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MONDAY, NOVEMBER 2
PLENARY
10–10:50am
Grand Ballroom B, Upper 200 Level
Computational Thinking, Inferential
Thinking and Big Data
Michael I. Jordan, Pehong Chen
Distinguished Professor in the Department
of Electrical Engineering and Computer
Science, Department of Statistics, University
of California, Berkeley
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
inferential and computational sciences. The
fact 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 computational 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. On a formal level, the gap
is made evident by the lack of a role for
computational concepts such as “runtime”
in core statistical theory and the lack of a
role for statistical concepts such as “risk”
in core computational theory. I present
several research vignettes aimed at bridging
computation and statistics, including the
problem of inference under privacy and
communication constraints, algorithm
weakening as a tool for trading off the speed
and accuracy of inference, and the theoretical
study of lower bounds that embody
computational and statistical constraints.
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 distributed
computing systems, natural language
PLENARY AND KEYNOTE PRESENTATIONS
Annals of Probability, Annals of Applied
Probability, and Mathematics of Operations
Research and Queueing Systems
. She
is a recipient of the Erlang Prize of the
INFORMS Applied Probability Society
(2006), was elected fellow of the Institute for
Mathematics and Statistics (IMS) in 2013, and
was an IMS Medallion Lecturer in 2015.
KEYNOTE
3:10–4pm
Grand Ballroom B, Upper 200 Level
Optimization Techniques in
Data Analysis
Stephen J. Wright, Professor of Computer
Sciences, University of Wisconsin-Madison
Optimization perspectives have provided
valuable insights into machine learning and
data analysis problems, and optimization
formulations have led to practical algorithms
with good theoretical properties. In turn,
the rich collection of problems arising in
learning and data analysis is driving new
fundamental research in optimization,
reviving interest in well-established
techniques and stimulating development
of new methods. We discuss research on
several areas of learning and data analysis,
including regression/classification, signal and
image reconstruction, and manifold learning,
in each case describing problem areas in
which optimization algorithms have been
developed and successfully applied.
Stephen J. Wright
is a professor of
computer sciences
at the University of
Wisconsin-Madison.
His research focuses
on computational
optimization and its
applications to many areas of science and
engineering. Prior to joining UW-Madison in
2001, Wright was a senior computer scientist
at Argonne National Laboratory (1990–2001),
and a professor of computer science at
the University of Chicago (2000–2001). He
has served as chair of the Mathematical
Optimization Society and as a Trustee
of the Society for Industrial and Applied
Mathematics (SIAM). He is a Fellow of SIAM.
In 2014, he won the W.R.G. Baker Award
from IEEE. Wright is the author or coauthor
of widely used text/reference books in
optimization including
Primal Dual Interior-
Point Methods
(SIAM 1997) and
Numerical
Optimization
(2nd ed., Springer 2006, with J.
Nocedal). He has published on optimization
theory, algorithms, software, and applications.
Wright is editor-in-chief of the
SIAM Journal
on Optimization
and has served as editor-
in-chief or associate editor of
Mathematical
Programming: Series A and B, SIAM Review,
and
Applied Mathematics and Computation
.
KEYNOTE
3:10–4pm
201C, 200 Level
Reprise of 2015 Edelman
Award-Winning Presentation
Joseph Byrum, Craig Davis, Gregory Doonan,
Tracy Doubler, David Foster, Bruce Luzzi,
Ronald Mowers, Chris Zinselmeier
Syngenta, a leading developer of crop
varieties (seeds) that provide food for human
and livestock consumption, is committed
to bringing greater food security to an
increasingly populous world by creating a
transformational shift in farm productivity.
Syngenta Soybean Research & Development
(R&D) is leading Syngenta’s corporate
plant-breeding strategy by developing and
implementing a new product development
model that is enabling the creation of an
efficient and effective soybean breeding
strategy. Key to the new strategy is the
combination of advanced analytics and plant-
breeding knowledge to find opportunities
to increase crop productivity and optimize
plant-breeding processes. Syngenta uses
discrete-event and Monte Carlo simulation
models to codify Syngenta Soybean
R&D best practices, and uses stochastic
optimization to create the best soybean
breeding plans and strategically align its
research efforts. As a result of using these
new analytical tools, Syngenta estimates that
it will save more than $287 million between
2012 and 2016.
FRANZ EDELMAN AWARD
The Franz Edelman Award for
Achievement in Operations Research
and the Management Sciences
calls out, recognizes, and rewards
outstanding, high impact applications
of OR/MS. Each year, six finalists
compete in the “Super Bowl” of
O.R. in practice. The 2015 finalists
include IBM, Ingram Micro, LMI/
Defense Logistics Agency, Saudi
Arabia Ministry of Municipal and Rural
Affairs, Sygenta, and U.S. Army. In this
keynote, the first-place Syngenta will
reprise their winning presentation.
All Plenary & Keynote Presentations will take place in the Convention Center.