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. Nov. 30, 2022Aukosh Jagannath wins Outstanding Paper award at NeurIPS 2022

    Dr. Aukosh Jagannath, assistant professor of Statistics and Actuarial Science and Applied Mathematics at the University of Waterloo, has won a prestigious Outstanding Paper award at NeurIPS 2022.

    NeurIPS, short for Neural Information Processing Systems, is the foremost international conference for research on AI and Machine Learning. The conference receives thousands of submissions a year, and of the approximately 20% that are accepted, only a handful are recognized with the Outstanding Paper awards.

    Learn more on the Faculty of Math website.

  2. Nov. 16, 2022A three-pronged approach to helping Indonesia cope with climate change
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    Prime Minister Justin Trudeau today announced $15 million in funding for the University of Waterloo’s Faculties of Mathematics and Environment to work with Indonesian partners on climate change adaptation and mitigation strategies in the country.

    The funding provided by Global Affairs Canada will support the Flood Impacts, Carbon Pricing, and Ecosystem Sustainability (FINCAPES) project. FINCAPES is led by Professor Stefan Steiner in collaboration with Professor David Landriault and Professor Brent Doberstein.

    Read the full story on the Waterloo News website.

  3. Nov. 1, 2022Department of Statistics and Actuarial Science is pleased to welcome Qinglong Tian as an Assistant Professor
    Qinglong Tian

    Tian, Qinglong (BEcon (Statistics), 2016, Renmin University of China; Ph.D. (Statistics), 2021, Iowa State University) will be joining the Department of Statistics and Actuarial Science on November 1st, 2022 as an Assistant Professor. Before that, Qinglong Tian was a Research Associate at the University of Wisconsin-Madison. His research interests include reliability, resampling, and transfer learning. His current research focuses on developing statistical methods for data subject to distributional shifts.

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  1. Dec. 12, 2022Seminar by Matteo Bonvini

    Please Note: This seminar will be given in person.

    Department Seminar

    Matteo Bonvini
    Carnegie Mellon University

    Room: M3 3127

    Optimal Subgroup Identification

  2. Dec. 16, 2022Seminar by Zhimei Ren

    Please Note: This seminar will be given virtually.

    Department Seminar

    Zhimei Ren
    Stanford University

    Virtually on Zoom

    Stable Variable Selection with Knockoffs 

  3. Dec. 21, 2022Seminar by Joshua Agterberg

    Please Note: This seminar will be given in person.

    Department Seminar

    Joshua Agterberg
    Johns Hopkins University

    Room: M3 3127

    Estimating Higher-Order Mixed Memberships via the Two to Infinity Tensor Perturbation Bound

All upcoming events

Meet our people

Changbao Wu

Changbao Wu

Professor / Chair

Contact Information:
Changbao Wu

Changbao Wu's personal website

Research interests

Professor Wu has a primary research interest in the design and analysis of complex surveys. His research also covers more broad topics including semiparametric and nonparametric methods, resampling (jackknife and bootstrap) techniques, missing data and measurement error problems. He has worked extensively on empirical likelihood (EL) methods and related computational procedures, with strong interest in developing R packages for practical implementations of the EL methods.