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Mathematics of Knowledge and Search Engines

Workshops II: Numerical Tools and Fast Algorithms for Massive Data Mining, Search Engines and Applications

October 22 - 26, 2007


Organizing Committee | Scientific Overview | Speaker List

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Organizing Committee

Ming Gu (University of California, Berkeley (UC Berkeley))
Piotr Indyk (Massachusetts Institute of Technology)
Yann LeCun (New York University)
Vladimir Rokhlin (Yale University)
Sam Roweis (University of Toronto)
Andrew Zisserman (University of Oxford)

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Scientific Overview

The present era of science can be said to have begun with the appearance of computers and large data sets. For more than a half century the tools of numerical analysis have required repeated honing in the attempt to keep up with the explosion of data and information generated in our technological world. The next step in evolution has been the rise of massive networks (e.g. the internet and world wide web) and this has led to a concomitant demand for new, fast algorithms for solutions of problems related to the page weight algorithm, webcrawlers, etc. Increasingly, large data sets are no longer restricted to classical scientific domains, but arise in virtually all fields, including finance, economics, social networks, law, and the humanities. What is now beginning to emerge is the next generation of numerical algorithms that can be used to sort, order, or otherwise extract knowledge in a wide variety of situations. As the information sciences expand and integrate with other disciplines, the need for these tools has become especially acute. All of the modern, numerical problems encountered have the common feature that they require scalable algorithms with robustness, i.e. good error estimates. The development of fast algorithms in the period 1980 -2000 has laid the groundwork for today's challenges of numerical linear algebra, but new methods are now needed. This workshop will bring together researchers in various disciplines to discuss advances in the following topics:

  • Deterministic and randomized algorithms for matrix approximation
  • Analysis of dense matrices
  • Fast algorithms for SVD solvers
  • Algorithms for l0 and l1 approximation
  • High precision randomized algorithms of linear algebra
  • Interior point methods
  • Relation of fast solvers to the Fast Multipole Method
  • Manifold approximation
  • Band-limited functions on data sets

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Invited Speakers

Michael Berry (University of Tennessee)
Dan Boley (University of Minnesota, Twin Cities)
Leon Bottou (NEC Research Institute)
Chris Burges (Microsoft Research)
Oliver Chapelle (Yahoo! Research)
Inderjit Dhillon (University of Texas at Austin)
Rob Fergus (New York University)
Andrew Fitzgibbon (Microsoft Research)
Kristen Grauman (University of Texas at Austin)
Mark Green (Institute for Pure and Applied Mathematics)
Bruce Hendrickson (Sandia National Laboratories)
Alfred Inselberg (Tel Aviv University)
Michael Isard (Microsoft Research)
Peter Jones (Yale University)
Tamara Kolda (Sandia National Laboratories)
Svetlana Lazebnik (University of North Carolina)
Yann LeCun (New York University)
Till Quack (Eidgenössische TH Zürich-Hönggerberg)
Vladimir Rokhlin (Yale University)
Sam Roweis (University of Toronto)
Ruslan Salakhutdinov (University of Toronto)
Tamas Sarlos (Yahoo! Research)
Yoram Singer (Google Inc.)
Mark Tygert (Yale University)
Andrew Zisserman (University of Oxford)

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Contact Us:

Institute for Pure and Applied Mathematics (IPAM)
Attn: SEWS2
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/sews2/

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