Events

Please note: This seminar will be given online.

Two-sample test on funscalar data with application to hemodialysis monitoring by Raman spectroscopy

To achieve in-session monitoring of hemodialysis through Raman spectroscopy, it is necessary to compare data consist of Raman spectra and intensity values for specific biomarkers (e.g., urea) contained in waste dialysate used in hemodialysis treatement. This calls for the development of a two-sample test procedure for funscalar data, data that are a mix of functional and scalar variables. Despite a rich literature on univariate functional data testing procedures and a few publications on multivariate functional data testing procedures, there is no such a testing procedure for funscalar data. In this work we propose the first testing procedure for funscalar data, generalizing the functional data approach in Horvath et al (2013). The test statistic is based on the L_2 distance between the two mean funscalar objects. Its asymptotic null distribution and asymptotic power are studied. We then demonstrate its performance through extensive simulations and its usefulness is through data collected in our hemodialysis monitoring experiments.

This seminar will be hosted by Webex. 

To join, please follow this link: Department Seminar by Pang Du

Can the reported COVID-19 data tell us the truth? Scrutinizing the data from the measurement error models perspective

The mystery of the coronavirus disease 2019 (COVID-19) and the lack of effective treatment for COVID-19 have presented a strikingly negative impact on public health. While research on COVID-19 has been ramping up rapidly, a very important yet overlooked challenge is on the quality and unique features of COVID-19 data. The manifestations of COVID-19 are not yet well understood.  The swift spread of the virus is largely attributed to its stealthy transmissions in which infected patients may be asymptomatic or exhibit only flu-like symptoms in the early stage. Due to the limited test resources and a good portion of asymptomatic infections, the confirmed cases are typically under-reported, error-contaminated, and involved with substantial noise. If the drastic effects of faulty data are not being addressed, analysis results of the COVID-19 data can be seriously biased.

In this talk, I will discuss the issues induced from faulty COVID-19 data and how they may challenge inferential procedures. I will describe a strategy of employing measurement error models to address the error effects. Sensitivity analyses will be conducted to quantify the impact of faulty data for different scenarios.  In addition, I will present a website of COVID-19 Canada (https://covid-19-canada.uwo.ca/), developed by the team co-led by Dr. Wenqing He and myself, which provides comprehensive and real-time visualization of the Canadian COVID-19 data.

Please note: This seminar will be given online through Webex. To join, please follow this link: Virtual seminar by Grace Yi.

Fairness through Experimentation: Inequality in A/B testing as an approach to responsible design

As technology continues to advance, there is increasing concern about individuals being left behind. Many businesses are striving to adopt responsible design practices and avoid any unintended consequences of their products and services, ranging from privacy vulnerabilities to algorithmic bias. We propose a novel approach to fairness and inclusiveness based on experimentation. We use experimentation because we want to assess not only the intrinsic properties of products and algorithms but also their impact on people. We do this by introducing an inequality approach to A/B testing, leveraging the Atkinson index from the economics literature. We show how to perform causal inference over this inequality measure. We also introduce the concept of site-wide inequality impact, which captures the inclusiveness impact of targeting specific subpopulations for experiments, and show how to conduct statistical inference on this impact. We provide real examples from LinkedIn, as well as an open-source, highly scalable implementation of the computation of the Atkinson index and its variance in Spark/Scala. We also provide over a year's worth of learnings -- gathered by deploying our method at scale and analyzing thousands of experiments -- on which areas and which kinds of product innovations seem to inherently foster fairness through inclusiveness.

Please note: This seminar will be given online through Webex. To join, please follow this link: Virtual seminar by Guillaume Saint-Jacques.

Multivariate Extremes: Block-Maxima vs Peak-Over-Threshold” 

Extreme value theory is concerned with describing the tail behaviour of univariate and multivariate distributions. In the estimation of the dependence structure of the extremes of multiple time series, the block maxima method and the peaks-over-threshold method are frequently applied. In this talk, I will compare these methods and propose some new methodologies. This is joint work with A. Bücher and S. Volgushev.

Nan is a lecturer in the Department of Mathematics and Statistics at Macquarie University in Sydney, Australia.

Please note: This seminar will be delivered via Zoom. Please check back later for the link. 

*This seminar will start at 5:00 p.m.

Applications of Nonstandard Analysis to Markov Processes

Nonstandard analysis, a powerful machinery derived from mathematical logic, has had many applications in probability theory as well as stochastic processes. Nonstandard analysis allows construction of a single object---a hyperfinite probability space---which satisfies all the first order logical properties of a finite probability space, but which can be simultaneously viewed as a measure-theoretical probability space via the Loeb construction. As a consequence, the hyperfinite/measure duality has proven to be particularly in porting discrete results into their continuous settings. 

In this talk, for every general-state-space discrete-time Markov process satisfying appropriate conditions, we construct a hyperfinite Markov process which has all the basic order logical properties of a finite Markov process to represent it.  We show that the mixing time and the hitting time agree with each other up to some multiplicative constants for discrete-time general-state-space reversible Markov processes satisfying certain condition. Finally, we show that our result is applicable to a large class of Gibbs samplers and Metropolis-Hasting algorithms.

Please note: This seminar will be delivered online through Webex. To join, please follow this link: Virtual seminar by Kevin (Haosui) Duanmu.

A statistician's introduction to genomics

A classical model of genetic association is introduced alongside a short history of its development with a particular focus on mouse models. The inferential consequences of the widespread use of mouse models are discussed, and the modern application of this model is introduced as a problem of measuring pairwise associations in a large data set. A broad algebraic framework for this model and others like it is used to demonstrate several results and suggest future avenues of investigation.

Discrimination-aware decisions in finance and insurance

We discuss the implications of considering protected attributes when individuals are paired with measures of risk. Two examples are analyzed, a credit scoring example using simulated data is given from the perspective of the regulator and an insurance pricing scenario is analyzed in view of the underlying causal model. 

Please Note: This talk will be given online through Microsoft Teams. To join, please follow this link: Virtual Seminar by Carlos Araiza Iturria.

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. To join, please follow this link: Virtual Seminar by Rui Qiao.

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 through Zoom. To join, please follow this link: Department Seminar by Aaditya Ramdas.

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 through Microsoft Teams. To join please click here: Student Seminar by Samuel Wong