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. May 16, 2018Students win $25,000 at the Waterloo Data OpenWinning teams with the judges and Correlation One and Citadel representatives

    Photography by Jon White

    Over 100 undergraduate and graduate students gathered in Mathematics 3 early Saturday morning to tackle large datasets at The Data Open, a competition that brings together the best minds in mathematics, engineering, science and technology to collaborate and compete using the world’s most important data sets. Students received the data sets at 8:00 a.m. and, in teams of three to four, had until 3:30 p.m. to analyze the data, extract meaningful insights, and propose solutions to a socially impactful problem.

  2. May 9, 2018Celebrating 2nd annual DataFest competition at the University of WaterlooDataFest Participants

    While challenging, DataFest 2018 was an incredibly rewarding experience that taught us about the nuances of real world data, resilience and the power of team work

    Yuan Yuan Mandy Gu, Statistics and Pure Math student. Winner of the 'Munich Re Best Insight' award

    Over 100 undergraduate students spent 48 hours on campus analyzing and applying data as they competed in the 2018 ASA DataFest competition this past weekend. Worldwide, more than 2,000 students participate in this competition at several of the most prestigious colleges and universities.

    For two consecutive days, students worked around the clock to put their data analysis skills to the test with more complex data than what they would normally be exposed to in class. Once the data is analyzed, groups had only two slides and five minutes to convince the judges that the conclusions they drew from the data were deserving of one of three titles: Munich Re Best Insight, Best Use of External Data, or Best Data Visualization.

  3. May 8, 2018Congratulations Mary Hardy & Greg Rice, winners of the annual Department of Statistics and Actuarial Science Teaching Award!Mary Hard and Greg Rice

    The Department of Statistics and Actuarial Science is proud to announce the winners of the 2018 Teaching Award goes to Mary Hardy & Greg Rice

    “You (Greg and Mary) have over many years demonstrated excellence in teaching that has been greatly appreciated by both your students and colleagues.”

    Stefan Steiner, Chair, Department of Statistics and Actuarial Science
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  1. May 24, 2018Department seminar by Steffen Klaere, University of Auckland

    Does your phylogenetic tree fit your data?

    Phylogenetic methods are used to infer ancestral relationships based on genetic and morphological data. What started as more sophisticated clustering has now become a more and more complex machinery of estimating ancestral processes and divergence times. One major branch of inference is maximum likelihood methods. Here, one selects the parameters from a given model class for which the data are more likely to occur than for any other set of parameters of the same model class. Most analysis of real data is executed using such methods.

    However, one step of statistical inference that has little exposure to application is the goodness of fit test between inferred model and data. There seem to be various reasons for this behaviour, users are either content with using a bootstrap approach to obtain support for the inferred topology, are afraid that a goodness of fit test would find little or no support for their phylogeny thus demeaning their carefully assembled data, or they simply lack the statistical background to acknowledge this step.

    Recently, methods to detect sections of the data which do not support the inferred model have been proposed, and strategies to explain these differences have been devised. In this talk I will present and discuss some of these methods, their shortcomings and possible ways of improving them.

  2. June 25, 2018David Sprott Distinguished Lecture by Dr. Pauline Barrieu, London School of Economics and Political SciencePauline Barrieu

    Assessing financial model risk

    Model risk has a huge impact on any financial or insurance risk measurement procedure and its quantification is therefore a crucial step. In this talk, we introduce three quantitative measures of model risk when choosing a particular reference model within a given class: the absolute measure of model risk, the relative measure of model risk and the local measure of model risk. Each of the measures has a specific purpose and so allows for flexibility. We illustrate the various notions by studying some relevant examples, so as to emphasize the practicability and tractability of our approach.

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Meet our people

Christopher Small

Christopher Small


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Christopher Small

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Research interests

Professor Small's research interests are in statistical inference, including estimating functions; and some areas of statistical geometry, including the statistical analysis of shape. Recently, he has been working on a book on asymptotic techniques.