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

Interested in graduate studies with Statistics and Actuarial Science? Meet Mingyu (Bruce) Feng, a PhD student in actuarial science. Bruce is a pioneer researcher in the field of sustainable investment and the impact of climate change. Learn more about furthering your education on the Future Graduate page on the Math site.

  1. Jan. 15, 2020Music and Math are a balanced combination
    A hand points to music notes on a  chalkboard.

  2. Jan. 10, 2020Waterloo startup aims to revolutionize food delivery industry
    Gooloo co-founder Yuqian Li pitches at the Concept 5K final

    They won’t graduate until next spring, but their startup has already taken flight.

    The five founders of Gooloo recently earned a spot in the University of Waterloo’s Concept $5K Finals, where they explained their business model to a panel of judges alongside nine other student teams.

  3. Dec. 3, 2019The Department of Statistics and Actuarial Science is pleased to welcome Assistant Professor Pengyu Wei as of October 1, 2019.
    Pengyu Wei

    Pengyu Wei holds a PhD in Mathematics from Oxford University from 2018. He is joining us from a senior research associate position at the University of New South Wales Business School. His research interests include quantitative finance, risk management and actuarial science. Given his research expertise at the interface between mathematical finance and actuarial science, Pengyu will create stronger linkages between existing faculty members in these two important areas.

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  1. Jan. 29, 2020Department seminar by Aya Mitani, Harvard T.H. Chan School of Public Health

    Marginal analysis of multiple outcomes with informative cluster size

    Periodontal disease is a serious infection of the gums and the bones surrounding the teeth. In Veterans Affairs Dental Longitudinal Study (VADLS), the relationships between periodontal disease and other health and socioeconomic conditions are of interest. To determine whether or not a patient has periodontal disease, multiple clinical measurements (clinical attachment loss, alveolar bone loss, tooth mobility) are taken at the tooth-level. However, a universal definition for periodontal disease does not exist and researchers often create a composite outcome from these measurements or analyze each outcome separately. Moreover, patients have varying number of teeth, with those that are more prone to the disease having fewer teeth compared to those with good oral health. Such dependence between the outcome of interest and cluster size (number of teeth) is called informative cluster size, and results obtained from fitting conventional marginal models can be biased. In this talk, I will introduce a novel method to jointly analyze multiple correlated outcomes for clustered data with informative cluster size using the class of generalized estimating equations (GEE) with cluster-specific weights. Using the data from VADLS, I will compare the results obtained from the proposed multivariate outcome cluster-weighted GEE to those from the conventional unweighted GEE. Finally, I will discuss a few other research settings where data may exhibit informative cluster size.

  2. Jan. 30, 2020Department seminar by Hyukjun (Jay) Gweon, Western University

    Batch-mode active learning for regression and its application to the valuation of large variable annuity portfolios

    Supervised learning algorithms require a sufficient amount of labeled data to construct an accurate predictive model. In practice, collecting labeled data may be extremely time-consuming while unlabeled data can be easily accessed. In a situation where labeled data are insufficient for a prediction model to perform well and the budget for an additional data collection is limited, it is important to effectively select objects to be labeled based on whether they contribute to a great improvement in the model's performance. In this talk, I will focus on the idea of active learning that aims to train an accurate prediction model with minimum labeling cost. In particular, I will present batch-mode active learning for regression problems. Based on random forest, I will propose two effective random sampling algorithms that consider the prediction ambiguities and diversities of unlabeled objects as measures of their informativeness. Empirical results on an insurance data set demonstrate the effectiveness of the proposed approaches in valuing large variable annuity portfolios (which is a practical problem in the actuarial field). Additionally, comparisons with the existing framework that relies on a sequential combination of unsupervised and supervised learning algorithms are also investigated.

  3. Feb. 4, 2020Department seminar by Haolun Shi, Simon Fraser University

    Bayesian Utility-Based Toxicity Probability Interval Design for Dose Finding in Phase I/II Trials

    Molecularly targeted agents and immunotherapy have revolutionized modern cancer treatment. Unlike chemotherapy, the maximum tolerated dose of the targeted therapies may not pose significant clinical benefit over the lower doses. By simultaneously considering both binary toxicity and efficacy endpoints, phase I/II trials can identify a better dose for subsequent phase II trials than traditional phase I trials in terms of efficacy-toxicity tradeoff.  Existing phase I/II dose-finding methods are model-based or need to pre-specify many design parameters, which makes them difficult to implement in practice. To strengthen and simplify the current practice of phase I/II trials, we propose a utility-based toxicity probability interval (uTPI) design for finding the optimal biological dose (OBD) where binary toxicity and efficacy endpoints are observed. The uTPI design is model-assisted in nature, simply modeling the utility outcomes observed at the current dose level based on a quasibinomial likelihood. Toxicity probability intervals are used to screen out overly toxic dose levels, and then the dose escalation/de-escalation decisions are made adaptively by comparing the posterior utility distributions of the adjacent levels of the current dose. The uTPI design is flexible in accommodating various utility functions while only needs minimum design parameters. A prominent feature of the uTPI design is that it has a simple decision structure such that a concise dose-assignment decision table can be calculated before the start of trial and be used throughout the trial, which greatly simplifies practical implementation of the design. Extensive simulation studies demonstrate that the proposed uTPI design yields desirable as well as robust performance under various scenarios. This talk is based on the joint work with Ruitao Lin and Ying Yuan at MD Anderson Cancer Center.

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Meet our people

Kun Liang

Kun Liang

Associate Professor

Contact Information:
Kun Liang

Kun Liang's personal website

Research interests

Large-scale inference

Statistical genetics

High-dimensional statistics

Machine learning