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 about 50 research active full-time faculty working in diverse and exciting areas. The Department is also home to around 1000 undergraduate students and about 175 graduate students in programs including Actuarial Science, Biostatistics, Quantitative Finance, Statistics, and Statistics-Computing.

Aerial View of MC & M3

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

  1. June 20, 2019READI project gets set to go on co-op education in Indonesia
    Representatives from Indonesia's education ministry, Global Affairs Canada and the READI project

    The Risk Management, Economic Sustainability and Actuarial Science (READI) Project has helped launch a government initiative launched in April that regulates the implementation of co-operative education in Indonesia.

    This initiative is part of the work that READI, housed in Waterloo’s Department of Statistics and Actuarial Science, has been undertaking in Indonesia.

    Read the full article on the Daily Bulletin

  2. June 19, 2019Congratulations Chao Qi (George) Li, winner of the 2019 Samuel Eckler Medal in Actuarial Science
    Eckler Winner - Chao Qi (George) Li

    The Department of Statistics and Actuarial Science is pleased to announce Chao Qi (George) Li as the winner of the Samuel Eckler Medal in Actuarial Science.

    Award description: 

    This prize was established to recognize the contribution of Samuel Eckler to the actuarial profession and is provided by Eckler Partners. The medal, which is cast in gold, is awarded each year to the outstanding graduating student in Honours Actuarial Science.

    "In addition to his outstanding performance in actuarial science, George demonstrated great potential in statistics, his performance in my upper year biostatistics course was phenomenal!"

    LEILEI Zeng, Associate Professor, Department of Statistics and Actuarial Science

  3. June 13, 2019Congratulations Mirabelle Huynh & Wayne Oldford, winners of the annual Department of Statistics and Actuarial Science Teaching Award!
    Mirabelle and Wayne smiling

    The Department of Statistics and Actuarial Science is proud to announce the winners of the 2019 Teaching Award goes to Mirabelle Huynh and Wayne Oldford!

    "Thank you for your commitment to and excellence in teaching"

    Stefan Steiner, Chair, Department of Statistics and Actuarial Science


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  1. July 11, 2019Department seminar by J. N. K. Rao, Carleton University

    On making valid inferences by combining data from multiple sources: An appraisal

    National statistical agencies have long been using probability samples from multiple sources in conjunction with census and administrative data to make valid and efficient inferences on population parameters of interest, leading to reliable official statistics. This topic has received a lot of attention more recently in the context of decreasing response rates from probability samples and availability of data from non-probability samples and in particular “big data”. In this talk, I will discuss some methods, based on models for the non-probability samples, which could lead to useful inferences when combined with probability samples observing only auxiliary variables related to a variable of interest.  I will also explain how big data may be used as predictors in small area estimation and comment on using non-probability samples to produce “real time” official statistics.

  2. Aug. 13, 2019Department seminar by Dr. Pavel Krupskiy, University of Melbourne

    Spatial Cauchy processes with local tail dependence

    We study a class of models for spatial data obtained using Cauchy convolution processes with random indicator kernel functions. We show that the resulting spatial processes have some appealing dependence properties including tail dependence at smaller distances and asymptotic independence at larger distances. We derive extreme-value limits of these processes and consider some interesting special cases. We show that estimation is feasible in high dimensions and the proposed class of models allows for a wide range of dependence structures.

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Meet our people

Christopher Small

Christopher Small


Contact Information:
Christopher Small

Christopher Small's personal website

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.