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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. Aug. 4, 2020Matthias Schonlau elected fellow of the American Statistical Association
    Matthias Schonlau

    Professor Matthias Schonlau of the Department of Statistics and Actuarial Science has been elected a Fellow of the American Statistical Association (ASA). His citation reads: "For notable contributions to survey methodology in both industry and academia, for serving as a connector between statistics and the social sciences via accessible publications, education, and software, and for service to the profession."

    The designation of ASA Fellow has been a significant honor for nearly 100 years. Under ASA bylaws, the Committee on Fellows can elect up to one-third of one percent of the total association membership as fellows each year.

  2. July 10, 2020Marius Hofert awarded NSERC Discovery Accelerator Supplement
    Marius Hofert

    Associate Professor Marius Hofert was awarded a Natural Sciences and Engineering Council of Canada (NSERC) Discovery Accelerator Supplement for his suggested new work on copula modeling with generative neural networks.

  3. July 3, 2020Pengfei Li wins 2020 Faculty of Mathematics Golden Jubilee Research Excellence Award
    Pengfei Li

    Statistics and Actuarial Science Professor Pengfei Li has received a 2020 Faculty of Mathematics Golden Jubilee Research Excellence Award.

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  1. Aug. 12, 2020Student Seminar by Rui Qiao, PhD in Statistics

    A statistician's introduction to proteomics

    Proteomics is the large-scale study of proteins. It has important applications in drug discovery and antibody sequencing. In this talk, I would like to explain the basic concepts and data formats in proteomics. I will introduce the commonly used workflows to generate statistically analyzable data from the raw data stored on public repositories. And, I want to sQiaoare with you several important research topics in proteomics where I think statisticians could make a huge contribution.

    Please Note: This talk will be given through Microsoft Teams. Please check back later for details.

  2. Aug. 13, 2020Department Seminar by Aaditya Ramdas, Carnegie Mellon University

    Concentration inequalities for sampling without replacement, with applications to post-election audits

    Many practical tasks involve sampling sequentially without replacement from a finite population in order to estimate some parameter, like a mean. We discuss how to derive powerful (new) concentration inequalities for this setting using martingale techniques, and apply it to auditing elections (see below).

    This is joint work with my PhD student, Ian Waudby-Smith, who was an undergrad at UWaterloo. An early preprint is available here.

    More details: When determining the outcome of an election, electronic voting machines are often employed for their tabulation speed and cost-effectiveness. Unlike paper ballots, these machines are vulnerable to software bugs and fraudulent tampering. Post-election audits provide assurance that announced electoral outcomes are consistent with paper ballots or voter-verifiable records. We propose an approach to election auditing based on confidence sequences (VACSINE)—these are visualizable sequences of confidence sets for the total number of votes cast for each candidate that adaptively shrink to zero width. These confidence sequences have uniform coverage from the beginning of an audit to the point of an exhaustive recount, but their main advantage is that their error guarantee is immune to continuous monitoring and early stopping, providing valid inference at any auditor-chosen, data-dependent stopping time. We develop VACSINEs for various types of elections including plurality, approval, ranked-choice, and score voting protocols.

    Please Note: This talk will be given online. Please check back later for details. 

  3. Aug. 19, 2020Student Seminar by Samuel Wong, Assistant Professor

    Assessing the Impacts of Mutations to the Structure of COVID-19 Spike Protein via Sequential Monte Carlo

    Proteins play a key role in facilitating the infectiousness of the 2019 novel coronavirus. A specific spike protein enables this virus to bind to human cells, and a thorough understanding of its 3-dimensional structure is therefore critical for developing effective therapeutic interventions. However, its structure may continue to evolve over time as a result of mutations. We take a data science perspective to study the potential structural impacts due to ongoing mutations in its amino acid sequence. To do so, we identify a key segment of the protein and apply a sequential Monte Carlo sampling method to detect possible changes to the space of low-energy conformations for different amino acid sequences. Such computational approaches can further our understanding of this important protein structure and complement laboratory efforts.

    Please Note: This talk will be given online. Please check back later for details. 

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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.