The Computer-Based Testing Facility (CBTF) aims to solve a key problem in teaching and learning: helping instructors run digital assessments at scale securely and equitably. The easiest way to describe the CBTF is to imagine it as a network-filtered computer lab dedicated to running digital assessments in 50-minute increments throughout the day, invigilated by trained proctors. Students are typically given a multi-day window to write their tests at a time and location convenient for them. With network filtering, centralized invigilation, a distributed exam model, and flexibility for students and instructors, the mission of the CBTF is to spur pedagogical innovation at the university in a broad range of classes, programs, and departments. Though not the initial motivation, recently the CBTF has also been used to maintain exam integrity in the face of modern AI tools, particularly for computer-based exams where any element of programming is needed under controlled environments. This session will be useful for a range of people including faculty members teaching courses, administrators, IT staff, grad students as well as anyone with an interest in pedagogy and innovative teaching methods. We’ll also hear about the experience of several Statistics faculty members in using the CBTF in their courses as pilots in previous terms. There will be plenty of time for Q&A and a larger conversation around migration to computer-based testing and different learning technologies. The CBTF currently supports a variety of assessment options including Canvas, PrairieLearn, MTA and others. We will also discuss the advantages and disadvantages of different learning technologies in the CBTF from a pedagogical, logistical, and financial perspective.

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

The UBC Statistics Department Colloquium Series features talks that are broad, accessible, and engaging - and open to everyone!

The third talk of our series will take place on Monday, June 8th where we will welcome Stephanie Hicks, Associate Professor of Biomedical Engineering and Biostatistics at Johns Hopkins University.

Date: Monday, June 8, 2026
Time: 3 - 4 PM
Location: ESB 5104/5106

Title: Statistical methods for single-cell and spatial data science

Abstract: Genomics is going through a data revolution where we can now profile gene expression at a single-cell or 2D spatial resolution. However, these data present unique challenges that have required the development of specialized statistical and computational methods and software infrastructure to successfully derive biological insights. Compared to bulk RNA-seq, there is an increased scale of the number of observations (or cells) that are measured and there is increased sparsity of the data, or fraction of observed zeros. Furthermore, as single-cell technologies mature, the increasing complexity and volume of data require fundamental changes in data access, management, and infrastructure alongside specialized methods to facilitate scalable analyses. I will discuss some challenges in the analysis of data and present some solutions that we have made towards addressing these challenges.

This colloquium series is sponsored in part by the Constance van Eeden Endowment.

Recovering counterfactual distributions is an important goal in causal inference. In recent years, there has been a growing literature on transport-based models that directly model transport maps between outcome distributions. These methods avoid specifying full data-generating processes and are therefore more robust to mis-specification. The multivariate nonlinear difference-in-differences (DiD) model is one such example. It recovers counterfactual distributions using optimal transport when the treatment is discrete. This approach, however, does not readily generalize to continuous treatments. In this talk, we extend the nonlinear DiD to a continuous treatment setting using cocycles, which are constructed using a different class of transport maps. We propose an estimator for the average treatment effect on the treated and conduct simulation experiments to empirically study its convergence. We also investigate whether having anchoring treatment groups can result in faster convergence and answer that in the negative based on the simulation results.

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

Telemetry data offer unprecedented opportunities to study wildlife behaviour, but extracting ecological insights from these complex processes requires advanced statistical methods. Using narwhal (Monodon monoceros) movement data as a case study, my PhD develops novel statistical methods to address three key challenges in animal movement analysis. First, while hidden Markov models (HMMs) provide a natural and powerful framework for inferring latent behavioural states from movement data, selecting the number of hidden states in such models is a notoriously difficult task. Common information criteria perform poorly in selecting the number of states under model misspecifications. I build upon a double penalized maximum likelihood estimator (DPMLE) for simultaneous estimation of the number of states and parameters of non-stationary HMMs. Through simulations and the narwhal case study, I show that the DPMLE outperforms traditional methods under misspecifications and enables more realistic modelling of movement data. Second, as human activities expand across wildlife habitats, quantifying behavioural responses to disturbances is crucial for conservation. I introduce a lasso-penalized threshold HMM that jointly estimates the distance at which animals react to a stimulus and ensures this distance threshold corresponds to a meaningful behavioural shift. Results suggest that narwhal react to vessels up to 4 km away, reducing movement persistence and spending more time in deeper waters. To my knowledge, this is the first model-based estimate of a disturbance threshold in movement ecology. Third, understanding habitat selection requires methods robust to location error inherent in animal tracking data. I extend the Langevin diffusion habitat selection model to accommodate error-prone observations, using automatic differentiation and the Laplace approximation for efficient maximum-likelihood estimation, providing the first Template Model Builder (TMB) implementation capable of handling covariates depending on latent variables. Simulations indicate that the proposed method performs better than conventional two-step procedures, which tend to produce estimates biased towards zero. Application to narwhal data reveals a stronger selection signal towards deeper water under my approach. Together, the methods developed in my dissertation advance the statistical toolkit for movement ecology and beyond, as the frameworks developed here are broadly applicable to time series analysis across a wide range of fields.

