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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. Sep. 1, 2020The Department of Statistics and Actuarial Science is pleased to welcome Glen McGee as an Assistant Professor
    Glen McGee

    Glen McGee holds a PhD in biostatistics from Harvard University. He is interested in developing statistical tools to solve problems in epidemiology, environmental health, and health policy. Currently, Glen is working on Bayesian frameworks for modelling multi-pollutant mixtures, corrections for informative presence in electronic health records, and designs for multigenerational studies.

  2. 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.

  3. 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.

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  1. Oct. 1, 2020Department Seminar by Ashley Petersen

    Please Note: This seminar will be given online.

    Statistics and Biostatistics Seminar Series

    Ashley Petersen
    University of of Minnesota

    Link to join seminar: Hosted on Webex.

    Data-Adaptive Regression Modeling in High Dimensions

  2. Oct. 2, 2020Department Seminar by Emiliano Valdez

    Please Note: This seminar will be given online.

    Actuarial Science and Financial Mathematics Seminar Series

    Emiliano Valdez, Professor
    University of Connecticut

    Link to join seminar: Hosted on Webex.

    Analysis of Prescription Drug Utilization with Beta Regression Models

  3. Oct. 7, 2020Department Seminar by Pieter Allaart, University of North Texas

     On univoque and strongly univoque sets

    For a number beta in the interval (1,2), the univoque set U_beta is the set of numbers which have exactly one expansion in base beta. It has been well studied in the literature, and can be viewed as the set of points whose orbits avoid the hole in an open dynamical system. In 2011, Jordan, Shmerkin and Solomyak introduced a subset U_\beta' of the univoque set which we'll call the strongly univoque set, and used it to study the multifractal spectrum of Bernoulli convolutions. In this talk we will see another, rather unexpected, application of the strongly univoque set, to the infinite derivatives of Okamoto's self-affine function. We show that U_beta and U_beta' have the same Hausdorff dimension, and characterize the Hausdorff dimension of the difference U_beta\U_beta'.

    Please Note: This talk will be hosted online.

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Meet our people

Pengfei Li

Pengfei Li


Contact Information:
Pengfei Li

Pengfei Li's personal website

Research interests

Professor Li's research interests concern some areas of statistics, including finite mixture model, asymptotic theory, empirical likelihood, inference with constraints, experimental design, and smoothing technique.