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This Program is Supported by the National Science Foundation's ACT Program, with additional support from Los Alamos National Laboratory.

Graduate Summer School: Intelligent Extraction of Information from Graphs and High Dimensional Data

July 11 - 29, 2005

Schedule and Presentations

Program Poster PDF


Organizing Committee

Edmond Chow (D.E. Shaw Research & Development)
Tina Eliassi-Rad (Lawrence Livermore National Laboratory)
Yann LeCun (New York University)
Carey Priebe (Johns Hopkins University)
Kevin Vixie (Los Alamos National Laboratory)


In recent years, there has been a rapidly increasing demand for targeted analysis of large data streams and large networks. One of the main goals has been identification of key features: face recognition in video streams and voice recognition in audio streams are two examples. Another goal has been inference of relationships: pattern discovery in large databases and determination of key links in social networks. At the same time, a number of scientific disciplines have come together to develop a theory for the analysis of high-dimensional data, as well as for the analysis of dynamic processes on massive graphs. The new techniques and new mathematics coming out of this line of research are ideally suited to a wide range of applications.

Applications and connections to real challenges will be drawn from: data fusion, automated feature extraction, face and shape recognition, spectral and hyperspectral image analysis, relational data mining, link analysis and discovery, graph mining, social and transactional networks, robust network design (making networks hard to break), optimal epidemic intervention (making networks easy to break), and hidden state inference (where are targets based on indirect measurements?).

The summer school is intended for graduate students and postdocs, as well as more senior researchers interested in focusing their efforts on these mathematical challenges and crucial applications. The program is organized as follows.

  • Week 1: High-dimensional data, relational data and kernel methods. The ubiquitous nature of high-dimensional data, combined with the difficulties presented by them, argues for the importance of finding models for their analysis. At the same time, large collections of relational data present the challenge of detecting and inferring factual information from sparse evidence. This week will highlight research in dimensionality reduction, as well as methods of graph mining and relational data mining.
  • Week 2: Image analysis and machine learning. The importance of image data for the validation of scientific theories in the form of large-scale computations underscores the need for principled metrics on data in those image spaces. This week will explore topics involving image detection as well as learning from image, voice and text data. Such problem are integral to building efficient algorithms for automatic detection of targets (such as faces), classification of patterns (face recognition) and prediction of important events (extreme event prediction).
  • Week 3: Streaming data and networks. There is a rapidly growing need for effective methods in addressing problems on large distributed networks. Problems associated with dynamics on and of networks are largely unexplored. This week will provide a further focus on graph mining as well as on analysis of streaming data, and will involve such topics as network tomography, moving neighborhood networks, dynamic network analysis and social networks.

We anticipate that some participants will be interested in attending the entire program while others will want to stay for only one or two of the week-long sessions.


James Abello (Rutgers University)
Tom Asaki (Los Alamos National Laboratory)
Erik Bollt (Clarkson University)
Leon Bottou (NEC)
Robert Burleson (Lawrence Livermore National Laboratory)
Frédéric Cao (Institut National de Recherche en Informatique Automatique (INRIA) - Lorraine)
Rick Chartrand (Los Alamos National Laboratory)
Ronald Coifman (Yale University)
John Conroy (Institute for Defense Analyses)
Terence Critchlow (Lawrence Livermore National Laboratory)
George Cybenko (Dartmouth University)
Tina Eliassi-Rad (Lawrence Livermore National Laboratory)
Christos Faloutsos (Carnegie Mellon University)
Leslie Greengard (New York University)
Martial Hebert (Carnegie Mellon University)
David Heckerman (Microsoft Research)
Piotr Indyk (Massachusetts Institute of Technology)
Shalev Itzkovitz (Weizmann Institute)
Peter Jones (Yale University)
Michael Jordan (University of California at Berkeley)
Ron Kimmel (Technion, Haifa, Israel)
Daphne Koller (Stanford University)
John Lafferty (Carnegie Mellon University)
Yann LeCun (New York University)
Gilad Lerman (University of Minnesota)
Michael Mahoney (Yale University)
Mehryar Mohri (New York University)
Andrew Moore (Carnegie Mellon University)
Jean-Michel Morel (Ecole Normale Supérieure, Cachan, France)
Robert Nowak (University of Wisconsin)
Bruno Olshausen (University of California at Davis)
Stanley Osher (Institute for Pure and Applied Mathematics)
Carey Priebe (Johns Hopkins University)
Prabhakar Raghavan (Yahoo! Research)
Ronald Resmini (NGA)
Guillermo Sapiro (University of Minnesota)
Lawrence Saul (University of Pennsylvania)
Edward Scheinerman (Johns Hopkins University)
Ingo Steinwart (Los Alamos National Laboratory)
William Szewczyk (Department of Defense)
Demetri Terzopoulos (New York University)
James Theiler (Los Alamos National Laboratory)
Godfried Toussaint (McGill University)
Richard Tsai (Princeton University)
Kevin Vixie (Los Alamos National Laboratory)
Grace Wahba (University of Wisconsin)

Contact Us:

Institute for Pure and Applied Mathematics (IPAM)
Attn: GSS2005
460 Portola Plaza
Los Angeles CA 90095-7121
Phone: 310 825-4755
Fax: 310 825-4756
Email: ipam@ucla.edu
Website: http://www.ipam.ucla.edu/programs/gss2005/

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