Current students

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.

While a high school student in Toronto, Maysum Panju knew that the University of Waterloo was the destination for math. While working on his undergraduate degree and Master’s in computational math, he started learning about machine learning. That led to his interests in developing algorithms and theoretical proofs and he decided to start his PhD in statistics.

University of Waterloo Faculty to Mathematics researchers have developed a new method that enables large insurers to reduce the time spent estimating the financial liabilities of their portfolios from days to hours while achieving high accuracy.

A study details the new method which significantly reduces computational time, but still estimates the financial liability of variable annuity portfolios accurately for business purposes.

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.

Friday, February 7, 2020 10:00 am - 10:00 am EST (GMT -05:00)

Department seminar by Gabriel Becker, University of California Davis

The Extended Reproducibility Phenotype - Re-framing and Generalizing Computational Reproducibility

Computational reproducibility has become a crucial part of how data analytic results are understood and assessed both in and outside of academia. Less work, however, has explored whether these strict computational reproducibility criteria are necessary or sufficient to actually meet our needs as consumers of analysis results. I will show that in principle they are neither. I will present two inter-related veins of work. First, I will provide a  conceptual reframing of the concepts of strict reproducibility, and the actions analysts take to ensure it, in terms of our ability to actually trust the results and the claims about the underlying data-generating systems they embody. Second, I will present a generalized conception of reproducibily by introducing the concepts of Currency, Comparability and Completeness and their oft-overlooked importance to assessing data analysis results.

Thursday, February 6, 2020 10:00 am - 10:00 am EST (GMT -05:00)

Department seminar by Liqun Diao, University of Waterloo

Censoring Unbiased Regression Trees and Ensembles

Tree-based methods are useful tools to identify risk groups and conduct prediction by employing recursive partitioning to separate subjects into different risk groups. We propose a novel paradigm of building regression trees for censored data in survival analysis. We prudently construct the censored-data loss function through an extension of the theory of censoring unbiased transformations. With the construction, we can conveniently implement the proposed regression trees algorithm using existing software for the Classification and Regression Trees algorithm (e.g., rpart package in R) and extend it for ensemble learning. Simulations and real data examples demonstrate that our methods either improve upon or remain competitive with existing tree-based algorithms for censored data.