Analysis and Optimizations of Stochastic Networks
The behavior of a distributed system or a network is subject to many irregularities and stochastic
fluctuations. Our success in solving a variety of inference and optimization tasks defined over such systems depends
heavily on our ability to adequately model, reason about and learn such a behavior.
Many existing stochastic models for complex systems and algorithms for
their probabilistic analysis build upon the assumption of full independence;
the condition that permits efficient probabilistic analysis but that is, at the same time,
frequently violated in realistic world settings with
intricate stochastic dependencies and interactions among components
of the system. A simple analysis of realworld network systems (such
as power grids, communication or transportation networks) reveals
that situations with more failures occurring at the same time are
more likely than in the model with failure independence in which an
occurrence of a larger number of failures tends to be a very
unlikely event. An example is August 2003 power blackout that
affected large areas of Eastern USA and involved a cascade of
interacting failures. Under the condition of failure independence
the probability of such an event would be extremely small. The aim of our
work is to develop probabilistic models of stochastic behaviors of complex distributed systems that
overcome this difficulty, offer good approximations of true behavior of the system,
and at the same time support efficient inferences and learning.
 Probabilistic models of large distributed systems, their efficient inference and learning.
 Resource optimizations for unreliable networks.
 Applications to traffic modeling, traffic flow optimizations and accident detection

Modeling of unreliable networks and noisyor component analysis

Resource optimizations in unreliable networks:

Traffic modeling and accident detection
 T. Singliar, and M. Hauskrecht.
Learning to detect incidents from noisily labeled data.
Machine Learning Journal, September 2009.
 T. Singliar, M. Hauskrecht.
Approximation strategies for routing in stochastic dynamic
networks.
In Proceedings of the Tenth International Symposium on Artificial Intelligence and Mathematics , Ft. Lauderdale, FL, January 2008.
 T. Singliar and M. Hauskrecht.
Modeling Highway Traffic Volumes.
Proceedings of Eighteen Europian Conference on Machine Learning (ECML), 2007.
 T. Singliar and M. Hauskrecht.
Learning to detect
traffic incidents from imperfectly labeled data.
Proceedings of Eleventh International
Conference on Principles of Knowledge Discovery in Databases, , pp. 236247
2007.
 T. Singliar and M. Hauskrecht.
Towards a learning traffic incident detection system.
ICML 2006 Workshop on Machine Learning Algorithms for Surveillance and Event
Detection, Pittsburgh, June 2006.

SCITI project publications
 D. Mosse, L. Comfort, A. Labrinidis, A. Amer, J. Brustoloni, P. Chrysanthis, M. Hauskrecht, T. Znati, R. Melhem, K. Pruhs.
SecureCITI Project Highlights.
Featured in the 7th Annual International Conference on Digital Government Research (dg.o 2006)
San Diego, CA, May 2006.
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