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.

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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.

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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. 

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LLMs have a lot of hype around them these days. Let’s demystify how they work and see how we can put them in context for data science use. As data scientists, we want to make sure our results are inspectable, reliable, reproducible, and replicable. We already have many tools to help us in this front. However, LLMs provide a new challenge; we may not always be given the same results back from a query. This means trying to work out areas where LLMs excel in, and use those behaviours in our data science artifacts. This talk will introduce you to LLms, the Ellmer, and Chatlas packages for R and Python, and how they can be integrated into a Shiny to create an AI-powered dashboard. We’ll see how we can leverage the tasks LLMs are good at to better our data science products.

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

The relative risk (RR) offers interpretation and comparison advantages over the Odds Ratio (OR) used in logistic regression. However, its direct estimation in high-dimensional settings is challenging. Common approaches, such as penalized log-binomial and Poisson regression, are built on parameters that are variationally dependent, while newer, variation-independent models have been limited by estimators not designed for high-dimensional or sparse data.

To address this, this project built on previous penalized RR models to implement a faster penalized estimator for the variation-independent relative risk model. The contributions include an efficient implementation in C++, the use of an Adaptive Step Size FISTA algorithm for robust optimization, and a comprehensive evaluation of different penalization strategies and model specifications. Through simulation studies, the proposed estimator is shown to be a robust tool for high-dimensional analysis. It demonstrates better predictive accuracy and the ability to identify relevant predictors in sparse scenarios correctly.

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Estimating the parameters of disease transmission models is an important component in analyzing disease outbreaks and inferring transmission networks. The introduction of genetic data into disease transmission models has enabled more detailed inference, particularly through phylogenetic trees derived from the genetic data. Existing approaches often rely on a single phylogenetic tree to subset transmission trees from a set of possible transmission trees inferred from epidemiological data. However, such methods typically do not account for the uncertainty inherent in phylogenetic reconstruction. This thesis introduces a Sequential Monte Carlo-Expectation Maximization (SMC-EM) framework that explicitly incorporates uncertainty in transmission and phylogenetic trees. We treat these trees as latent variables and use observed genetic sequences, sampling times, and epidemiological data to inform the model. Our method constructs transmission and phylogenetic trees sequentially, conditioned on infection times, and updates parameter estimates iteratively via a variant of the EM algorithm. We evaluate the performance of the proposed method through extensive simulation studies and demonstrate its applicability using a real-world outbreak dataset. The results indicate that the SMC-EM approach provides improved parameter estimates while effectively capturing the uncertainty in latent tree structures. 

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There are many non-Gaussian time series models available in the literature. Copula-based time series models are particularly relevant as they can handle serial tail dependence or the clustering of extreme observations. To date, mainly copula-based Markov time series models that extend the autoregressive time series model have been studied and applied. In this talk, I will consider non-Markovian copula-based time series models that can be viewed as an extension of Gaussian autoregressive moving average (ARMA) models. I derive distributional properties and discuss conditions for stationarity, as well as the asymptotic properties of the maximum-likelihood estimators. Finally, the probabilistic forecasting performance is evaluated.

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DNA methylation is a key epigenetic mechanism governing gene regulation and cellular identity. Advances in high-throughput sequencing technologies have enabled detailed investigation of methylation landscapes across single cells and complex tissue mixtures. However, the sparsity and noise inherent in single-cell data, as well as the signal distortion in enrichment-based platforms, pose major analytical challenges. This thesis presents two novel statistical frameworks to address these limitations and advance the computational toolkit for DNA methylation analysis.

The first contribution is vmrseq, a probabilistic method and software for detecting variably methylated regions from single-cell bisulfite sequencing data. vmrseq integrates a smoothing-based strategy for candidate region identification with hidden Markov modeling to account for spatial correlation and technical noise. Through extensive benchmarking on synthetic and experimental datasets, vmrseq demonstrates improved precision and biological relevance in identifying methylation heterogeneity, supporting downstream analyses such as unsupervised clustering and cell-type-specific marker discovery.

The second contribution is decemedip, a hierarchical Bayesian model and software for cell type deconvolution of enrichment-based methylation data such as MeDIP-seq. By leveraging reference panels derived from alternative platforms and modeling the complex relationship between methylation levels, CpG density, and read counts, decemedip enables accurate estimation of cell type proportions with uncertainty quantification. Its performance is validated through simulations, cross-platform comparisons, and real-world applications involving patient-derived xenografts and circulating cell-free DNA from cancer cohorts.

Together, these methods address critical gaps in the analysis of high-throughput DNA methylation data, enabling robust detection of epigenetic heterogeneity across biological contexts. The associated open-source software implementations provide practical tools for future epigenomic research and potential clinical applications.

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