EDUCATION
Ph.D: Industrial and Systems Engineering, Georgia
Institute of Technology, 1998
M.S.: Applied Mathematics, Georgia Institute of Technology, 1995, and
University of Sao Paulo, Brazil, 1992
B.S.: Computer Science, University of Sao Paulo, Brazil, 1987
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A
BRIEF STATEMENT ABOUT MY RESEARCH
My research focuses mostly on optimization of
systems when there is uncertainty involved. It includes:
 Theory and algorithms for stochastic optimization, particularly
using sampling methods.
 Algorithms for optimization with risk management
 Uncertainty modeling
 Applications, especially to transportation and energy.
Background: (or [Back to Top])
My research interests lie in a broad area consisting
of problems where the goal is to aid the decision making process while taking
into account some underlying uncertainty. The presence of uncertainty arises
from various sources  e.g., future information, unknown factors, unexpected
events, etc. Problems of such type are ubiquitous, arising in a variety of
areas such as production planning, finance, and service logistics, to name a
few. A realistic example occurs in the airline industry, where a company must
decide which ticket classes (at different prices) will be on sale at each
point in time during the booking process. This is a difficult problem,
especially because customers who are willing to pay more (e.g., those
traveling on business) usually do not book early. Thus, on one hand the
airline wants to reserve some seats for those highfare paying customers by
closing lowerfare classes, but on the other hand it does not know how many
of those customers will actually book a ticket. The airline wants then to optimize
its revenue but needs to deal with the uncertainty of demand.
The uncertainties in a problem are usually modeled
by random variables, with each combination of values taken by such variables
 sometimes called a scenario  corresponding to a possible
outcome. Of course, one cannot expect to make a decision that will be optimal
regardless of the outcome; rather, it is desirable to make a decision that
optimizes some sensible performance measure. For example, the goal may be to
protect oneself against a "worstcase scenario.'' Another possibility is
to find a solution that is optimal "on the average.'' Risk  often
measured by
variance or statistical percentiles  is another common measure.
Optimization problems under uncertainty (also called stochastic
optimization problems) have been studied since
the 1950's; however, it was not until recently that computer power allowed
for the solution of realistic problems in reasonable time. Since then, many
models and corresponding solution techniques have been
developed, with applications in a variety of subjects. Despite all the
advances in the area, many issues remain to be addressed.
One such issue concerns the development of numerical methods that can be implemented to solve practical problems. In
particular, the introduction of more uncertainty factors to make the models
more realistic poses obvious computational difficulties, as the number of
possible scenarios grows. As a very simple example, consider a model with n independent random variables, each with two
possible alternatives; the total number of scenarios is thus 2^{n},
and so even for moderate values of n it
becomes impractical to take all possible outcomes into account. In such
cases, sampling techniques are a natural tool to be
used. However, since sampling only provides an approximation, it is
necessary to study the impact of its use and to develop optimization methods
that can incorporate sampling in an appropriate way.
In many situations, one desires the find the solution that gives the best
value on the average. Oftentimes, however, it is very important to measure
and control the risk of the
decision being made. There are many ways to model
risk, and there has been considerable activity in the research community to
develop optimization models that can take risk into account.
One class of such models is defined by problems where the constraints are
modeled using the notion of stochastic dominance, which conveys the
preferences of an arbitrary decisionmaker who is riskaverse. One advantage
of such formulation is that it does not require the knowledge of the decision
maker utility function, which is a common approach to incorporate risk
management into optimization. The concepts of stochastic dominance have been used for many years, particularly in Economics,
but recently they have been incorporated into optimization.
The incorporation of risk into optimization models
becomes more challenging when there is a dynamic component. There are many issues that arise in that context  for
example, there is no standard way of even formulating the problem, as there
are different ways of measuring risk.
