Here are
the abstracts in plain text format.This schedule is subjected to changes and revisions over the next few days.
Here are
the abstracts in plain text format.
WEDNESDAY - JUNE 25, 2008 - DATA ANALYSIS AND DATA APPLICATIONS
9:00 - 9:45 Breakfast and registration
9:45 - 10:00 Opening: Organizers
10:00 - 11:00 Christos Faloutsos (Carnegie Mellon University)
TUTORIAL: Graph mining: laws, generators and tools
11:00 - 11:30 Deepak Agarwal (Yahoo! Research, Silicon Valley)
Predictive discrete latent models for large incomplete dyadic data
11:30 - 12:00 Chandrika Kamath (Lawrence Livermore National Laboratory)
Scientific data mining: why is it difficult?
12:00 - 2:00 LUNCH (ON YOUR OWN)
2:00 - 3:00 Edward Chang (Google Research, Mountain View)
TUTORIAL: Challenges in mining large-scale social networks
3:00 - 3:30 Sharad Goel (Yahoo! Research, New York)
Predictive indexing for fast search
3:30 - 4:00 James Demmel (University of California, Berkeley)
Avoiding communication in linear algebra algorithms
4:00 - 4:30 COFFEE BREAK
4:30 - 5:00 Jun Liu (Harvard University)
Bayesian inference of interactions and associations
5:00 - 5:30 Fan Chung (University of California, San Diego)
Four graph partitioning algorithms
5:30 - 6:00 Ronald Coifman (Yale University)
Diffusion geometries and harmonic analysis on data sets
6:00 - 9:30 OPENING RECEPTION (NEW GUINEA GARDEN)
THURSDAY - JUNE 26, 2008 - NETWORKED DATA AND ALGORITHMIC TOOLS
9:00 - 10:00 Milena Mihail (Georgia Institute of Technology)
TUTORIAL: Models and algorithms for complex networks,
with network elements maintaining characteristic profiles
10:00 - 10:30 Reid Andersen (Microsoft Research, Redmond)
An algorithm for improving graph partitions
10:30 - 11:00 COFFEE BREAK
11:00 - 11:30 Michael W. Mahoney (Yahoo! Research, Silicon Valley)
Community structure in large social and information networks
11:30 - 12:00 Nikhil Srivastava (Yale University)
Graph sparsification by effective resistances
12:00 - 12:30 Amin Saberi (Stanford University)
Sequential algorithms for generating random graphs
12:30 - 2:30 LUNCH (ON YOUR OWN)
2:30 - 3:00 Pankaj K. Agarwal (Duke University)
Modeling and analyzing massive terrain data sets
3:00 - 3:30 Leonidas Guibas (Stanford University)
Detection of symmetries and repeated patterns in 3D point cloud data
3:30 - 4:00 Yuan Yao (Stanford University)
Topological methods for exploring pathway analysis in complex biomolecular folding
4:00 - 4:30 COFFEE BREAK
4:30 - 5:00 Piotr Indyk (Massachusetts Institute of Technology)
Sparse recovery using sparse random matrices
5:00 - 5:30 Ping Li (Cornell University)
Compressed counting and stable random projections
5:30 - 6:00 Joel Tropp (California Institute of Technology)
Algorithms for matrix column selection
FRIDAY - JUNE 27, 2008 - STATISTICAL, GEOMETRIC, AND TOPOLOGICAL METHODS
9:00 - 10:00 Jerome H. Friedman (Stanford University)
TUTORIAL: Fast sparse regression and classification
10:00 - 10:30 Tong Zhang (Rutgers University)
An adaptive forward/backward greedy algorithm for learning sparse representations
10:30 - 11:00 COFFEE BREAK
11:00 - 11:30 Jitendra Malik (University of California, Berkeley)
Classification using intersection kernel SVMs is efficient
11:30 - 12:00 Elad Hazan (IBM Almaden Research Center)
Efficient online routing with limited feedback and optimization in the dark
12:00 - 12:30 T.S. Jayram (IBM Almaden Research Center)
Cascaded aggregates on data streams
12:30 - 2:30 LUNCH (ON YOUR OWN)
2:30 - 3:30 Gunnar Carlsson (Stanford University)
TUTORIAL: Topology and data
3:30 - 4:00 Partha Niyogi (University of Chicago)
Manifold regularization and semi-supervised learning
4:00 - 4:30 COFFEE BREAK
4:30 - 5:00 Sanjoy Dasgupta (University of California, San Diego)
Random projection trees and low dimensional manifolds
5:00 - 5:30 Kenneth Clarkson (IBM Almaden Research Center)
Tighter bounds for random projections of manifolds
5:30 - 6:00 Yoram Singer (Google Research, Mountain View)
Efficient projection algorithms for learning sparse representations
from high dimensional data
6:00 - 6:30 Arindam Banerjee (University of Minnesota, Twin Cities)
Bayesian co-clustering for dyadic data analysis
6:30 - 9:30 RECEPTION AND POSTER SESSION (OLD UNION CLUB HOUSE)
SATURDAY - JUNE 28, 2008 - MACHINE LEARNING AND DIMENSIONALITY REDUCTION
9:00 - 10:00 Michael I. Jordan (University of California, Berkeley)
TUTORIAL: Sufficient dimension reduction
10:00 - 10:30 Nathan Srebro (University of Chicago)
More data less work: SVM training in time decreasing with larger data sets
10:30 - 11:00 COFFEE BREAK
11:00 - 11:30 Inderjit S. Dhillon (University of Texas, Austin)
Rank minimization via online learning
11:30 - 12:00 Nir Ailon (Google Research, New York)
Efficient dimension reduction
12:00 - 12:30 Satyen Kale (Microsoft Research, Redmond)
A combinatorial, primal-dual approach to semidefinite programs
12:30 - 2:30 LUNCH (BOX LUNCH PROVIDED)
2:30 - 3:00 Ravi Kannan (Microsoft Research, India)
Spectral algorithms
3:00 - 3:30 Chris Wiggins (Columbia University)
Inferring and encoding graph partitions
3:30 - 4:00 Anna Gilbert (University of Michigan, Ann Arbor)
Combinatorial group testing in signal recovery
4:00 - 4:30 COFFEE BREAK
4:30 - 5:00 Lars Kai Hansen (Technical University of Denmark)
Generalization in high-dimensional matrix factorization
5:00 - 5:30 Holly Jin (LinkedIn)
Exploring sparse nonnegative matrix factorization
5:30 - 6:00 Elizabeth Purdom (University of California, Berkeley)
Data analysis with graphs
6:00 - 6:30 Lek-Heng Lim (University of California, Berkeley)
Ranking via Hodge decompositions of digraphs and skew-symmetric matrices
6:30 - 8:00 CLOSING RECEPTION