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. Feb. 28, 2018Department seminar by Hoora Moradian, University of Quebec at Montreal

    New developments in survival forests techniques

    Survival analysis answers the question of when an event of interest will happen. It studies time-to-event data where the true time is only observed for some subjects and others are censored. Right-censoring is the most common form of censoring in survival data. Tree-based methods are versatile and useful tools for analyzing survival data with right-censoring. Survival forests, that are ensembles of trees for time-to-event data, are powerful methods and are popular among practitioners. Current implementations of survival forests have some limitations. First, most of them use the log-rank test as the splitting rule which loses power when the proportional hazards assumption is violated. Second, they work under the assumption that the event time and the censoring time are independent, given the covariates. Third, they do not provide dynamic predictions in presence of time-varying covariates. We propose solutions to these limitations: We suggest the use of the integrated absolute difference between the two children nodes survival functions as the splitting rule for settings where the proportionality assumption is violated. We propose two approaches to tackle the problem of dependent censoring with random forests. The first approach is to use a final estimate of the survival function that corrects for dependent censoring. The second one is to use a splitting rule which does not rely on the independent censoring assumption. Lastly, we make recommendations for different ways to obtain dynamic estimations of the hazard function with random forests with discrete-time survival data in presence of time-varying covariates. In our current work, we are developing forest for clustered survival data.

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

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Meet our people

Liqun Diao

Liqun Diao

Assistant Professor

Contact Information:
Liqun Diao


Research Interests

Professor Diao’s research interests lie in developing and applying statistical methods to advance knowledge in medicine and public health. Her current research effort is mainly devoted to analysis of event history processes, methodology development in recursive partitioning and the interface between the two.