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. Feb. 1, 2018New funding for domestic students

    The Faculty of Mathematics and the University of Waterloo’s Graduate Studies and Postdoctoral Affairs have developed a new funding initiative for full-time domestic graduate students. Worth more than half a million dollars, this initiative will result in new scholarship opportunities for domestic* PhD students, as well as additional entrance and travel awards for domestic research Master’s students.

    More precisely, the following new funding opportunities are being made available for full-time domestic students:

  2. Jan. 11, 2018It is with great pleasure that the Department of Statistics and Actuarial Science at the University of Waterloo welcomes Assistant Professor Audrey Beliveau as of January 1st 2018.Audrey Beliveau

    BELIVEAU, Audrey (PhD 2016, Simon Fraser University) comes to us from a Post-Doctoral position at the University of British Columbia. Her research interests include survey sampling, meta-analysis and applications in ecology and epidemiology. More specifically she has a number of interdisciplinary research collaborations with fisheries biologists. With her interest and experience with applications and her research focus, Audrey complements the department’s existing strength in biostatistics and greatly expands our scope for ecology related statistical research.

  3. Jan. 9, 2018Undergraduate Student Research Award (USRA) opportunity at the Royal Military College of Canada, Kingston ON, May to August 2018

    Supervisor:  Dr. Mohan Chaudhry

    Project Title: Inverting transforms that arise in the study of Markov models

    Many of the analytic solutions in queueing and other stochastic processes are derived in various transforms such as probability generating functions and Laplace transforms. The problems become more complicated if there are unknowns in the transforms. Several complicated algorithms/methods have been proposed to invert such transforms. We have developed a software program which inverts such transforms using the roots of high degree polynomials and transcendental functions. Our method of inverting such transforms is much more efficient and fast when compared with other methods.

    Student's role: The student's role will be to invert such transforms using mathematical tools such as MAPLE/MATLAB or MATHEMATICA and QROOT, a software developed by us as well as do some mathematical typing.

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  1. Mar. 22, 2018Department seminar by Foivos Xanthos, Ryerson University

    Dual representations of risk measures on Orlicz spaces

    The standard theory of risk measures, developed for bounded positions, asserts that any coherent risk measure with the Fatou property can be represented as the worst expectation over a class of probabilities. In this talk, we will discuss possible extensions of this result when the space of financial positions is taken to be an Orlicz space. We show that the representation fails in general and remains valid if the risk measure possess additional properties (e.g., law-invariance, strong Fatou property).

  2. Mar. 29, 2018Department seminar by Oksana Chkrebtii, The Ohio State University

    Probability models for discretization uncertainty with adaptive grid designs for systems of differential equations

    When models are defined implicitly by systems of differential equations without a closed form solution, small local errors in finite-dimensional solution approximations can propagate into large deviations from the true underlying state trajectory. Inference for such models relies on a likelihood approximation constructed around a numerical solution, which underestimates posterior uncertainty. This talk will introduce and discuss progress in a new adaptive formalism for modeling and propagating discretization uncertainty through the Bayesian inferential framework, allowing exact inference and uncertainty quantification for discretized differential equation models.  

  3. May 4 to 6, 2018ASA DataFest 2018Datafest Logo

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Meet our people

Mu Zhu

Mu Zhu


Contact Information:
Mu Zhu

Mu Zhu personal website

Research interests

Mu's main research interests are statistical machine learning and multivariate analysis, with their applications in health informatics, bioinformatics, and data mining. He is currently working with his PhD students on problems having to do with transactional networks and protein structures.