Abstract: Modern applications such as Internet traffic, telecommunication
records, and large-scale social networks generate massive amounts of data
with multiple aspects and high dimensionalities. Tensors (i.e., multi-way
arrays) provide a natural representation for such data. Consequently, tensor
decompositions such as Tucker become important tools for summarization and
analysis.

One major challenge is how to deal with highdimensional, sparse data. In
other words, how do we compute decompositions of tensors where most of the
entries of the tensor are zero. Specialized techniques are needed for
computing the Tucker decompositions for sparse tensors because standard
algorithms do not account for the sparsity of the data. As a result, a
surprising phenomenon is observed by practitioners: Despite the fact that
there is enough memory to store both the input tensors and the factorized
output tensors, memory overflows occur during the tensor factorization
process. To address this intermediate blowup problem, we propose
Memory-Efficient Tucker (MET). Based on the available memory, MET adaptively
selects the right execution strategy during the decomposition. We provide
quantitative and qualitative evaluation of MET on real tensors. It achieves
over 1000X space reduction without sacrificing speed; it also allows us to
work with much larger tensors that were too big to handle before. Finally,
we demonstrate a data mining case-study using MET.

Biography: Dr. Jimeng Sun is a research staff member at IBM TJ Watson lab.
He received a Bachelor and MPhil in Computer Science from Hong Kong
University of Science and Technology in 2002 and 2003. After that, he
obtained a MS and PhD degree in Computer Science from Carnegie Mellon
University in 2006 and 2007. His research interests include data mining for
streams and networks, databases and service science. He has received the
best research paper award in ICDM 2008, the best research paper award in SDM
2007. He has published over 30 refereed articles and one book chapter.  He
filed four patents and has given four tutorials. He has served as a program
committee member of SIGKDD, SDM and CIKM and a rev