Interface 2004
Invited Program

• Protein Folding - David Banks
  • Ingo Ruczinski, STATISTICAL AND COMPUTATIONAL ISSUES IN AB INITIO PROTEIN STRUCTURE PREDICTION, Johns Hopkins University
  • Jeffrey J. Gray, Finding the Protein-Protein Interface via Docking Calculations, Johns Hopkins University
  • Chris Bystroff, Five hierarchical levels of sequence-structure correlation in proteins, Department of Biology, Rensselaer Polytechnic Institute
• Cancer Classification Using Gene Expression Profiling - Dechang Chen
  • Zhenqiu Liu, Cancer Prediction with Robust Kernel PLS Algorithm and Gene Expression Profile, Bioinformatic Cell/ TATRC
  • Xue-wen Chen, Cancer Classification Using Informative Gene Profiles,The University of Kansas
  • Hanchuan Peng, An Efficient Max-Dependency Algorithm For Gene Selection, Lawrence Berkeley National Lab
• Genetic and Biochemical Networks: Methods and Empirical Models - Nicholas J. I. Lewin-Koh
  • Tianjiao Chu, Limitations of Statistical Learning from Gene Expression Data, Institute for Human and Machine Cognition, University of West Florida
  • Nicholas J. I. Lewin-Koh, Generating constraints on the topology of genetic networks using expression data: a combinatorial approach, Lilly Systems Biology Pte Ltd
  • Michal Ronen, Transient response of steady-state yeast cells to small perturbations, Stanford
• Too Many Features, Too Few Samples - Challenges for Disease Profiling Using Biomedical Data Ray Somorjai
  • R.L. Somorjai, The Analysis of Biomedical Data - Caveats and Challenges, Institute for Biodiagnostics, National Research Council Canada
  • Richard Simon, Supervised Analysis When the Number of Features (p) Greatly Exceeds the Number of Cases, National Cancer Institute
  • Kristin P. Bennett, Many features, few samples: from cheminformatics to bioinformatics, Rennselaer Poly. Tech.
• Genetic Algorithms for Computational Biology John Grefenstette
  • Bruce A. Shapiro, Visual Data Mining of RNA Secondary Structure Folding Pathways, Laboratory of Experimental and Computational Biology, NCI-Frederick
  • Michael Raymer, Knowledge discovery in large biological data sets using hybrid classifier/evolutionary algorithms, Wright State University
  • Ewy Mathe, Polyoptimizing Genetic Algorithms for Feature Selection, George Mason University
• Towards Understanding and Analyzing Proteomics Data - Kim-Anh Do
  • Marina Vannucci, Bayesian Methods for Proteomics with Feature Selection, Texas A&M University
  • Keith Baggerly, The Analysis of MALDI-TOF Proteomic Spectra from Serum Samples - A Case Study, MD Anderson Cancer Center
  • Kim-Anh Do, Nonparametric approaches to the classification of proteomic profiles, U. T. M.D. Anderson Cancer Center)
• Statistical and Metrological Issues in Proteomics using Time-of-flight Mass Spectrometry - Z. Q. John Lu
  • Bao-Ling Adam, Exploring Bioinformatics in Serum Proteomic Analysis for Early Detection of Prostate Cancer, Medical College of Georgia
  • Walter S. Liggett, Data-Driven and Peak-Based Feature Selection in Serum Protein Mass Spectrometry, National Institute of Standards and Technology
  • Zhen Zhang, Bioinformatics for Clinical Proteomics: Usage and Abusage, Johns Hopkins University
  • Z. Q. John Lu, SVD-based Functional ANOVA For Measurement Evaluation of MALDI-TOF Mass Spectrometry, NIST
• Mixture Modelling of Gene Expression Data - Kim-Anh Do
  • Giovanni Parmigiani, Mixture Models in Molecular Classification, Johns Hopkins University
  • Geoff McLachlan, Supervised and Unsupervised Learning Methods for Gene-Expression Data, University of Queensland
  • Wei Pan, Clustering-Based Classification for Gene Function Prediction Using Microarray Data, University of Minnesota
• Text Mining for Biomedical Applications - Lynette Hirschman
  • Dr. Wendy W. Chapman, Natural Language Processing for Biosurveillance, University of Pittsburg
  • Yves A. Lussier, Automated Terminological Networks For High Throughput Comparative Biology of Phenotypes, Columbia University
  • Laurie Damianos, The MiTAP System for Monitoring Reports of Disease Outbreak, The MITRE Corporation
  • James Wilson, V, MD, Project Argus, ISIS Center, Georgetown University, Washington D.C.
