochastic modeling of magnetic resonance images with applications to tissue classification," Univ. of Mass., 1996.
• Kenneth Wilder, "Decision tree algorithms for handwritten digit recognition," Univ. of Mass., 1998.
• Chunming Li, "Classification by active testing with applications to imaging and change detection," Univ. of Mass., 1998.
• Francois Fleuret, "Hierarchical face detection by statistical learning," Univ. de Paris VI, 2000.
• Alexey Koloydenko, Univ. of Mass., "Modeling natural microimage statistics," Univ. of Mass., 2000.
• Franck Jung, "Reconnaissance d'objects par focalisation et detection de changements," Ecole Polytechnique, 2001.
• Hichem Sahbi, "Support vector machines for hierarchical face detection," Universite de Versailles, 2003.
• Christian d'Avignon, "Applying machine learning to biomedical data: the small-sample and interpretability dilemmas," Johns Hopkins University, 2004.
• Xiaodong Fan, "Learning a hierarchy of classifiers for multi-class shape detection," Johns Hopkins University, 2006.
• Sachin Gangaputra, "Invariant coarse-to-fine object detection and tracking," Johns Hopkins University, 2006.
• Francisco Sanchez, Johns Hopkins University, in progress
• Kan Jiang, Johns Hopkins University, in progress
• Erdem Yoruk, Johns Hopkins University, in progress
• Mary Lin, Johns Hopkins University, in progress
• Ting Li, Johns Hopkins University, in progress
• Bahman Afsari, Johns Hopkins University, in progress

