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. Sep. 28, 2017David Sprott Distinguished Lecture by Susan A. MurphyDavid Sprott Distinguished lecture poster

  2. Aug. 10, 2017Pengfei Li is a 2017 recipient of the Faculty of Mathematics Award for Distinction in Teaching Pengfei Li

    The selection committee announced that Pengfei Li of the Department of Statistics and Actuarial Science is one of the two 2017 recipiants of the Faculty of Mathematics Award for Distinction.  Pengfei shares this honor with David Jao from the Department of Combinatorics and Optimization.

  3. July 12, 2017Samuel Eckler Medal in Actuarial ScienceAward winner Kieran Hendrickson-Gracie

    This prize was established to recognize the contribution of Samuel Eckler to the actuarial profession and is provided by Eckler Partners. The medal, which is cast in gold, is awarded each year to the outstanding graduating student of the Honours Actuarial Science Program.

    This year’s recipient is Kieran Hendrickson-Gracie who not only graduated as the top Actuarial Science major, but also has completed the five preliminary Society of Actuaries (SOA) exams, and earned three Cherry awards, in STAT 443, STAT 430, and STAT 431.

    Kieran's brother Aaron Hendrickson-Gracie, was awarded the Samuel Eckler Medal in 2013.

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  1. Sep. 26, 2017Graduate Student Seminar by Professor Ben Feng

    Owning Your Research: Things I wish I knew for graduate study

    Pursuing graduate study is a courageous decision for life that takes time, effort, and commitment. Graduate study can be mysterious at the beginning, miserable in the process, and marvelous at the end. As a recent grad, I am going to share some of my graduate study experiences in this presentation. In particular, I hope to give some advice to current graduate students to make their graduate study easier, faster, and happier. Among other recommendations, I will provide some tricks and tips on coding that can improve research productivity. 

  2. Sep. 28, 2017David Sprott Distinguished Lecture by Susan Murphy, University of Michigan, on September 28, 2017 Susan Murphy

    Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time

    A formidable challenge in designing sequential treatments is to  determine when and in which context it is best to deliver treatments.  Consider treatment for individuals struggling with chronic health conditions.  Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment.   That is, the treatment is adapted to the individual's context; the context may include  current health status, current level of social support and current level of adherence for example.  Data sets on individuals with records of time-varying context and treatment delivery can be used to inform the construction of the decision rules.    There is much interest in personalizing the decision rules, particularly in real time as the individual experiences sequences of treatment.   Here we discuss our work in designing  online "bandit" learning algorithms for use in personalizing mobile health interventions. 

  3. Oct. 5, 2017Department seminar by Xiaoming Hou, Georgia Institute of Technology

    Statistically and Numerically Efficient Independence Test

    We study how to generate a statistical inference procedure that is both computational efficient and having theoretical guarantee on its statistical performance. Test of independence plays a fundamental role in many statistical techniques. Among the nonparametric approaches, the distance-based methods (such as the distance correlation based hypotheses testing for independence) have numerous advantages, comparing with many other alternatives. A known limitation of the distance-based method is that its computational complexity can be high. In general, when the sample size is n, the order of computational complexity of a distance-based method, which typically requires computing of all pairwise distances, can be O(n^2). Recent advances have discovered that in the univariate cases, a fast method with O(n log n) computational complexity and O(n) memory requirement exists. In this talk, I introduce a test of independence method based on random projection and distance correlation, which achieves nearly the same power as the state-of-the-art distance-based approach, works in the multivariate cases, and enjoys the O(n K log n) computational complexity and O(max{n,K}) memory requirement, where K is the number of random projections. Note that saving is achieved when K < n/ log n. We name our method a Randomly Projected Distance Covariance (RPDC). The statistical theoretical analysis takes advantage of some techniques on random projection, which are rooted in contemporary machine learning. Numerical experiments demonstrate the efficiency of the proposed method, in relative to several competitors.

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Meet our people

Jock MacKay

Jock MacKay

Adjunct Professor

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Jock MacKay

​Research interests

My research interests span a variety of areas in the application of statistical methods to the improvement of manufacturing processes, including experimental design and observational methods. In collaboration with my colleague Stefan Steiner, we have developed these methods into a system for reducing variation in process outputs. This work led to our book, Statistical Engineering.