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. 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.

  2. Nov. 20, 2019Congratulations to Richard J Cook for being awarded the Math Faculty Research Chair position
    Richard J Cook

    The Department of Statistics and Actuarial Science would like to congratulate Professor Richard J Cook for being awarded the five-year Faculty of Mathematics Research Chair position in recognition of his outstanding research contributions. The Faculty of Mathematics recognizes Richard's exceptional scholarly achievements and pre-eminence in the field of Statistics. Richard will receive a $250,000 research grant and a teaching reduction of one course per year.

  3. Nov. 5, 2019Tony Wirjanto appointed as the Curator in Insurance and Asset Management for the World Economic Forum
    Professor Tony Wirjanto

    On October 4, 2019, the World Economic Forum (WEF) and University of Waterloo appointed Professor Tony Wirjanto as the Curator in Insurance and Asset Management for the WEF.

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  1. Dec. 16, 2019Department seminar by Yixin Wang, Columbia University

    The Blessings of Multiple Causes


    Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods assume that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have observed all confounders is a famously untestable assumption. We describe the deconfounder, a way to do causal inference from observational data allowing for unobserved confounding.

    How does the deconfounder work? The deconfounder is designed for problems of multiple causal inferences: scientific studies that involve many causes whose effects are simultaneously of interest. The deconfounder uses the correlation among causes as evidence for unobserved confounders, combining unsupervised machine learning and predictive model checking to perform causal inference. We study the theoretical requirements for the deconfounder to provide unbiased causal estimates, along with its limitations and tradeoffs. We demonstrate the deconfounder on real-world data and simulation studies.

  2. Dec. 19, 2019Department seminar by Yuqi Gu, University of Michigan

    Uncover Hidden Fine-Grained Scientific Information: Structured Latent Attribute Models


    In modern psychological and biomedical research with diagnostic purposes, scientists often formulate the key task as inferring the fine-grained latent information under structural constraints. These structural constraints usually come from the domain experts’ prior knowledge or insight. The emerging family of Structured Latent Attribute Models (SLAMs) accommodate these modeling needs and have received substantial attention in psychology, education, and epidemiology.  SLAMs bring exciting opportunities and unique challenges. In particular, with high-dimensional discrete latent attributes and structural constraints encoded by a design matrix, one needs to balance the gain in the model’s explanatory power and interpretability, against the difficulty of understanding and handling the complex model structure.

    In the first part of this talk, I present identifiability results that advance the theoretical knowledge of how the design matrix influences the estimability of SLAMs. The new identifiability conditions guide real-world practices of designing diagnostic tests and also lay the foundation for drawing valid statistical conclusions. In the second part, I introduce a statistically consistent penalized likelihood approach to selecting significant latent patterns in the population. I also propose a scalable computational method. These developments explore an exponentially large model space involving many discrete latent variables, and they address the estimation and computation challenges of high-dimensional SLAMs arising from large-scale scientific measurements. The application of the proposed methodology to the data from an international educational assessment reveals meaningful knowledge structure of the student population.

  3. Jan. 6, 2020Department seminar by Kris Sankaran, Quebec Institute for Artificial Intelligence

    To be announced.

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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