Talks

• Multimedia Image Retrieval (MIR'06), Santa Barbara, October 26, 2006 (Keynote Address)
• Annual Meeting, "Norwegian Society for Image Processing and Pattern Recognition," Oslo, Norway, September 7, 2006 (Keynote Address)
• International Symposium on Information Theory (ISIT06), Seattle, WA, July 14, 2006 (Plenary Speaker)
• "Visual Learning and Recognition Workshop," IMA, Minneapolis, MN, May 23, 2006 (Organizer)
• Snowbird Learning Conference, Snowbird, Utah, April 5, 2006 (Invited Talk)
• Stochastic Systems Conference, Notre Dame University, March 25, 2006.
• Distinguished Lecture Series, Scientific Computing and Imaging Institute (SCI), Univeristy of Utah, Salt Lake City, March 17, 2006.
• Audio- and Video-Based Biometric Person Authentication (AVBPA 2005), Rye, New York, July 21-22, 2005.
• "Statistical Analysis of Postgenomic Data," Institut National Agronomique Paris, April 21-22, 2005.
• Workshop on "Pattern Classification, Learning and Object Recognition," MSRI, Berkeley, March 21-25, 2005.
• Introductory Workshop, "Mathematical, Computational and Statistical Aspects of Image Analysis," MSRI, Berkeley, Jan.24-28, 2005.
• Workshop on Object Recognition, Taromina, Sicily, October 10-12, 2004.
• Advanced Concepts for Intelligent Vision Systems, ACIVS04, Brussels, Belgium, Aug.31 - Sept. 3, 2004 (Plenary Speaker)
• Meeting of the Mathematical Association of America, Baltimore, MD, Nov. 7-8, 2003 (Invited Speaker).
• Computational Sciences Lecture Series: "Computational Vision and Image Analysis,'' University of Wisconsin, Madison, WI, October 30, 2003.
• "Mathematics Day'', Institute of Mathematics, Academia Sinica, Taipei, Taiwan, September 3, 2003.
• DIMACS Workshop on "Complexity and Inference'', Rutgers University, N.J., June 3, 2003.
• EURANDOM Workshop on "Statistical Learning in Classification and Model Selection'', Eindhoven, Netherlands, Jan. 18, 2003.
• SIAM Minisymposium on "Mathematical Problems in Image Analysis,''Joint Mathematics Meetings, Baltimore, MD, Jan. 16, 2003.
• "Distinguished Seminar Series on Vision,'' University of Maryland, Oct. 16, 2002.
• SIAM 50th Anniversary and 2002 Annual Meeting, Minitutorial: "Statistical Methods and Learning in Computer Vision,'' Philadelphia, July 9, 2002.
• International Workshop on "Energy Minimization Methods in Computer Vision and Pattern Recognition,'' Sophia Antipolis, France, Sept. 5, 2001 (Plenary Speaker)
• XXXIII Journees de Statistique (Annual Meeting, French Statistical Society), Nantes, France, May 14, 2001 (Keynote Address)
• MSRI Workshop: "Nonlinear Estimation and Classification,''Mathematical Sciences Research Institute, Berkeley, March 26, 2001.
• IMA Workshop: "Image Analysis and High Level Vision,'' Institute of Mathematics and its Applications, Minneapolis, November 13, 2000.
• Summer Seminar Series, Center for Language and Speech Processing, Johns Hopkins University, August 2, 2000.
• Colloquium on Signal and Image Processing, Ecole Polytechnique, November 22, 1999.
• Euroconference on "Computer Vision and Speech Recognition: Statistical Foundations and Applications,'' Anogia, Crete, July 4-8, 1999.
• CVPR Workshop on "Statistical and Computational Theories of Vision,'' Fort Collins, CO, June 22, 1999.
• The Thirteenth New England Statistics Symposium, University of Connecticut, April 24, 1999. (Plenary Speaker).
• "Birck Distinguished Lecture,'' School of Electrical and Computer Engineering, Purdue University, March 29, 1999.
• CIRM Conference on "Information Theory, Statistics and Image Analysis,'' Luminy, France, December 7-11, 1998.
• Symposium on "Questions Mathematiques en Traitement du Signal et de l'Image,'' Institut Henri Poincare, December, 1998.
• Annual SPIE Meeting, Session on "Bayesian Inference for Inverse Problems,'' San Diego, July 19-24, 1998.
• 1998 Lukacs Symposium on "Statistics for the 21'st Century,'' Bowling Green University, April 24-26, 1998.
• Workshop on "Machine Learning and Computer Vision,'' Newton Institute for Mathematical Sciences, Cambridge, England, Oct. 6-10, 1997.
• NATO ASI Symposium on "Face Recognition: From Theory to Applications", Stirling, UK, June 23-July 4, 1997.
• "Object Recognition and Sequential Testing'' (Lecture Series), Ecole Normale Superieure de Cachan, June, 1997.
• Second Seminar on "Stochastic Analysis, Random Fields and Applications,'' Monte Verita, Ascona, Switzerland, September 16-21, 1996.
• IEEE International Conference on Image Processing ("ICIP-96''), Lausanne, Switzerland, Sept. 16-19, 1996. (Plenary Speaker)
• Annual meeting: "Classification Society of North America,''Amherst, MA., June 14-15, 1996. (Plenary Speaker)
• University de Cergy, Department Sciences de l'Information, "Shape Quantization and Recognition,'' June, 1996. (Lecture Series)
• 24th Annual Dutch Conference on "Probability Theory and Mathematical Statistics," Lunteren, Netherlands, November 13-15, 1995. (Lecture Series)
• Workshop on "Spatial Statistics, Image Analysis and Stochastic Geometry," CWI, Amsterdam, November 9-11, 1995.
• Workshop on "Mathematical Methods in Computer Vision," Geometry Center, Univ. of Minnesota, September 11-15, 1995.
• Seminaire "Mathematiques et Imagerie," ENS Cachan, March 29, 1995.
• IEEE-IMS "Information Theory and Statistics Workshop," Alexandria, VA., October 27-29, 1994. (Plenary Speaker)
• Ninth Conference on "Pattern Recognition and Artificial Intelligence" (RFIA '94), Paris, January 11-14, 1994. (Plenary Speaker)

