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.
