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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. Apr. 9, 2020Congratulations to the department’s newest University Research Chair
    Alexander Schied

    The University has appointed Professor Alexander Schied as a University Research Chair.

  2. Apr. 8, 2020Richard Cook appointed University Professor
    Richard J. Cook

    The University of Waterloo appointed Professor Richard Cook a “University Professor” in recognition of his outstanding research contributions in the field of Biostatistics. Waterloo’s designation of University Professor acknowledges exceptional scholarly achievement and international pre-eminence.

  3. Apr. 6, 2020Yilin Chen wins 2020 Huawei Prize for Best Research Paper

    Statistics and Actuarial Science PhD candidate Yilin Chen is one of two students to claim the 2020 Huawei Prize for Best Research paper by a Mathematics Graduate Student. The $4,000 prize affirms the value of Chen’s efforts to establish a framework for analyzing nonprobability survey samples in her winning paper: Doubly Robust Interference with Nonprobability Survey Samples.

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Meet our people

Yeying Zhu

Yeying Zhu

Assistant Professor

Contact Information:
Yeying Zhu

Yeying Zhu's personal website

Research interests

Dr. Zhu’s research interest lies in causal inference, machine learning and the interface between the two. She highly appreciates the interdisciplinary nature of causal inference and aim to develop theoretically sound methods for data-driven problems.

Her recent focus is on the development of variable selection/dimension reduction procedures to adjust for confounding in observational studies in a high-dimensional setting. In addition, she has developed innovative machine learning algorithms for the modeling of propensity scores for binary, multi-level and continuous treatments.