The core of my research lies in the development of
theory and algorithms for optimization problems under uncertainty. Sampling
and simulation techniques play a central role in my studies. I am
particularly interested in the use of alternative sampling approaches  for
example, the socalled QuasiMonte Carlo methods  in that context. More recently I have been focusing on the development of theory
and algorithms for dynamic decision problems. Another topic I have been
working on deals with datadriven problems, through the study of algorithms
that optimize a system based on increasing availability of data. Finally, I
work on application problems where such methods can be used, such as in
transportation and energy.
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TEACHING
 Foundation of Operations Management (undergraduate course)
 Quantitative Analysis (Exceutive MBA
course)
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GRANTS (as
Principal Investigator)
 Project: "Models and Strategies for MultiStage Stochastic
Programs with Risk Control"
CoPI: Bernardo Pagnoncelli (UAI)
Funding source: FONDECYTChile
Date: March, 2012 until February 2016
 Project: "Optimization Algorithms for Problems with
Stochastic Dominance Constraints"
CoPI: Sanjay Mehrotra (Northwestern)
Funding source: National Science Foundation
Date: September, 2007, through August, 2010
 Project: "Model Accuracy and Learning in Revenue Management
and Dynamic Pricing"
CoPIs: William Cooper (University of Minnesota) and Anton Kleywegt
(Georgia Tech)
Funding source: National Science Foundation
Date: June, 2007, through June, 2010
 Project: "Improved Operations at Coyote Logistics: Solving
the Network"
CoPI: Karen Smilowitz (Northwestern)
Funding source: Coyote Logistics
Date: April, 2008, through March, 2009
 Project: "Yield Management Opportunities at Carry
Transit"
CoPIs: Mark Daskin and Karen Smilowitz (Northwestern)
Funding source: Superior Bulk Logistics
Date: January, 2007, through December, 2008
 Project: "Yield Management Opportunities at Carry
Transit"
CoPIs: Mark Daskin and Karen Smilowitz (Northwestern)
Funding source: Seed Grant award, provided by the Transportation Center
at Northwestern
Date: June, 2007, through September, 2007
 Project: "Stochastic Optimization for Revenue
Management"
CoPI: William Cooper (University of Minnesota)
Funding source: National Science Foundation
Date: October, 2001, through September, 2005
 Project: "Periodic Transportation Scheduling under
Uncertainty"
Funding source: Seed Grant award, provided by The Ohio State University
Date: January, 1999, through December, 1999
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UNIVERSITY
COLLABORATORS
Alexander Shapiro, Georgia
Institute of Technology (PhD advisor)
Javiera Barrera, Universidad Adolfo Ibanez
Lijian Chen, University of Louisville
William Cooper, University of Minnesota
Mark Daskin, Northwestern University
Jian Hu, Northwestern University
Anton Kleywegt, Georgia Institute of Technology
Jane Lin, University of Illinois at Chicago
Jeff Linderoth, University of Wisconsin
Sanjay Mehrotra, Northwestern University
Eduardo Moreno, Universidad Adolfo Ibanez
Marco Nie, Northwestern University
Bernardo Pagnoncelli, Universidad Adolfo Ibanez
Reuven Rubinstein, Technion,
Israel
Karen Smilowitz, Northwestern University
Leilei Zhang, Iowa State University
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PUBLICATIONS
Click here for a list.
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AWARDS
 Best Applied
Paper prize in Operations Engineering
and Analysis, awarded by the journal IIE Transactions (shared with coauthors Jian Hu and Sanjay
Mehrotra), 2013.
 INFORMS Revenue Management and Pricing
Section Prize for Best Paper (shared with coauthors William L. Cooper
and Anton Kleywegt), 2007.
 Meritorious
Service Award, awarded by the journal Operations
Research, 2005.
 Meritorious
Service Award, awarded by the journal Operations
Research, 2004.
 Winner of the
1998 George Nicholson Student Paper Competition (organized by INFORMS).
 Outstanding
Ph.D. student award, Georgia Institute of Technology, 1998.
 Doctoral
scholarship from CNPq (Brazilian
government science agency), 19931998.
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LINKS
Societies:
Stochastic Programming pages:
Other sites:
PERSONAL LINKS
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