Hierarchical Modal Clustering based on the topography of Multivariate mixtures, department of Mathematics and Statistics, boston University, boston Mar 22, 2006. Hierarchical Modal Clustering based on the topography of Multivariate mixtures, department of Mathematics and Statistics, McGill University, montreal, canada feb 28, 2006. Hierarchical Modal Clustering based on the topography of Multivariate mixtures, department of biostatistics, University of North Carolina, chapel Hill Feb 24, 2006. Hierarchical Modal Clustering based on the topography of Multivariate mixtures, department of Statistics, yale University, new haven Feb 13, 2006. Model Selection in High-Dimensions: a quadratic risk-based approach, department of Prob- ability and Statistics, national University of Singapore, singapore feb 3, 2006 The topography of multivariate mixtures and Modal clusters, department of Mathematics and Statistics, University of Bristol, uk, jan 11, 2006 quadratic Distance:The basis. Louis, missouri june 8-12, 2005 bayes Factors in Structural Equation Models: Schwarz's bic and Other Approximations, American Sociological Association Section on Methodology: 2005 Annual meeting, Chapel Hill Apr 22, 2005 Selecting the number of Components in a finite mixture: a risk-based Approach, Interna- tional Conference. Professional Activities Organizer and chairperson of sessions in scientific meetings.
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Department of bio- statistics, University of Minnesota, october 31, 2007. An Extended bic for Model Selection. Joint Statistical meetings, salt lake city august 1, 2007. Modal Inference: building the bridge between nonparametric clustering and mixture analyses. Wnar and ims meetings, Irvine june 26, 2007. Modal inference online and its application to high-dimensional clustering, department of Statistics, harvard University, cambridge April 30, 2007. Department of Electrical and Computer Engineering, boston University march 21, 2007. Modal inference rumor and its application to high-dimensional clustering, department of biotatis- tics, harvard University, boston Feb 21, 2007. Hierarchical Modal Clustering based on the topography of Multivariate mixtures, Interna- tional Conference on Multivariate Statistical Methods, kolkata, india dec 29, 2006. Modal inference and its application to high-dimensional clustering, department Statistics, University of Connecticut, Storrs nov 9, 2006. Modal em for Mixtures and its Application in Clustering, department of Mathematics and Statistics, boston University, boston Sep 28, 2006.
Mhcprop: R-package for mhc binder prediction based on biophysiochemical properties of amino acids. Skills Statistics: Mixture models, model selection in high dimensions, Asymptotics of high dimen- sional low sample gender size, bioinformatics, Immunoinformatics, medical Image Analysis. Programming Languages: C/c, perl, python, java, pascal, css-html. Computing Platforms: Unix (Linux, sun Solaris), dos/Windows, mac os-x. Statistical Software: Extensive experience with R/SPlus; sas, matlab, mathematica, spss. Recent Invited Presentations Data mining and Knowledge discovery of Land cover and Terrestrial Ecosystem Processes from Global Remote sensing Data nasa conference on Intelligent Data Understanding: Presented by mark Friedl September 9-10, 2008 Modal Inference and Its Application to high-Dimensional Clustering Session on Mixture models. A tool for multi-layered clustering and dimension reduction. International Conference on Statistical Paradigms - recent Advances and reconciliations (icsprar-2008 Indian Statistical Institute, kolkata january 1-4, 2008. Modal Inference and Its Application to high-Dimensional Clustering.
Degrees of shredder Freedom in quadratic goodness of Fit. O., ray,., visser, i, bayarri,. A., ray,., zavisca,. A scaled unit information prior approximation to the bayes Factor. Published software The following softwares will be shortly available through cran (http cran. For current information about the packages and downloads visit. Edu/people/sray/software/ quadrisk: C binary for calculating estate quadratic risk of a mixture fit and providing graphical aid to high-dimensional model selection problems. Modality: R-package for finding the number of modes of a multivariate normal and pro- viding graphical and analytical representation of high-dimensional manifolds.
Model-based bi-clustering using two-way mixtures. Ray,., marron,. Feature selection based on high dimensional low sample size geometry. Ray,., lindsay,. Modal em for Mixtures and its Application in Clustering. Ray,., yeong,., pizer,., muller,., han. Sample size advantages of statistics on a nonlinear manifold to characterize nonlinear variation in a population. G., markatou., ray,.
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Proceedings of International Workshop on Mathematical foundations resume of Computational Anatomy. Ray,., lindsay,.(2005). The topography of Multivariate normal Mixtures. The Annals of Statistics 33,. Sequence pattern discovery with applications to understanding gene regulation and vaccine design. Handbook of Statistics. Elsevier Press in press ray,., lindsay,.