Publications

Peer-Reviewed Journals

• Xu, L, D. Geman and R. Winslow (2007), "Large-scale integration of cancer microarray data identifies a robust common cancer signature," BMC Bioinformatics 8:275.
• Anderson, T. J., I. Tchernyshyov, R. Diez, R.N. Cole, D. Geman, C. V. Dang and R. L. Winslow (2007). "Discovering robust protein biomarkers for disease from relative expression reversals in 2D DIGE data," Proteomics 7:1197-1207.
• Sahbi, H. and D. Geman (2006). "A hierarchy of support vector machines for pattern detection." Journal of Machine Learning Research 7: 2087-2123.
• Blanchard, G. and D. Geman (2005). "Sequential testing designs for pattern recognition." Annals of Statistics 33(3): 1155-1202.
• Tan, A. C., D. Q. Naiman, L. Xu, R. L. Winslow and D. Geman (2005). "Simple decision rules for classifying human cancers from gene expression profiles." Bioinformatics 21(20): 3896-3904.
• Xu, L., A. C. Tan, D. Q. Naiman, D. Geman and R. L. Winslow (2005). "Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data." Bioinformatics 21(20): 3905-3911.
• Amit, Y., D. Geman and X. Fan (2004). "A coarse-to-fine strategy for multi-class shape detection." IEEE Trans. Pattern Analysis and Machine Intelligence 26(12): 1606-1621.
• Geman, D., C. D'Avignon, D. Q. Naiman and R. L. Winslow (2004). "Classifying gene expression profiles from pairwise mRNA comparisons." Stat. Appl. Genet. Mol. Biol. 3(1): Article 19.
• D'Avignon, C. and D. Geman (2003). "Tree-structured neural decoding." Journal of Machine Learning Research 4: 743-754.
• Fleuret, F. and D. Geman (2001). "Coarse-to-fine face detection." International Journal of Computer Vision 41(1-2): 85-107.
• Geman, D. and B. Jedynak (2001). "Model-based classification trees." IEEE Trans. Information Theory 47(3): 1075-1082.
• Amit, Y. and D. Geman (1999). "A computational model for visual selection." Neural Computation 11: 1691-1715.
• Amit, Y. and D. Geman (1997). “Shape quantization and recognition with randomized trees.” Neural Computation 9:1545-1588.
• Amit, Y., D. Geman and K. Wilder (1997). “Joint induction of shape features and tree classifiers.” IEEE Trans. Pattern Analysis and Machine Intelligence 19(11): 1300-1306.
• Geman, D. and B. Jedynak (1996). "An active testing model for tracking roads from satellite images." IEEE Trans. Pattern Analysis and Machine Intelligence. 18(1): 1-14.
• Geman, D. and C. G. Yang (1995). "Nonlinear image recovery with half-quadratic regularization." IEEE Trans. Image Processing 4(7): 932-946.
• Geman, D. and G. Reynolds (1992). "Constrained restoration and the recovery of discontinuities." IEEE Trans. Pattern Analysis and Machine Intelligence 14(3): 367-383.
• Geman, S., D. E. McClure and D. Geman (1992). "A nonlinear filter for film restoration and other problems in image processing." Computer Vision, Graphics, and Image Processing 54(4): 281-289.
• Geman, D., S. Geman, C. Graffigne and P. Dong (1990). "Boundary detection by constrained optimization." IEEE Trans. Pattern Analysis and Machine Intelligence 12(7): 609-628.
• Geman, D. (1987). "A stochastic model for boundary detection." Image and Vision Computing 5: 61-65.
• Geman, S. and D. Geman (1984). "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images." IEEE Trans. Pattern Analysis and Machine Intelligence 6: 721-741.
• Derin, H., H. Elliott, R. Christi and D. Geman (1984). "Bayes smoothing algorithms for segmentation of images modeled by Markov random fields." IEEE Trans. Pattern Analysis and Machine Intelligence 6: 707-721.
• Geman, D., J. Horowitz and J. Rosen (1984). "A local time analysis of the intersections of Brownian paths in the plane." Annals of Probability 12: 86-107.
• Geman, D. and J. Horowitz (1981). "Smooth perturbations of a function with a smooth local time." Trans. Amer. Math. Soc. 267: 517-530.
• Geman, D. and J. Horowitz (1980). "Occupation densities." Annals of Probability 8: 1-67.
• Geman, D. (1979). "Dispersion points for linear sets and approximate moduli for some stochastic processes." Trans. Amer. Math. Soc. 253: 257-272.
• Geman, D. and J. Zinn (1978). "On the increments of multi-dimensional random fields." Annals of Probability 6: 151-158.
• Geman, D. (1977). "On the approximate local growth of multi-dimensional random fields." Z. Wahrscheinlichkeitstheorie verw. Geb. 38: 237-251.
• Geman, D. (1977). "Local times for vector functions: energy integrals and local growth rates." Houston J. Math. 3: 195-206.
• Geman, D. (1976). "A note on the continuity of local times." Proc. Amer. Math. Soc. 57: 321-326.
• Geman, D. and J. Horowitz (1976). "Occupation times for functions with countable level sets and the regeneration of stationary sequences." Z. Wahrscheinlichkeitstheorie verw. Geb. 35: 189-211.
• Geman, D. and J. Horowitz (1976). "Local times for real and random functions." Duke Math. J. 43: 809-828.
• Geman, D., J. Horowitz and J. Zinn (1976). "Recurrence of stationary sequences." Annals of Probability 4: 372-381.
• Geman, D. and J. Horowitz (1975). "Polar sets and Palm measures in the theory of flows." Trans. Amer. Math. Soc. 208: 141-159.
• Geman, D. and J. Horowitz (1975). "Random shifts which preserve measure." Proc. Amer. Math. Soc. 49: 143-150.
• Geman, D. and J. Horowitz (1974). "Transformations of flows by discrete random measures." Indiana Univ. Math. J. 24: 291-306.
• Geman, D. (1973). "A note on the distribution of hitting times." Annals of Probability 1: 854-856.
• Geman, D. and J. Horowitz (1973). "Occupation times for smooth stationary processes." Annals of Probability 1: 131-137.
• Geman, D. and J. Horowitz (1973). "Remarks on Palm measures." Annals Inst. H. Poincare 9: 215-232.
• Geman, D. (1972). "On the variance of the number of zeros of a stationary Gaussian process." Annals Math. Stat. 43: 977-982.

