[auton-users] Michael Jordan

[predragp at andrew.cmu.edu]

Dear All,

Professor Jordan from UC Berkeley

http://www.cs.berkeley.edu/~jordan/

will be Statistics department colloquium speaker on Monday October 14th
from 12:00 PM - 1:00 PM in University Center Rangos. He will be speaking
"On the Computational and Statistical Interface and "Big Data"

This is the abstract of his talk:

On the Computational and Statistical Interface and "Big Data" 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 Ameet Talwalkar]

Cheers,
Predrag