DIMACS Workshop on Recent Advances in Mathematics and Information Sciences for Analysis and Understanding of Massive and Diverse Sources of Data

May 14 - 15, 2007
DIMACS Center, CoRE Building, Rutgers University

Organizers:

Wen Masters, Office of Naval Research, Wen_Masters@onr.navy.mil

Fred Roberts, DIMACS, froberts@dimacs.rutgers.edu

Presentation Slides:

• Richard Baraniuk, Rice University
  Compressive Sensing for High-Dimensional Data

• David Blei, Princeton University
  Modeling Science: Discovering Themes in Large Collections of Documents

• Claire Cardie, Cornell University
  Employing Recent Advances in Machine Learning for Opinion Summarization

• Lawrence Carin, Duke University
  Inferring Data Inter-Relationships Via Fast Hierarchical Models

• Ronald Coifman Yale University
  Diffusion Geometries, and Multiscale Harmonic Analysis on Graphs and Complex Data Sets

• Graham Cormode, AT & T Labs
  A Compact Survey of Compressed Sensing

• Pedro Domingos, University of Washington
  Statistical Relational Learning

• Anna Gilbert, University of Michigan
  The Interplay of Analysis and Algorithms

• Robert Grossman, University of Illinois
  Modeling Highly Heterogeneous (Large) Data Sets: Towards a Billion Models

• Anil Jain, Michigan State University
  Semi-supervised Learning

• Cliff Joslyn, Los Alamos National Laboratory
  Semantic Hierarchies in Knowledge Analysis and Integration

• Paul Kantor, Rutgers University
  Bayesian Models, Prior Knowledge, and Data Fusion for Monitoring Messages and Identifying Authors

• John Lafferty, Carnegie Mellon University
  Nonparametric Sparsity

• Hassun Park, Georgia Institute of Technology
  Effective Dimension Reduction with Prior Knowledge

• Dan Roth, University of Illinois at Urbana-Champaign
  Learning and Global Inference for Information Access and Natural Language Understanding

• Bin Yu, UC Berkeley
  Grouped and Hierarchical Model Selection through Composite Absolute Penalties (CAP)

Workshop Index

DIMACS Homepage