Richard J. Cook, a professor in the Department of Statistics and Actuarial Sciences, has been named a fellow the Royal Society of Canada (RSC). The prestigious RSC award accentuates Cook’s storied academic career and voluminous research portfolio.
Waterloo Student Conference in Statistics, Actuarial Science and Finance
Cecilia Cotton from the Department of Statistics and Actuarial Sciences has been named a winner of this year’s Awards for Distinction in Teaching from the Faculty of Mathematics.
The annual teaching award goes to faculty members who have consistently demonstrated outstanding pedagogical skills and a deep commitment to students. The winners are honoured by a public citation along with a cash prize.
When Sacha Tihanyi was halfway through an undergraduate economics degree, he recognized a budding passion for mathematics. “I had many broad interests in school, but something flipped for me during that program,” he remembered. “I chose Waterloo because it had a reputation as the best mathematics school in Canada.”
Internet users from Canadian rural and remote communities suffer from frequent Internet interruptions, which generally result from various network issues. The lack of human resources, expertise and support make these issues difficult to identifyand fix. Remote areas lack responsive and cost-effective operations or maintenance efforts.
When Grace Tompkins attended Waterloo’s Grad Visit Day in 2019, she was presented with an opportunity to combine her interests in public health with her research in mathematics. The Master of Mathematics in Biostatistics program was the perfect blend of mathematical theory and epidemiology that she was looking for.
Longevity risk is an essential concept for life insurance companies and reinsurance companies. A deep understanding of the risk is crucial, as an underestimation of longevity can seriously affect the financial healthiness of pension plans and annuity funds.
Network Response Regression for Modeling Population of Networks with Covariates
Multiple-network data are fast emerging in recent years, where a separate network over a common set of nodes is measured for each individual subject, along with rich subject covariates information. Existing network analysis methods have primarily focused on modeling a single network, and are not directly applicable to multiple networks with subject covariates.
In this talk, we present a new network response regression model, where the observed networks are treated as matrix-valued responses, and the individual covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept matrix, and the parsimonious effects of subject covariates on the network through a sparse slope tensor. We formulate the parameter estimation as a non-convex optimization problem, and develop an efficient alternating gradient descent algorithm. We establish the non-asymptotic error bound for the actual estimator from our optimization algorithm. Built upon this error bound, we derive the strong consistency for network community recovery, as well as the edge selection consistency. We demonstrate the efficacy of our method through intensive simulations and two brain connectivity studies.
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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.
Statistics and Actuarial Science Professor Pengfei Li has received a 2020 Faculty of Mathematics Golden Jubilee Research Excellence Award.