On the Computational and Statistical Interface and "Big Data"
Michael I. Jordan
, UC - Berkeley
Date: Thursday, October 17, 2013
Time: 3:00 PM to 4:00 PM Note: all times are in the Eastern Time Zone
Host: Big Data Initiative at CSAIL
Contact: Sheila M. Marian, 617-253-1996, firstname.lastname@example.org
Speaker URL: None
TALK: On the Computational and Statistical Interface and "Big Data"
Please note time and room change for this talk.
The rapid growth in the size and scope of datasets in science and
technology has created a need for novel foundational perspectives on
data analysis that blend the statistical and computational sciences.
That classical perspectives from these fields are not adequate to
address emerging problems in "Big Data" is apparent from their sharply
divergent nature at an elementary level---in computer science, the
growth of the number of data points is a source of "complexity" that
must be tamed via algorithms or hardware, whereas in statistics, the
growth of the number of data points is a source of "simplicity" in
that inferences are generally stronger and asymptotic results can be
invoked. Indeed, if data are a data analyst's principal resource, why
should more data be burdensome in some sense? Shouldn't it be
possible to exploit the increasing inferential strength of data at
scale to keep computational complexity at bay? I present three
research vignettes that pursue this theme, the first involving the
deployment of resampling methods such as the bootstrap on parallel and
distributed computing platforms, the second involving large-scale
matrix completion, and the third introducing a methodology of
"algorithmic weakening," whereby hierarchies of convex relaxations are
used to control statistical risk as data accrue. [Joint work with
Venkat Chandrasekaran, Ariel Kleiner, Lester Mackey, Purna Sarkar, and
Bio: Michael I. Jordan is the Pehong Chen Distinguished Professor in the
Department of Electrical Engineering and Computer Science and the
Department of Statistics at the University of California, Berkeley.
He received his Masters in Mathematics from Arizona State University,
and earned his PhD in Cognitive Science in 1985 from the University of
California, San Diego. He was a professor at MIT from 1988 to 1998.
His research in recent years has focused on Bayesian nonparametric
analysis, probabilistic graphical models, spectral methods, variational
methods, kernel machines and applications to problems in statistical
genetics, signal processing, computational biology, information retrieval
and natural language processing. Prof. Jordan is a member of the National
Academy of Sciences, a member of the National Academy of Engineering
and a member of the American Academy of Arts and Sciences. He is a
Fellow of the American Association for the Advancement of Science.
He has been named a Neyman Lecturer and a Medallion Lecturer by the
Institute of Mathematical Statistics. He is an Elected Member of the
International Institute of Statistics. He is a Fellow of the AAAI,
ACM, ASA, CSS, IMS, IEEE and SIAM.
Created by Sheila M. Marian at Tuesday, October 08, 2013 at 1:33 PM.