Conference Proceedings

• Ferecatu, M. and D. Geman (2007), "Interactive search for image categories by mental matching," Proc. Inter. Conf. on Computer Vision (ICCV '07), Rio de Janeiro.
• Koloydenko, A. and D. Geman (2006). "Ordinal coding of image microstructure." Proc. Inter. Conf. Image Processing, Computer Vision and Pattern Recognition (IPCV’06), Las Vegas.
• Gangaputra, S. and D. Geman (2006). "A design principle for coarse-to-fine classification." Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), New York, New York: 1877-1884.
• Fang, Y. and D. Geman (2005). "Experiments in mental face retrieval." Proc. Inter. Conf. on Audio and Video-based Biometric Person Authentication (AVBPA), Rye, NY, Lecture Notes in Computer Science: 637-646.
• Gangaputra, S. and D. Geman (2005). "A unified stochastic model for detecting and tracking faces." Proc. Second Canadian Conf. on Computer and Robot Vision (CRV’05), Victoria, British Columbia: 306-313.
• Fan, X. and D. Geman (2004). "Hierarchical object indexing and sequential learning." Proc. 17th Inter. Conf. on Pattern Recognition (ICPR’04), Cambridge, UK: 65-68.
• Gangaputra, S. and D. Geman (2004). "Self-normalized linear tests." Proc. IEEE Inter. Conf. on Computer Vision and Pattern Recognition (CVPR’04), Washington DC: 616-662.
• Geman, D., C. D'Avignon, D. Q. Naiman, R. L. Winslow and A. Zeboulon (2004). "Gene expression comparisons for class prediction in cancer studies." Proc. 36’th Symp. On the Interface: Computing Science and Statistics, Baltimore.
• Fleuret, F. and D. Geman (2002). "Fast face detection with precise pose estimation." Proc. 16th Inter. Conf. on Pattern Recognition (ICPR), Québec City: 235-238.
• Sahbi, H., D. Geman and N. Boujemaa (2002). "Face detection using coarse-to-fine support vector classifiers." Proc. IEEE Inter. Conf. on Image Processing (ICIP), Rochester, New York: 3, 925-928.
• Geman, D. (2001). "Interrogation Bayesienne d'une base de donnees." 33rd Journees de Statistique, Nantes, France: 15-20.
• Geman, D. and R. Moquet (2000). "A stochastic model for image retrieval." Proc. Reconnaissance des Formes et Intelligence Artificielle (RFIA), Paris, France.
• Fleuret, F. and D. Geman (1999). "Graded learning for object detection." Proc. 1st IEEE Workshop on Statistical and Computational Theories of Vision (SCTV), Fort Collins, Colorado.
• Geman, D. and A. Koloydenko (1999). "Invariant statistics and coding of natural microimages." Proc. 1st IEEE Workshop on Statistical and Computational Theories of Vision (SCTV), Fort Collins, Colorado.
• Li, C. and D. Geman (1999). "Active testing at multiple resolutions." Proc. Conf. American Statistical Association (ASA), Baltimore, Maryland.
• Geman, D. (1994). "The entropy strategy for shape recognition." Proc. IEEE-IMS Workshop on Information Theory and Statistics, Alexandria, VA.
• Geman, D. and B. Jedynak (1994). "Shape recognition and twenty questions." Proc. Reconnaissance des Formes et Intelligence Artificielle (RFIA) : 21-37.
• Geman, D., J. Horowitz and J. Kepner (1993). "Computation of IRAS fluxes via a priori astrometry." Proc. Conf.on Infrared Astronomy with Arrays: The Next Generation, Los Angeles, California.
• Geman, D. and B. Jednyak (1991). "Detection of roads in SPOT satellite images." Proc. IEEE Inter. Geoscience and Remote Sensing Symp.(IGARSS), Helsinski, Finland.
• Geman, D. (1985). "Bayesian image analysis by adaptive annealing." Proc. IEEE Inter. Geoscience and Remote Sensing Symp. (IGARSS): 269-276.

