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. 24, 2020Department seminar by Michael Gallaugher, McMaster University

    Clustering and Classification of Three-Way Data

    Clustering and classification is the process of finding and analyzing underlying group structure in heterogenous data and is fundamental to computational statistics and machine learning. In the past, relatively simple techniques could be used for clustering; however, with data becoming increasingly complex, these methods are oftentimes not advisable, and in some cases not possible. One such such example is the analysis of three-way data where each data point is represented as a matrix instead of a traditional vector. Examples of three-way include greyscale images and multivariate longitudinal data. In this talk, recent methods for clustering three-way data will be presented including high-dimensional and skewed three-way data. Both simulated and real data will be used for illustration and future directions and extensions will be discussed.

  2. Jan. 27, 2020Department seminar by James Wilson, University of San Francisco

    Network Analysis of the Brain: from Generative Modeling to Multilayer Network Embedding of Functional Connectivity Data


    Recent large-scale projects in neuroscience, such as the Human Connectome Project and the BRAIN initiative, emphasize the need of new statistical and computational techniques for analyzing functional connectivity within and across populations. Network-based models have greatly improved our understanding of brain structure and function, yet many important challenges remain. In this talk, I will consider two particularly important challenges: i) how does one characterize the generative mechanisms of functional connectivity, and ii) how does one identify discriminatory features among connectivity scans over disparate populations? To address the first challenge, I propose and describe a generative network model, called the correlation generalized exponential random graph model (cGERGM), that flexibly characterizes the joint network topology of correlation networks arising in functional connectivity. The model is the first of its kind to directly assess the network structure of a correlation network while simultaneously handling the mathematical constraints of a correlation matrix. I apply the cGERGM to resting state fMRI data from healthy individuals in the Human Connectome Project. The cGERGM reveals remarkably consistent organizational properties guiding subnetwork architecture, suggesting a fundamental organizational basis for subnetwork communication that differs from previous beliefs.

    For the second challenge, I focus on learning interpretable features from complex multilayer networks arising in population studies of functional connectivity. I will introduce the multi-node2vec algorithm, an efficient and scalable feature engineering method that learns continuous node feature representations from multilayer networks. The multi-node2vec algorithm identifies maximum likelihood estimators of nodal features through the use of the Skip-gram neural network model. Asymptotic analysis of the algorithm reveals that it is a fast approximation to a multi-dimensional non-negative matrix factorization applied to a weighted average of the layers in the multilayer network. I apply multi-node2vec to a multilayer functional brain network from resting state fMRI scans over a population of 74 healthy individuals and 70 patients with varying degrees of schizophrenia. The identified functional embeddings closely associate with the functional organization of the brain and offer important insights into the differences between patient and healthy groups that is well-supported by theory.

  3. 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.

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Meet our people

Ruodu Wang

Ruodu Wang

Associate Professor; University Research Chair

Contact Information:
Office: M3 3122
Phone: 519-888-4567, ext. 31569
Email: wang@uwaterloo.ca

Ruodu Wang's personal website