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David Shmoys obtained his Ph.D. in Computer Science
from the University of California at Berkeley in 1984. He has faculty appointments
in both the School of Operations Research and Industrial Engineering and
the Department of Computer Science at Cornell University. Dr. Shmoys’
research has focused on the design and analysis of efficient algorithms
for discrete optimization problems. His work has highlighted the central
role that linear programming plays in the design of approximation algorithms
for NP–hard problems. In particular, he is known for his results on
scheduling and clustering problems, including the first constant-performance
guarantees for several problems central to the literature, including the
k-center and k-median problems, the generalized assignment problem, as well
as scheduling problems in which the aim is to minimize the average job-completion
time. His work on polynomial-time approximation schemes for scheduling problems
introduced techniques that have subsequently been applied to a variety of
other settings. His current work includes the application of discrete optimization
techniques to several issues in computational biology.
For more information, see: http://www.orie.cornell.edu/orie/people/faculty/profile.cfm?netid=dbs10
Relevant publications:
Levi R, Pal M, Roundy R, Shmoys D (2005) Approximation Algorithms for Stochastic
Inventory Control Models". In Proceedings of the 11th MPS Conference
on Integer Programming and Combinatorial Optimization 306-320
Shmoys D, Swamy C (2004) Stochastic Optimization is (Almost) as Easy as
Deterministic Optimization". In Proceedings of the 45th IEEE Symposium
on Foundations of Computer Science 228-237
Vision T, Brown D, Shmoys D, Durret R, Tanksley S (2000) Selective Mapping:
A Strategy for Optimizing the Construction of High-Density Linkage Maps.
Genetics 155:407-429.
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