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Ljiljana Trajkovic
Simon Fraser University


Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, in 1974,  the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, in 1979 and 1981, respectively, and the Ph.D. degree in electrical engineering from University of California at Los Angeles, in 1986.

She is currently a Professor in the School of Engineering Science at Simon Fraser University, Burnaby, British Columbia, Canada. From 1995 to 1997, she was a National Science Foundation (NSF) Visiting Professor in the Electrical Engineering and Computer Sciences Department, University of California, Berkeley.  She was a Research Scientist at Bell Communications Research, Morristown, NJ, from 1990 to 1997, and a Member of the Technical Staff at AT&T Bell Laboratories, Murray Hill, NJ, from 1988 to 1990. Her research interests include high-performance communication networks, control of communication systems, computer-aided circuit analysis and design, and theory of nonlinear circuits and dynamical systems.

Dr. Trajkovic serves as President-Elect (2013) and Vice President Publications of the IEEE Systems, Man, and Cybernetics Society (2012-2014 and 2010-2011) and served as Vice President Long-Range Planning and Finance (2008-2009) and as a Member at Large of its Board of Governors (2004-2008). She served as 2007 President of the IEEE Circuits and Systems Society. She was a member of the Board of Governors of the IEEE Circuits and Systems Society (2001-2003 and 2004-2005). She is Chair of the IEEE Circuits and Systems Society joint Chapter of the Vancouver/Victoria Sections. She was Chair of the IEEE Technical Committee on Nonlinear Circuits and Systems (1998). She was Technical Program Co-Chair of ISCAS 2005 and served as Technical Program Chair and Vice General Co-Chair of ISCAS 2004. She served as an Associate Editor of the IEEE Transactions on Circuits and Systems (Part I) (2004-2005 and 1993-1995), the IEEE Transactions on Circuits and Systems (Part II) (2002-2003 and 1999-2001), and the IEEE Circuits and Systems Magazine (2001-2003). She was a Distinguished Lecturer of the IEEE Circuits and Systems Society (2010-2011 and 2002-2003) and a Fellow of the IEEE.

Understanding Communication Networks

Understanding modern data communication networks such as Internet involves analysis of data collected from deployed networks and characterization and modeling of network traffic. It also calls for development of tools for analysis of Internet topologies and performance evaluation of routing protocols.

This talk describes collection and analysis of real-time traffic data using special purpose hardware and software tools. Analysis of collected datasets indicates a complex underlying network infrastructure that carries a variety of the Internet applications. Furthermore, data collected from the Internet routing tables can be used to illustrate the existence of historical trends in the development of the Internet.

Machine Learning Models for Feature Selection and Classification of Traffic Anomalies


Traffic anomalies in in communication networks greatly degrade network performance. The lecture surveys statistical and machine learning techniques that are used to classify and detect network anomalies such as Internet worms that affect performance of routing protocols. Various classification features are used to design anomaly detection mechanisms. They are used to classify test datasets and identify the correct anomaly types.

Intelligent Internet Systems

Reinforcement learning-based algorithms have been proposed for routing in computer networks. These algorithms have not been widely implemented in deployed networks where the inherent random nature of the reinforcement learning algorithms is not desired. However the randomness of these algorithms is sought-after in certain cases such as deflection routing.

The presentation introduces a predictive Q-learning-based deflection routing algorithm that may be employed in buffer-less networks. The algorithm is implemented and tested using the ns-3 network simulator. Simulation results show that the proposed algorithm decreases the packet loss probability while it requires fewer deflections compared to an existing reinforcement learning deflection routing scheme.
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