To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca. 

Biostatistics is a field that requires multi-disciplinary collaboration between statisticians, medical professionals and other subject-domain experts. Over the last several years, BC Children’s Hospital Research Institute (BCCHRI) has built a biostatistics core to provide expertise to their research community on development of appropriate study design and analysis methods for clinical and public health research. This talk will outline the experience of leading the biostatistics unit, key skills for success in applied settings when working with non-statistical collaborators, and the tension between theoretical best practice and the constraints of real-world data. Examples of projects from BCCHRI will be used to illustrate statistical techniques and challenges. 

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca. 

Jeff Bone is the Biostatistical Lead at BC Children’s Hospital Research Institute. In this role, he provides methodological input to clinical and epidemiological research studies across a range of disciplines, supervises analysts and trainees, and provides community education. He has a PhD in Women’s and Children’s Health (UBC) focused on statistical methods and modelling in perinatal epidemiology, an MSc in Statistics (UBC) and BSc (Hons) in Mathematics and Statistics (UVic). His current areas of statistical research include analysis of population level data, causal inference for observational data and design and analysis of randomized controlled trials. His main areas of applied work are in perinatal epidemiology, obstetrics, and pediatric diabetes.

Variational inference (VI) approximates a target distribution within a chosen family that permits i.i.d. sampling and tractable density evaluation. Because the approximation is obtained by minimizing a divergence to the target, its best achievable quality is constrained by the family’s expressiveness. Yet greater flexibility does not guarantee better results: the optimization landscape is typically highly non-convex, so the theoretical optimum is rarely attained in practice. Consequently, VI generally lacks the asymptotic exactness of Markov chain Monte Carlo (MCMC)—the ability to achieve arbitrarily accurate inference given sufficient computation, regardless of tuning.

In this talk, I will introduce mixed variational flows (MixFlows): a framework for constructing tuning-free, asymptotically exact variational families using measure-preserving dynamical systems. The key methodological advance is a way to use involutive MCMC kernels to build variational flows, yielding families that inherit MCMC-level convergence guarantees while retaining VI’s tractability (i.i.d. sampling and closed-form density evaluation).

I will also discuss how tools from chaotic dynamical systems illuminate the propagation of probabilistic error through \emph{inexact} flows—errors that arise from finite-precision arithmetic and numerical discretization—providing practical guidance for when flow-based approximations remain reliable in spite of numerical instability. 

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca. 

Building data literacy requires intentional design—both inside and outside the classroom. As statistics educators, we are uniquely positioned to help learners not only analyze data but also communicate, question, and connect with it in meaningful ways. This talk will explore several initiatives that promote inclusive and engaging approaches to developing data literacy across different educational levels.

First, I will discuss a range of outreach activities designed to introduce statistical thinking to elementary and secondary students through play, storytelling, and authentic data contexts. These activities—ranging from constructing visualizations using the Spotify API to exploring sampling methods with geodes and “dinosaur fossils”—have been implemented at events such as Florence Nightingale Day and Pursue STEM. Such initiatives align with calls to cultivate early data literacy and “real-world statistical reasoning” among pre-tertiary learners (Ben-Zvi & Garfield, 2004; Ridgway, 2016).

Second, I will highlight innovations in postsecondary statistics education, focusing on the integration of Universal Design for Learning (UDL) principles (CAST, 2018) in a large third-year course (STA304: Surveys, Sampling, and Observational Data). Through flexible grading, grace period, and generative AI policies, the course design supports diverse learners while maintaining academic rigor. Student feedback illustrates how flexibility can enhance motivation, equity, and engagement—findings that echo recent work on inclusive assessment and learning autonomy in statistics education (Engel, 2017).

Together, these projects demonstrate how flexibility, communication, and creativity can support inclusive data literacy education across age groups. By integrating outreach and UDL-informed teaching, we can expand access to data-driven inquiry and foster a more diverse and data-confident generation of learners.

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca. 

Bayesian hypothesis testing using the Bayes factor is an alternative to hypothesis testing based on the p-Value. They are especially useful if one considers the p-postulate, which suggests that equal p-values, irrespective of sample size, should represent equal evidence against a null hypothesis, false. Bayes factors can, however, be computationally intensive and require a prior distribution. We define an extension of Jeffrey's approximate objective Bayes factor (eJAB) based on a generalization of the unit information prior. Its computation requires nothing more than the p-value and the sample size and it provides a measure of evidence that allows one to interpret the p-value in light of the associated sample size through the lens of an approximate Bayes factor corresponding to an objective prior. We apply eJAB to reexamine the evidence from 71,130 clinical trial findings with particular attention to contradictions between Bayes factors and NHST—i.e., instances of the Jeffreys–Lindley paradox (JLP). Our findings reflect increasing evidence in the literature of problematic clinical trial design and results.

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca. 

Artificial Intelligence (AI) is clearly one of the hottest subjects these days. Basically, AI employs a huge number of inputs (training data), super-efficient computer power/memory, and smart algorithms to perform its intelligence. In contrast, Biological Intelligence (BI) is a natural intelligence that requires very little or even no input. This talk will first discuss the fundamental issue of input (training data) for AI. After all, not-so-informative inputs (even if they are huge) will result in a not-so-intelligent AI. Specifically, three issues will be discussed: (1) input bias, (2) data right vs. right data, and (3) sample vs. population. Finally, the importance of Statistical Intelligence (SI) will be introduced. SI is somehow in between AI and BI. It employs important sample data, solid theoretically proven statistical inference/models, and natural intelligence. In my view, AI will become more and more powerful in many senses, but it will never replace BI. After all, it is said that “The truth is stranger than fiction, because fiction must make sense.” The ultimate goal of this study is to find out “how can humans use AI, BI, and SI together to do things better.”

