Lilin Zhang

Ph.D. Candidate, M.Sc. (University of Toronto)

 
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PhD Degree, University of Toronto

Winter 2011

ECE1548 Advanced Network Architecture
The objective of this course is to present the key trends in the evolution of network architectures and the services and applications they support. Part I. Existing Architectures: Internet; LTE 4G architecture; Cloud computing; Apple and Google application platforms. Future Architectures: The Internet is broken; Future Internet proposals; Experimental networks and testbeds. Part II. Network Science Design Principles: Introduction to graph theory and optimization; Flow and capacity assignment; Topology design; Robustness and adaptation; Networked markets; Pricing strategies and market power. Part III. Designing Future Networks: Virtual networks; Green networks; Datacenters and computing clouds; Smart grids.

Winter 2011

ECE1771 Quality of Service
This course aims to present a collection of fundamental design principles and guidelines in modern distributed systems and real-world large-scale networks. In the process, we review a small collection of important research results, not only in the recent literature but also in the literature spanning the past two decades, and see how they reflect fundamental design principles that we have discussed. Our focus is on more recent research literature, in the areas that have been studied extensively: multimedia networking, peer- to-peer networks, as well as multi-hop wireless networks. We start with an examination of our design objectives, including Quality of Service. We then introduce a number of fundamental design principles that may lead to a high-quality design. Subsequently, we take a leisure walk through more specific areas of research, spanning peer- to-peer networks, wireless mesh networks, secure protocols, so-called "killer" applications, as well as recent advances in network coding. Throughout the course, we revisit the design principles often, and see how they affect the successes (or failures) of research ideas.

Fall 2010

ECE1502 Information Theory
This course provide a comprehensive coverage of the major areas of information theory, including Entropy and mutual information,Data compression,Channel capacity, Rate-distortion theory, Network information theory. It is a fundamental course for students interested in digital communications, data compression and signal processing.

Fall 2010

ECE1500 Stochastic Processes
This course provides the Probability Theory Overview, including axioms of probability, repeated trials, conditional probability, random variables, characteristic functions; Sequence of Random Variables,including joint statistics, conditional statistics, stochastic convergence, law of large numbers, central limit theorem. The course also covers the topics of Stochastic Processes, including the definitions and interpretations, classical examples, statistics, ergodicity, Fourier series and Karhunen-Loève expansion; Mean Square Estimation, including linear mean square estimation, geometric interpretation, Wiener filtering. The course further covers the topics of Discrete-Time Markov Chain, including classification, Chapman-Kolmogorov equations, stationary distribution, mean absorption time, branching; Continuous-Time Markov Chain, including transition rates, birth-death processes.

Fall 2010

CSC2600 Convex Optimization
This course provides a comprehensive coverage of the theoretical foundation and numerical algorithms for convex optimization with engineering applications. Topics include: convex sets and convex functions; convex optimization problems; least-square problems; optimal control problems; Lagrangian duality theory. Karush-Kuhn-Tucker (KKT) theorem; Slater.s condition; generalized inequalities; minimiax optimization and saddle point; introduction to linear programming, quadratic programming, semidefinite programming and geometric programming; numerical algorithms: descent methods, Newton.s method, interior-point method; convex relaxation; applications to communications and signal processing.

Master's Degree, University of Toronto

Winter 2009

CSC2305 Numerical Methods for Optimization Problems

Winter 2009

CSC2410 Introduction to Graph Theory

Fall 2008

CSC2206 Computer Systems Modelling

Fall 2008

CSC 2401 Introduction to Computational Complexity

Fall 2008

CSC2515 Introduction to Machine Learning

 
Last Updated: April 2014