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. 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!

  2. 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.

  3. July 29, 2019The Department of Statistics and Actuarial Science is pleased to welcome Assistant Professor Aukosh Jagannath as of July 1, 2019.
    Aukosh Jagannath

    Aukosh Jagannath holds a PhD in Mathematics from the Courant Institute at New York University from 2016. Since then he has been an NSF mathematical sciences postdoctoral fellow at the University of Toronto and Harvard University as well as a Benjamin Pierce Fellow at Harvard University. His research interests are in probability and analysis and their applications to statistical physics, combinatorial optimization, the mathematics of data science, and high-dimensional statistics. Aukosh will help develop a stronger theoretical foundation for data science and expand our links with other departments and faculties at Waterloo.

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  1. Aug. 22, 2019Department seminar by Dr. Yan Yuan, School of Public Health, University of Alberta

    Development and Application of A Measure of Prediction Accuracy for Binary and Censored Time to Event Data

    Clinical preventive care often uses risk scores to screen population for high risk patients for targeted intervention. Typically the prevalence is low, meaning extremely unbalanced classes. Positive predictive value and true positive fraction have been recognized as relevant metrics in this imbalanced setting. However, for commonly used continuous or ordinal risk scores, these measures require a subjective cut-off threshold value to dichotomize and predict class membership. In this talk, I describe a summary index of positive predictive value (AP) for binary and event time outcome data. Similar to the widely used AUC, AP is rank based and a semi-proper scoring rule. We also study the behavior of incremental values of AUC, AP and the strict proper scoring rule scaled Brier score (sBrier) when an additional risk factor Z is included. It is shown that the incremental values agreement between AP and sBrier increases as the class unbalance increases, while the agreement between AUC and sBrier decreases as class unbalance increases. Under certain configurations, the changes in AP and sBrier indicate worse prediction performance when Z is added to the risk profile, while the changes in AUC are almost always favor the addition of Z. Several real world examples are used throughout the talk to illustrate and contrast these metrics.

  2. 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.

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

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

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Meet our people

Fan Yang

Fan Yang

Assistant Professor

Contact Information:
Fan Yang

Research interests

Fan Yang’s research interests lie in the areas of quantitative risk management, actuarial science and mathematical finance.