[Ml-stat-talks] CS Colloquium: Michael Jordan, W Oct 16 4:30pm, Friend Center 006

David Blei blei at CS.Princeton.EDU
Fri Oct 11 13:52:11 EDT 2013


michael jordan (u.c. berkeley, computer science & statistics) is
giving a distinguished colloquium next week in the computer science
department.  the lecture is on wednesday october 16 at 4:30pm with a
wine & cheese to follow.  title and abstract are below.

jordan is one of the most influential intellectuals in statistics and
machine learning, and he is a fantastic lecturer as well.  do not
miss.

best
dave

---

On the Computational and Statistical Interface and "Big Data"
Michael I. Jordan, University of California, Berkeley

Wednesday, October 16
4:30PM
Friend Center 006

http://www.cs.princeton.edu/events/event/457

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.


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.
His research interests bridge the computational, statistical,
cognitive and biological sciences, and have focused in recent years on
Bayesian nonparametric analysis, probabilistic graphical models,
spectral methods, kernel machines and applications to problems in
statistical genetics, signal processing, natural language processing
and distributed computing systems. 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, and has received the
ACM/AAAI Allen Newell Award. He is a Fellow of the AAAI, ACM, ASA,
CSS, IMS, IEEE and SIAM.


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