Welcome to the Office of Graduate Studies & Postdoctoral Fellows

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Our internationally recognized graduate programs will expand your intellectual horizons, strengthen your skill set and enhance your professional network.

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  • Lin Liu, MSc Thesis Defence, Computing Science
    10:30 AM - 12:15 PM
    June 3, 2015
    M.SC. THESIS DEFENCE Lin Liu M.Eng., Beijing University of Posts and Telecommunications, China, 2013 B.Eng., Minzu University of China, China, 2010 Wednesday, June 3 rd , 2015 10:30 a.m. TASC1 9204 West Title A NEW METHOD FOR OUTLIER DETECTION ON TIME SERIES DATA Abstract Time series outlier detection has been attracting a lot of attention in research and application. In this thesis, we introduce the new problem of detecting hybrid outliers on time series data. Hybrid outliers show their outlyingness in two ways. First, they may deviate greatly from their neighbors. Second, their behaviors may also be different from that of their peers in other time series. We propose a framework to detect hybrid outliers, and two algorithms based on the framework are developed to show the feasibility of our framework. An extensive empirical study on both real data and synthetic data verifies the effectiveness and efficiency of our algorithms. M.Sc. Examining Committee: Dr. Jian Pei, Senior Supervisor Dr. Qianping Gu, Supervisor Dr. Jiangchuan Liu, Examiner Dr. Arrvindh Shriraman, Chair
  • Noor Fageh, MSc Thesis Defence, Biological Sciences
    2:00 PM - 4:00 PM
    June 3, 2015
    Senior Supervisor: Dr. Francis Law Thesis Title: Using a panel of in vitro yeast screening bioassays to assess endocrine disrupting chemical contents in water and sediment samples from Surrey and Langley, British Columbia
  • Li Xiong, MSc Thesis Defence, Computing Science
    10:30 AM - 12:15 PM
    June 4, 2015
    M.SC. THESIS DEFENCE Li Xiong B.Eng., Sichuan University, China, 2013 Thursday, June 4 th , 2015 10:30 a.m. TASC1 9204 West Title EXPLORING THE POWER OF FREQUENT NEIGHBORHOOD PATTERNS ON EDGE WEIGHT ESTIMATION Abstract Since links on social networks model a mixture of many factors, such as acquaintances and friends, the problem of link strength prediction arises: given a social tie e=(u,v) in a social network, how strong the tie e is? Previous work tackles this problem mainly by node profile-based methods, i.e., utilizing users' profile information. However, some networks do not have node profiles. In this thesis, we study a novel problem of exploring the power of frequent neighborhood patterns on edge weight estimation. Given a labeled graph, we estimate its edge weights by applying its structural information as features. We develop an efficient pattern-growth based mining algorithm to mine frequent neighborhood patterns as features to estimate edge weights. Our experimental results on two real datasets show the efficiency of our method and the effectiveness of the frequent neighborhood pattern based features. M.Sc. Examining Committee: Dr. Jian Pei, Senior Supervisor Dr. Joseph Peters, Supervisor Dr. Ramesh Krishnamurti, Examiner Dr. Binay Bhattacharya, Chair
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