Book Chapters

• Gangaputra, S. and D. Geman (2006). The trace model for object detection and tracking. Toward Category-Level Object Recognition , Lecture Notes in Computer Science, 4170. J. Ponce et al. Berlin, Springer-Verlag: 401-420.
• Geman, D. (2003). Coarse-to-fine classification and scene labeling. Nonlinear Estimation and Classification. Lecture Notes in Statistics, 171. D. D. Denison, M. Hansen, C. C. Holmes, B. Mallick and B. Yu. New York, Springer-Verlag: 31-48.
• Amit, Y., D. Geman and B. Jedynak (1998). Efficient focusing and face detection. Face Recognition: From Theory to Applications. H. Wechsler. Berlin, Springer-Verlag: 157-173.
• Jung, F., B. Jednyak and D. Geman (1997). Recognizing buildings in aerial images. Automatic Extraction of Man-Made Objects from Aerial and Space Images (II). A. Gruen, E. P. Baltsavias and O. Basel. Birkhauser: 173-182.
• Geman, D., G. Reynolds and C. Yang (1993). Stochastic algorithms for restricted image spaces and experiments in deblurring. Markov Random Fields: Theory and Applications. R. Chellappa and A. Jain, Academic Press: 39-68.
• Geman, D. and B. Gidas (1991). Image Analysis and Computer Vision. Spatial Statistics and Image Processing , National Academy Press, Washington: 9-37.
• Geman, D. (1990). Remarks on hard modeling vs. image processing, circumstellar disks, and model validation. Restoration of HST Images and Spectra, Space Telescope Science Institute: 74-79.
• Geman, D., S. Geman and C. Graffigne (1987). Locating object and texture boundaries. Pattern Recognition Theory and Applications. P. Devijver and J. Kittler, Springer-Verlag.
• Derin, H., H. Elliott, R. Christi and D. Geman (1986). Application of the Gibbs distribution to image segmentation. Statistical Image Processing and Graphics, Marcel-Dekker: 3-24.
• Geman, D. and S. Geman (1986). Bayesian image analysis. Disordered Systems and Biological Organization. E. Bienenstock, F. Fogelman and G. Weisbuch, Springer-Verlag.

Monograph

• Geman, D. (1990). "Random Fields and Inverse Problems in Imaging." Lecture Notes in Mathematics, Springer-Verlag. 1427: 113-193.

Reports

• Fang, Y., D. Geman, N. Boujemaa, J. P. Chièze and H. Sahbi (2004). Experiments in mental face retrieval. Project IMEDIA, INRIA-Rocquencourt.
• Krempp, S., D. Geman and Y. Amit (2002). Sequential learning with reusable parts for object detection. Center for Imaging Science, Johns Hopkins University.
• Geman, D. and R. Moquet (2001). Q & A models for interactive search, Center for Mathematics and Its Applications, ENS-Cachan, France.
• Dupuis, P., D. Geman, J. Horowitz and G. Reynolds (1991). Statistical inference on the shape of circumstellar disks from HST observations, University of Massachusetts.
• Geman, D. and S. Geman (1987). Relaxation and annealing with constraints. Complex Systems Technical Report No. 35, Division of Applied Mathematics, Brown University.
• Geman, D. (1984). Parameter estimation for Markov random fields with hidden variables and experiments with the EM algorithm. Reports on Pattern Analysis No. 21, Division of Applied Mathematics, Brown University.
• Geman, D. and S. Geman (1983). Parameter estimation for some Markov random fields. Reports on Pattern Analysis No. 11, Division of Applied Mathematics, Brown University.