Welcome to the Department of Statistics and Actuarial Science

The Department of Statistics and Actuarial Science is among the top academic units for statistical and actuarial science in the world and is home to more than 40 research active full-time faculty working in diverse and exciting areas. The Department is also home to over 900 undergraduate students and about 150 graduate students in programs including Actuarial Science, Biostatistics, Quantitative Finance, Statistics, and Statistics-Computing.

We are located on University of Waterloo main campus, which is located at the heart of Canada's Technology Triangle about 100 kilometers west of Toronto.

  1. Nov. 19, 2018The Vector Institute recognizes MMath Data Science for Artificial Intelligence Scholarship Vector Institute logo
    Source: Vector Institute for Artificial Intelligence

    There are currently eight programs recognized by the Vector Institute for Artificial Intelligence for ensuring that master’s graduates are equipped with the knowledge, skills and competencies sought by industry.  The Department of Statistics and Actuarial Science's Master of Mathematics (MMath) Data Science Specialization is one of the few recognized programs. 

    The Vector Institute (Vector) has been tasked with supporting Ontario’s growing AI eco-system by aiding Ontario’s AI companies and labs source talent. This will be achieved through a combination of three primary activities: talent placement via internships; networking and AI community building; and, applied AI education.

    A primary goal associated with applied AI education is to increase enrolment and the number of graduates from AI-related master’s programs. To support universities in achieving this goal, Vector has introduced the Vector Scholarships in Artificial Intelligence (VSAI) to recognize top students enrolled in AI-related master’s programs. 

    The VSAI are expected to enhance recruitment of top tier students, increase the number of applicants to recognized AI programs, increase access to advanced studies in AI, and build community among scholars.

  2. Nov. 14, 2018Carbon emissions will start to dictate stock prices

    Companies that fail to curb their carbon output may eventually face the consequences of asset devaluation and stock price depreciation, according to a new study out of the University of Waterloo.

    The researchers further determined that the failure of companies within the emission-intensive sector to take carbon reduction actions could start negatively impacting the general stock market in as little as 10 years’ time.

  3. Oct. 19, 2018Undergraduate Student Research Awards (USRA)

    DEADLINE for Winter 2018 is October 19, 2018

    The Undergraduate Student Research Awards (USRA) are sponsored by the Natural Sciences and Engineering Research Council (NSERC) of Canada.  The Department of Statistics and Actuarial Science has 6 awards available for Winter 2019 term.

    This award allows you to conduct research in a university environment full time for a term. The goal is to stimulate your interest in research and to encourage students to enroll in graduate studies in Statistics and Actuarial Sciences.  If you would like to gain research work experience that complements your studies in an academic setting, this award provides you with financial support.

    Continue reading on the USRA page

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  1. Nov. 16, 2018Department seminar by Mehdi Molkaraie, ETH Zurich

    The Ising model: series expansions and new algorithms

    We propose new and simple Monte Carlo methods to estimate the partition function of the Ising model. The methods are based on the well-known series expansion of the partition function from statistical physics. For the Ising model, typical Monte Carlo methods work well at high temperature, but fail in the low-temperature regime. We demonstrate that our proposed Monte Carlo methods work differently: they behave particularly well at low temperature. We also compare the accuracy of our estimators with the state-of-the-art variational methods.

  2. Nov. 21, 2018Graduate Student Seminar by Danqiao Guo

    Eigen Portfolio Selection: A Robust Approach to Sharpe Ratio Maximization


    We show that even when a covariance matrix is poorly estimated, it is still possible to obtain a robust maximum Sharpe ratio portfolio by exploiting the uneven distribution of estimation errors across principal components. This is accomplished by approximating an investor’s view on future asset returns using a few relatively accurate sample principal components. We discuss two approximation methods. The first method leads to a subtle connection to existing approaches in the literature; while the second one is novel and able to address main shortcomings of existing methods. 

      

    ** Pizza & refreshments will be provided **

    Everyone welcome!

  3. Nov. 22, 2018Department seminar by Dr. Bei Jiang, University of Alberta

    A Bayesian Approach to Joint Modeling of Matrix-valued Imaging Data and Treatment Outcome with Applications to Depression Studies


    In this talk, we discuss a unified Bayesian joint modeling framework for studying association between a binary treatment outcome and a baseline matrix-valued predictor. Specifically, a joint modeling approach relating an outcome to a matrix-valued predictor through a probabilistic formulation of multilinear principal component analysis (MPCA) is developed. This framework establishes a theoretical relationship between the outcome and the matrix-valued predictor although the predictor is not explicitly expressed in the model. Simulation studies are provided showing that the proposed method is superior or competitive to other methods, such as a two-stage approach and a classical principal component regression (PCR) in terms of both prediction accuracy and estimation of association; its advantage is most notable when the sample size is small and the dimensionality in the imaging covariate is large. Finally, our proposed joint modeling approach is shown to be a very promising tool in an application exploring the association between baseline EEG data and a favorable response to treatment in a depression treatment study by achieving a substantial improvement in prediction accuracy in comparison to competing methods.

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Faculty Joint Publications

Map of Faculty and PhD Students backgrounds

Meet our people

Cecilia Cotton

Cecilia Cotton

Associate Professor

Contact Information:
Cecilia Cotton

Research interests

The underlying theme of my research interests is using longitudinal data to solve problems in public health:

Inference for comparing survival across multiple dynamic treatment regimens based on observational longitudinal data. Applications to epoetin dosing strategies for hemodialysis subjects with chronic kidney disease. Joint modeling of longitudinal and survival data in the context of causal inference.