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

Distinguished Lecture by Paul Glasserman on October 11.       Paul Glasserman

David Sprott Distinguished Lecture by Xiao-Li Meng on Thursday October 17.  Xiao-Li Meng 

New Conference: Waterloo Student Conference in Statistics, Actuarial Science and FinanceGraduate student conference  Registration SpeakersScheduleAccommodations

  1. Oct. 11, 2019Interested in Actuarial Science?
    Angel Yang

    Learn about the program from Angel Yang, a fourth year student majoring in Actuarial Science at the University of Waterloo. In her interview with AdvisorSmith, Angel talks about why she chose to study at the University of Waterloo, her experience on campus, why a co-op program made sense to her, and much more. View the full interview online to see what she has to say.  

    If you are interested in pursuing Actuarial Science, Waterloo should be at the top of your list. The Department of Statistics and Actuarial Science (SAS) is considered a top tier academic unit in the field of Actuarial Science.  Our department has one of the world’s largest programs at both the undergraduate and the graduate levels. SAS offers professional masters programs as well as research-oriented masters and doctoral programs in actuarial science and finance. It is home to the bachelor of mathematics in actuarial science program which covers a wide range of courses, including full coverage of the material of the SOA/CAS associateship requirements and some coverage of the SOA/CAS fellowship requirements. With a sizeable actuarial faculty, the range of courses offered is broad and extends well beyond the SOA/CAS syllabi. Students may choose to gain foundational knowledge in life insurance, property and casualty insurance, pensions, risk theory, quantitative finance, and corporate finance. Students who are particularly interested in financial or predictive analytics topics may elect to add either option to their actuarial science honours plan. 

  2. Sep. 20, 2019Celebrating Mary Thompson's 50th anniversary in the Department of Statistics and Actuarial Science
    (From the left) Jerry Lawless, Jock Mackay, Mary Thompson, Winston Cherry, Steve Brown, Bovas Abraham, and Jack Robinson.

    On Wednesday, September 18, 2019, the Department of Statistics and Actuarial Science (SAS) celebrated the 50th anniversary of Professor Mary Thompson as a faculty member in the department. Many current faculty and staff and a number of retired professors gathered in the SAS Lounge and celebrated Mary’s milestone with cake, coffee and fruits. The Vice-President, Research & International, of University of Waterloo, Professor Charmaine Dean, who was a PhD graduate from the department, sent a beautiful bouquet to congratulate Mary on this special occasion. A couple retired faculty members turned the clock back and told stories about Mary during the early days of the department. Mary thanked the department for being her academic home for the past 50 years, and told the younger generation of faculty members that SAS is a “can-do” place to fulfill their full potential and aspiration.  

    Mary joined the department in 1969 as one of the first group of statistics faculty members, when the department was still in its infancy (established in 1967). Over the past half a century, Mary has become a fixture of the department, a highly accomplished scholar, and a great inspiration and role model for many students and young faculty members. She has provided dedicated services to the statistical community at all levels, including chair of the department, acting dean of the faculty, first scientific director of the Canadian Statistical Sciences Institute, and president of the Statistical Society of Canada.

  3. Sep. 16, 2019Phelim Boyle named Fellow of the Royal Society of Canada
    Phelim Boyle

    The Department of Statistics and Actuarial Science would like to congratulate Phelim Boyle, along with two other Faculty of Mathematics researchers, on being named a fellow of the Royal Society of Canada (RSC). They are among the seven University of Waterloo researchers to receive this honour and among 93 new fellows elected by their peers for outstanding scholarly, scientific, and artistic achievement across Canada.

    Phelim is a professor emeritus at Waterloo and a professor of business and economics and Wilfrid Laurier University. He is an actuary whose seminal research work in finance and insurance has won international recognition. He uses mathematical models to solve problems at the interface of these fields. Boyle has made pioneering contributions to quantitative finance and his ideas have transformed how actuaries handle financial risk. His research has influenced financial practice by providing sophisticated tools for financial institutions to better manage their risks.

    Read the full article on Math News.

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  1. Oct. 15, 2019Department seminar by Sebastian Engelke, University of Geneva

    Graphical Models and Structural Learning for Extremes

    Conditional independence, graphical models and sparsity are key notions for parsimonious models in high dimensions and for learning structural relationships in the data. The theory of multivariate and spatial extremes describes the risk of rare events through asymptotically justified limit models such as max-stable and multivariate Pareto distributions. Statistical modeling in this field has been limited to moderate dimensions so far, owing to complicated likelihoods and a lack of understanding of the underlying probabilistic structures.

    We introduce a general theory of conditional independence for multivariate Pareto distributions that allows to define graphical models and sparsity for extremes. New parametric models can be built in a modular way and statistical inference can be simplified to lower-dimensional margins. We define the extremal variogram, a new summary statistics that turns out to be a tree metric and therefore allows to efficiently learn an underlying tree structure through Prim's algorithm. For a popular parametric class of multivariate Pareto distributions we show that, similarly to the Gaussian case, the sparsity pattern of a general graphical model can be easily read of from suitable inverse covariance matrices. This enables the definition of an extremal graphical lasso that enforces sparsity in the dependence structure. We illustrate the results with an application to flood risk assessment on the Danube river.

    This is joint work with Adrien Hitz. Preprint available on \texttt{https://arxiv.org/abs/1812.01734}.

  2. Oct. 17, 2019David Sprott Distinguished Lecture by Xiao-Li Meng, Harvard University

    Building Deep Statistical Thinking for Data Science 2020: Privacy Protected Census, Gerrymandering, and Election

    The year 2020 will be a busy one for statisticians and more generally data scientists.  The US Census Bureau has announced that the data from the 2020 Census will be released under differential privacy (DP) protection, which in layperson’s terms means adding some noises to the data.  While few would argue against protecting data privacy, many researchers, especially from the social sciences, are concerned whether the right trade-offs between data privacy and data utility are being made. The DP protection also has direct impact on redistricting, an issue that is already complicated enough with accurate counts, due to the need of guarding against excessive gerrymandering.  The central statistical problem there is a rather unique one:  how to determine whether a realization is an outlier with respect to a null distribution, when that null distribution itself cannot be fully determined?  The 2020 US election will be another highly watched event, with many groups already busy making predictions. Will the lessons from predicting the 2016 US election be learned, or the failure be repeated?  This talk invites the audience on a journey of deep statistical thinking prompted by these questions, regardless whether they have any interest in the US Census or politics.

  3. Oct. 25, 2019Department seminar by Fabio Bellini, Università degli Studi di Milano-Bicocca

    On the properties of Lambda-quantiles

    We present a systematic treatment of Lambda-quantiles, a family of generalized quantiles introduced in Frittelli et al. (2014) under the name of Lambda Value at Risk. We consider various possible definitions and derive their fundamental properties, mainly working under the assumption that the threshold function Lambda is nonincreasing. We refine some of the weak continuity results derived in Burzoni et al. (2017), showing that the weak continuity properties of Lambda-quantiles are essentially similar to those of the usual quantiles. Further, we provide an axiomatic foundation for Lambda-quantiles based on a locality property that generalizes a similar axiomatization of the usual quantiles based on the ordinal covariance property given in Chambers (2009). We study scoring functions consistent with Lambda-quantiles and as an extension of the usual quantile regression we introduce Lambda-quantile regression, of which we provide two financial applications.

    (joint work with Ilaria Peri).

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Meet our people

Chengguo Weng

Chengguo Weng

Associate Professor

Contact Information:
Chengguo Weng

Chengguo Weng's personal website

Research interests

Professor Weng’s research interests span a broad spectrum of scientific disciplines from actuarial science, finance to probability, statistics and stochastic optimization. The primary objective of Professor Weng’s research is to develop innovative risk assessment methods and prioritization strategies for actuarial and financial risk management.