• Analysis of Functional Neuroimaging Data - Bill Eddy
  • Kary Myers, Brains on Film: Using Optical Imaging to Build Maps of Brain Activity, Carnegie Mellon University
  • Rebecca L. McNamee, Reducing Phyiological Noise in fMRI Experiments, University of Pittsburgh
  • William Eddy, Systematic Noise in Magnetoencephalography, Carnegie Mellon University
  • Ivo Dinov, Wavelet-Based Statistical Analysis of fMRI Data, UCLA Statistics/Neurology
• Internet Tomography - Amy Braverman
  • Cheolwoo Park, Wavelet and SiZer Analyses of Internet Traffic Data, SAMSI
  • George Michailidis, Tools for the Analysis and Visualization of Network Traffic, The University of Michigan
• Mass Spectroscopy and Clinical Proteomics - Michael Trosset
  • Dariya Malyarenko, Signal Conditioning and Filtering of SELDI Mass Spectrometry Time Series, College of William and Mary and INCOGEN, Inc.
  • Timothy W. Randolph, A Multiresolution View of Protein Mass Spectrometry Data, University of Washington
• Functional Data Analysis for Computational Biology - Catherine Loader and Ramani Pilla
  • Lyndia C. Brumback, Self modeling with flexible, random time transformations, University of Washington
  • Julian Faraway, Modeling Continuous Shape Change for Facial Animation, University of Michigan
  • Keith Worsley, Detecting Changes in Brain Shape, Scale and Connectivity via the Geometry of Random Fields, McGill University
• Epistasis - Bill Shannon
  • Jurg Ott, Gene-gene and gene-environment interactions in genetic case-control association studies, Rockefeller University
  • Rob Culverhouse, Detecting Epistatic Interactions Contributing to Quantitative Traits, Washington University School of Medicine
  • Jason H. Moore, Systems biology thought experiments for interpreting epistasis models, Vanderbilt University
• The Best of Data Mining from KDD - Arnie Goodman
  • Haixun Wang, Mining Concept-Drifting Data Streams Using Ensemble Classifiers, IBM T. J. Watson Research Center
  • Stephen Bay, Mining Distance-Based Outliers in Near Linear Time, Stanford University
  • Jaideep Vaidya, Privacy Preserving K-Means Clustering over Vertically Partitioned Data, Purdue University
• The Analysis of Streaming Data Bill Szewczyk
  • Kun-Lung Wu, Indexing Continual Range Queries for Efficient Stream Processing, IBM Watson Research
  • Edward J. Wegman, Visual Analytics for Streaming Internet Data, George Mason University
  • Andrew Norton, Streaming Graphics, SPSS, Inc.