2005) Selecting the resume number of Components in a finite mixture: a risk-based Approach. Proceedings of the of the 37th Symposium on the Interface, computing Science and Statistics. Basu,., ray,., park,., basu,. (2002) Improved Power in Multinomial goodness-of-fit Tests, journal of the royal Statistical Society series d, 51, 3, 381-393. (2003) Distance-based Model-Selection with application to the Analysis of Gene Expression Data. working Papers ray,.
G., markatou., ray,., yang,., Chen,. (2008) quadratic distances on probabilities: the foundations. The Annals of Statistics Vol. 2, page ray,., lindsay,.(2008). Model selection in High-Dimensions: a quadratic-risk based Approach. Journal of the royal Statistical Society - series b volume 70 Issue 1 (Feb 95-118.
Ray,., tom Kepler (2007). Amino acid biophysical properties in the statistical prediction of peptide-mhc class I binding. Immunome research Oct 29;3(1 9 li,., ray,., Bruce g lindsay. (2007) a nonparametric Statistical Approach to Clus- tering via mode Identification journal of Machine learning Research 8(Aug. R., ray,., Chaney,. M.(2007) Signaling local non- credibility in an automatic segmentation pipeline Proceedings of the International Society for Optical Engineering meetings on Medical Imaging, volume 6512 jeong,., pizer,. (2006) Statistics on Anatomic Objects Reflecting Inter- Object Relations.
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Spr 2006 bios 110 ( Principles of Statistical Inference) Dept. Fall 2005 bios 110 ( Principles of Statistical Inference) Dept. Sum 2005 taught 3 Modules in samsi/crsc undergraduate workshop, crsc, north Car- olina State University. Spr 2004 bios 145 (Principles of Experimental Analysis dept. Fall 2001 stat 401 (Experimental Methods pennsylvania state University. Publications Hong huang Lin, ray,., songsak tongchusak, ellis. Reinherz, vladimir Brusic (2008) evaluation of mhc class I peptide margaret binding prediction servers: applications for vaccine research. Bmc immunology, 9:8 Lindsay,.
Medical Imaging- paperweight segmentation and characterization of anatomical objects in high dimensions and non-linear manifolds. Bioinformatics- focusing on classification of epitopes and model based clustering of microarray gene-expression data, with applications to "epitope-based" vaccine development. Teaching Experience Spr 2008 ma 576 (Generalized Linear Models) Dept. Of Mathematics and Statistics,. Ma 584 (Multivariate Statistical Analysis) Dept. Of Mathematics and Statistics. Fall 2007 ma 881 (Topics in High Dimensional Data Analysis) Dept. Spr 2007 ma 576 (Generalized Linear Models) Dept. Fall 2006 ma 586 (Design of Experiments) Dept.
Presentation" in Theoretical Statistics at the Interna- tional Conference on Statistics, combinatorics and Related Areas. (see presenta- tions below) several graduate student travel awards from the Eberly college of Science, pennState. 2002 davey graduate fellowship award from the Eberly college of Science, pennState. 2002 August and Ruth Homeyer Graduate fellowship award from the Eberly college of Science, pennState. 2000 Vollmer-Kleckner Scholarship award in Science from the Eberly college of Science, pennState, for the most outstanding performance in Phd qualifiers. Research Interests Theory and applications of finite mixture models, and detection of modes in high dimensional data, modal clustering. Assessment of model fit in high dimensional data and nonlinear space. Statistical methodology for social sciences focusing on structural equation models.
Dept of Statistics, pennsylvania state University. Dissertation: "Distance-based Model-Selection with application to Analysis of Gene. Expression Data paper advisor: Bruce. Dates attended: Aug 1999- aug,. Stat., indian Statistical Institute, calcutta, india. first division with distinction; - specialization: Applied Statistics and Data Analysis. Dates attended: Aug 1997- may. (Honors) in Statistics, Presidency college, calcutta - first division with distinction; - minors: Mathematics, Economics.
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Other Formats,. Ps, surajit ray, contact Details: Department of Mathematics and Statistics. T: Cummington Street, Rm 222 v:, boston University, boston, ma 02215, b: homepage:.edu/people/sray/. Professional Experience 2006-, assistant Professor, dept. Of Mathematics and Statistics, boston University, boston., visiting Assistant, professor, dept. Of biostatistics, University of North Carolina at Chapel Hill., Chapel Hill, post Doctoral Fellow, Statistics and Applied with Mathematical Sciences Institute, research Triangle park, durham, visiting Assistant, professor, dept. Of biostatistics, university of North Carolina at Chapel Hill., research Assistant, dept. Of Statistics, pennsylvania state University, university park, education.