Welcome to the Department of Statistics and Actuarial Science

The Department of Statistics and Actuarial Science is a top tier academic unit among statistical and actuarial science globally. Our students and faculty explore topics such as Actuarial Science, Biostatistics, Data Science, Quantitative Finance, Statistics, & Statistics-Computing. Our department is home to:

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full-time faculty researching diverse and exciting areas

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undergraduate students from around the world

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 graduate students in Master, Doctoral, and professional programs

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  1. Aug. 23, 2019Speed identified as the best predictor of car crashes

    Speeding is the riskiest kind of aggressive driving, according to a unique analysis of data from on-board devices in vehicles.

    Researchers at the University of Waterloo examined data from 28 million trips for possible links between four bad driving behaviours – speeding, hard braking, hard acceleration and hard cornering – and the likelihood of crashes.

    Read the full article on the Math News site.

  2. Aug. 16, 2019Congratulations Mario Ghossoub and Mirabelle Huynh, for earning their designation of Fellow of the Society of Actuaries
    Mario Ghossoub and Mirbelle Hyunh

    The Department of Statistics and Actuarial Science would like to highlight the recent achievements of two of its department members, Mario Ghossoub and Mirabelle Huynh, for earning their designation of Fellow of the Society of Actuaries.

    Congratulations to the two of them for this extraordinary achievement!

  3. Aug. 2, 2019Waterloo READI-ly helping Indonesia meet its actuarial needs
    Actuarial Students in Indonesia

    The University of Waterloo, which is among the top universities in the world for actuarial science and number one in co-operative learning, has combined these two things to help build a stronger insurance and pension industry in Indonesia.

Read all news
  1. Sep. 12, 2019Department seminar by Rodney Sparapani, Medical College of Wisconsin   

    Nonparametric failure time with Bayesian Additive Regression Trees

    Bayesian Additive Regression Trees (BART) is a nonparametric machine learning method for continuous, dichotomous, categorical and time-to-event outcomes.  However, survival analysis with BART currently presents some challenges.  Two current approaches each have their pros and cons.  Our discrete time approach is free of precarious  restrictive assumptions such as proportional hazards and Accelerated Failure Time (AFT), but it becomes increasingly computationally demanding as the sample size increases.  Alternatively, a Dirichlet Process Mixture approach is computationally friendly, but it suffers from the AFT assumption.  Therefore, we propose to further nonparametrically enhance this latter approach via heteroskedastic BART which will remove the restrictive AFT assumption while maintaining its desirable computational properties.

  2. Sep. 13, 2019Department seminar by Silvana Pesenti, University of Toronto

    More information about this seminar will be added as soon as possible.

  3. Sep. 19, 2019Department seminar by Enlu Zhou, Georgia Institute of Technology

    More information about this seminar will be added as soon as possible.

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Meet our people

Tony Wirjanto

Tony Wirjanto


Contact Information:
Tony Wirjanto

Tony Wirjanto's personal website

Research interests

Professor Wirjanto's research interests focus on developing statistical methodology for applications in the finance area. His work has centered on the modeling of volatility fluctuations in financial returns with applications to asset and derivatives pricing, portfolio selection, and the term structure of interest rates. His current work explores the use of finite mixtures of distributions as well as ultra high-frequency data for volatility forecasting, portfolio choice and financial risk management.