• Analysis of Very Large Datasets - David Scott
  • Alan Wilks, Having It All, AT&T Labs - Research
  • Antony Unwin, Interactive Graphics for large data sets - there is more to it than meets the eye, Augsburg University
  • David Scott, Alternatives to Mixture Modeling in High Dimensions, Rice University
• Future of Statistical Software Jim Gentle
  • John Sall, Challenges for Future Statistical Software for Non-expert Users, SAS Institute
  • Uwe Ziegenhagen, Yxilon -- Designing the next generation, vertically integrable statistical computing environment, HU Berlin
  • Yuichi Mori, XML-Based Applications in Statistical Analysis, Okayama University of Science
• Visualization and Analysis of Text/Web Data - Dunja Mladenic Her Website
• Marko Grobelnik, Efficient Visualization of Large Text Corpora, J.Stefan Institute
• Anne Kao Visual Text Mining with TRUST and Starlight, Boeing
• Text Mining and Applications - Ed Wegman>
  • Jeff Solka Identifying Cross Copora Document Associations Via Minimal Spanning Trees, NSWCDD
  • Elizabeth Leeds Intersection Graphs for Text Analysis, NSWCDD
  • Wendy L. Martinez Document Classification and Clustering Using Weighted Text Proximity Matrices, Ofice of Naval Research
• Mixture Modeling: Modern Approaches and Applications Richard Charnigo
  • Ramani Pilla, The Volume-of-Tube Formula: Applications to Perturbation and Mixture Models, Case Western Reserve University
  • Jeremy Nadolski, The Role of Latent Variables in Model Selection Accuracy, University of Kentucky
  • Jing Qin, Empirical likelihood based inferences in semiparametric finite mixtures, Memorial Sloan-Kettering Cancer Center
• Best of the IASC Session - I - Best of the IASC: High Dimensional Statistical Genomics from Genes to Proteins to Pathways - David Allison
  • Grace S. Shieh, Using Bayesian Networks to Reconstruct Yeast Genetic Networks, Institute of Statistical Science, Academia Sinica, Taiwan
  • Kui Zhang, Does Sequence Similarity Predict Expression Similarity, Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham
  • Francoise Seillier-Moiseiwitsch, Statistical Methods for Proteomics, University of Maryland Baltimore County
• Best of the IASC Session - II - Michael G. Schimek
  • Michele La Rocca, Resampling techniques in neural networks for non linear time series analysis, University of Salerno, Italy
  • Knut M. Wittkowski, Combining ordinal measures in medical research, The Rockefeller University
• Highlights of the SAMSI Data Mining Year David Banks
  • David Banks, Combinatorial Search in Data Mining, Duke University
  • Ashish Sanil, Issues in 'Real Data' Mining, National Institue of Statistical Sciences
  • Ernest Fokoue, Mixtures of Factor Analyzers: Their Place in Data Mining, SAMSI
• Statistical Analysis of Internet Data Alfred Hero
  • Bin Yu, Network Tomography, UC Berkeley
  • Eric D Kolaczyk, Empirical Analysis of Structure in Computer Network Traffic Flows, Boston University
  • Rui Castro, Hierarchical Clustering and Network Topology Identification, Rice University
• Brief Tutorials in Tubes and Graphs - Wendy Martinez
  • Catherine Loader, The Volume-of-Tube formula: Computational Methods and Statistical Applications, Case Western Reserve University
  • Susan Holmes, Multivariate Statistics for Trees and Networks
• Matrix Computations and Data Mining - Jesse Barlow and Jia Li
  • Chris Ding, Principal Component and Self-aggregation Clustering, Lawrence Berkeley National Laboratory
  • Jesse Barlow, Operations to Construct and Maintain a Truncated ULV Decompostion, The Pennsylvania State University
  • Jia Li, Classification of Microarray Data by Two-way Gaussian Mixtures, The Pennsylvania State University, University Park
  • Hao Helen Zhang, Unified Multiclass Proximal Support Vector Machines, North Carolina State University
• Computational Geometry and Robust Statistics - Diane Souvaine
  • David M. Mount, On the Least Median Square Problem, Univerity of Maryland
  • Eynat Rafalin, Computational Geometry and Statistical Depth Measures, Tufts University
  • Ming OUYANG, Imputation of microarray data, University of Medicine and Dentistry of New Jersey
• West Nile Virus - Juergen Symanzik
  • James M Wilson, V, MD, The Anatomy of a Bioevent: West Nile Virus in Washington, DC, ISIS Center, Georgetown University, Washington D.C.
  • Sean C. Ahearn, Dead Birds and Human Infections. A geostatistical approach, Center for Advanced Research of Spatial Information (CARSI Lab.), Hunter College-CUNY
  • Juergen Symanzik, Visualization, Web-Access, and Simulation of West Nile Virus Data - From the Regional to the National Level, Juergen Symanzik, Utah State University

Last Update May 6,2004