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca. 

Dr. Dennis K. J. Lin is a Distinguished Professor of Statistics at Purdue University. He served as the Department Head during 2020-2022. Prior to this current job, he was a University Distinguished Professor of Supply Chain Management and Statistics at Penn State, where he worked for 25 years. His research interests are data quality, industrial statistics, statistical inference, and data science. He has published nearly 300 SCI/SSCI papers in a wide variety of journals. He currently serves or has served as an associate editor for more than 10 professional journals and was a co-editor for Applied Stochastic Models for Business and Industry. Dr. Lin is an elected fellow of ASA, IMS, ASQ, & RSS, an elected member of ISI, and a lifetime member of ICSA. He is an honorary chair professor for various universities, including Fudan University, and National Taiwan Normal University and a Chang-Jiang Scholar at Renmin University of China. His recent awards include, the Youden Address (ASQ, 2010), the Shewell Award (ASQ, 2010), the Don Owen Award (ASA, 2011), the Loutit Address (SSC, 2011), the Hunter Award (ASQ, 2014), the Shewhart Medal (ASQ, 2015), and the SPES Award (ASA, 2016). He won the Deming Lecturer Award at 2020 JSM. His most recent award is “The 2022 Distinguished Alumni Award” (National Tsing Hua University, Taiwan).

Most universities offer centralized teaching development programs for Teaching Assistants (TAs), but discipline-specific initiatives – particularly in statistics and actuarial science – are often limited or informal. In response to this gap, the Department of Statistics and Actuarial Science at the University of Waterloo launched a comprehensive TA Program in 2023. This initiative encompasses all aspects of graduate teaching assistantships and includes the Foundations for University Teaching in Statistics and Actuarial Science certificate training program.

Developed in collaboration with the university’s Centre for Teaching Excellence, our program provides structured, sequential training tailored to the unique demands of statistics and actuarial science courses. It equips incoming and current graduate TAs with the skills needed to confidently and effectively fulfill their roles, including proctoring, grading, facilitating tutorials, and preparing and delivering lecture content.

In this talk, we will outline the state of our TA training prior to 2023, share the motivations behind the creation of our program, and describe its current structure. We will present data on TA participation, share feedback from past trainees, and discuss future directions for the program, including its potential adaptation by other departments and institutions.

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca. 

Multilevel Regression with Post-stratification (MrP) has become a workhorse method for estimating population quantities using non-probability surveys, and is the primary alternative to traditional survey calibration weights, e.g.~ as computed by raking. For simple linear regression models, MrP methods admit “equivalent weights”, allowing for direct comparisons between MrP and traditional calibration weights (Gelman 2006). In the present paper, we develop a more general framework for computing and interpreting “MrP approximate weights” (MrPaw), which admit direct comparison with calibration weights in terms of important diagnostic quantities such as covariate balance, frequentist sampling variability, and partial pooling. MrPaw is based on a local equivalent weighting approximation, which we show in theory and practice to be accurate. Importantly, MrPaw can be easily computed based on existing MCMC samples and conveniently wraps standard MrP software implementations. We illustrate our approach for several canonical studies that use MrP, including for the binary outcome of vote choice, showing a high degree of variability in the performance of MrP models in terms of frequentist diagnostics relative to raking.

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca. 

Many common Markov chain Monte Carlo (MCMC) kernels can be formulated using a deterministic involutive proposal with a step size parameter. Selectingan appropriate step size is often a challenging task in practice; and for complex multiscale targets, there may not be one choice of step size that works well globally. In this work, we address this problem with a novel class of involutive MCMC methods—AutoStep MCMC—that selects an appropriate step size at each iteration adapted to the local geometry of the target distribution. We prove that under mild conditions AutoStep MCMC is π-invariant, irreducible, and aperiodic, and obtain bounds on expected energy jump distance and cost per iteration. Empirical results examine the robustness and efficacy of our proposed step size selection procedure, and show that AutoStep MCMC is competitive with state-of-the-art methods in terms of effective sample size per unit cost on a range of challenging target distributions.

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Functional neuroimaging data, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), often exhibit rich temporal, spatial, and spectral structure, posing unique challenges for statistical modeling. This talk presents Bayesian modeling approaches for functional neuroimaging data, focusing on time-frequency representations of EEG signals from multi-condition experiments. In such experiments, brain activity is recorded as subjects engage in various tasks or are exposed to different stimuli. The resulting data often exhibit smooth variation across time and frequency and can be naturally represented as two-way functional data, with conditions nested within subjects. To jointly account for the data’s multilevel structure, functional nature, and subject-level covariates, we propose a Bayesian mixed-effects model incorporating covariate-dependent fixed effects and multilevel random effects. For interpretability and parsimony, we introduce a novel decomposition of the fixed effects with marginally interpretable time and frequency patterns, along with a sparsity-inducing prior for rank selection. The proposed method is evaluated through extensive simulations and applied to EEG data collected to investigate the effects of alcoholism on cognitive processing in response to visual stimuli.  Extensions to modeling dynamic functional connectivity and other Bayesian methods developed for fMRI data will also be discussed. 

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca.