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. 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.

  2. 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).

  3. Oct. 31, 2019Department seminar by Qi Long, University of Pennsylvania

    Variable selection for structured high-dimensional data using known and novel graph information

    Variable selection for structured high-dimensional covariates lying on an underlying graph has drawn considerable interest. However, most of the existing methods may not be scalable to high dimensional settings involving tens of thousands of variables lying on known pathways such as the case in genomics studies, and they assume that the graph information is fully known. This talk will focus on addressing these two challenges. In the first part, I will present an adaptive Bayesian shrinkage approach which incorporates known graph information through shrinkage parameters and is scalable to high dimensional settings (e.g., p~100,000 or millions). We also establish theoretical properties of the proposed approach for fixed and diverging p. In the second part, I will tackle the issue that graph information is not fully known. For example, the role of miRNAs in regulating gene expression is not well-understood and the miRNA regulatory network is often not validated. We propose an approach that treats unknown graph information as missing data (i.e. missing edges), introduce the idea of imputing the unknown graph information, and define the imputed information as the novel graph information.  In addition, we propose a hierarchical group penalty to encourage sparsity at both the pathway level and the within-pathway level, which, combined with the imputation step, allows for incorporation of known and novel graph information. The methods are assessed via simulation studies and are applied to analyses of cancer data.

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Meet our people

Adam Kolkiewicz

Adam Kolkiewicz

Associate Professor

Contact Information:
Adam Kolkiewicz

Research interests

Professor Kolkiewicz's research interests are primarily in the areas of statistics and financial mathematics. In statistics, he has focused on statistical tools for time series analysis, robust methods of estimation, and asymptotic methods of inference.