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

A treatment benefit predictor is a function that maps patient characteristics to a putative treatment benefit for that patient. Such predictors support the optimization of individualized treatment decisions, a central idea of precision medicine. However, evaluating the predictive performance of a treatment benefit predictor is challenging, as we often cannot observe each individual's treatment benefit. This work theoretically underpins common predictive metrics and demonstrates conceptual and practical evaluation of prespecified treatment benefit predictors in the target population. At a conceptual level, we define the estimands of a set of predictive performance metrics. A particular measure of discrimination is used as an illustrative example to reveal methodological concerns on multiple fronts. We describe how to evaluate a treatment benefit predictor using observational data from the target population and explore how predictive performance metrics may change when confounding is not fully controlled. In practice, we propose and implement estimation methods for evaluating the predictive performance of treatment benefit predictors, assessing their reliability through simulation studies. We illustrate their practical use in real-world observation data, including cohort construction and modeling strategies. Overall, this work helps bridge the gap between predictive modeling and causal inference, providing a framework for evaluating treatment benefit predictors using predictive performance metrics.

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

This talk surveys recent advances in the understanding of Chatterjee's nearest-neighbor graph-based correlation coefficient. I will present, for the first time, a comprehensive theoretical framework for statistical inference based on this coefficient, including results on asymptotic normality, bias correction, and inconsistency of bootstrap methods. I will also discuss several open problems that may be of interest to researchers wishing to explore this area further.

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

In low- and middle-income countries (LMICs), accurate estimates of subnational health and demographic indicators are critical for guiding policy and identifying disparities. Many indicators of interest are proportions of binary outcomes and the task of estimating these fractions is often called prevalence mapping. In LMICs, health and vital records data are limited, so prevalence mapping relies on data from household surveys with complex sampling designs. However, estimates are often desired at spatial resolutions at which data are insufficient for reliable weighted estimation. We review two families of approaches to prevalence mapping: small area estimation (SAE) methods (from the survey statistics literature) and model-based geostatistics (MBG) methods (from the spatial statistics literature). SAE models can be "area-level" or "unit-level" and commonly use area-specific random effects and rely upon high-quality covariate data, often obtained from administrative sources. Unit-level models for binary responses are relatively underdeveloped. MBG approaches explicitly specify binary response models, incorporate continuous spatial random effects, and leverage alternative sources of data such as those arising from satellite imagery. These models are usually studied under a Bayesian framework. SAE methods often address the design by incorporating sampling weights or modeling the sampling mechanism. Two delicate issues arise when using MBG methods for prevalence mapping. First, aggregating unit level predictions to create area-level summaries requires population-level information that is rarely directly available. Second, MBG approaches typically assume the sampling design is ignorable. We review both SAE and MBG approaches to prevalence mapping, and argue that binary response models can be improved using insights from both the survey sampling and the spatial statistics literature. We highlight these issues using household survey data from different Demographic and Health Surveys, and with various indicators.

This is joint work with Geir-Arne Fuglstad, Peter Gao and Zehang Richard Li.

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

Much work in causal inference focuses on finite-dimensional targets like average treatment effects. However, many substantively important causal questions involve inherently infinite-dimensional objects, such as counterfactual outcome distributions, heterogeneous treatment effect surfaces, and continuous treatment curves. These targets occupy a hybrid space between classical parameter estimation and nonparametric function estimation. In this talk, I survey some recent work involving these infinite-dimensional causal estimands, highlighting both model-based and model-free nonparametric approaches. I discuss how, despite the impossibility of root-n-rate estimation, ideas from semiparametric theory (like double robustness) continue to play a central role. Throughout I emphasize the relevance of these methods in applications in social sciences and medicine.

This talk is part of the UBC Statistics Colloquium Series, which features broad and accessible seminars throughout the term and is sponsored in part by the Constance van Eeden Endowment.

Registration & Talk details

We invite you to a speaker series focused on learning about equity, diversity and inclusion practices and initiatives in Statistics and Data Science. Our next speaker will be Dr. Aleksandra Korolova, Assistant Professor of Computer Science and Public Affairs, Princeton University. 

Date/Time: March 26, 2026, 11:00am – 12:00pm

Talk title: Lessons from auditing the hidden societal impacts of ad delivery algorithms

Abstract: Although targeted advertising has been touted as a way to give advertisers a choice in who they reach, increasingly, ad delivery algorithms designed by the ad platforms are invisibly refining those choices. In this talk, I will present our findings from "black-box" auditing of the role of ad delivery algorithms in shaping who sees opportunity and political ads using only the tools and data accessible to any advertiser. I will then discuss legal and policy efforts to mitigate the harmful effects of ad delivery in these domains, including their shortcomings and potential paths forward.

Bio: Aleksandra Korolova is an Assistant Professor of Computer Science and Public Affairs at Princeton University, where she is also affiliated with the Center for Information Technology Policy. She studies societal impacts of AI, and develops and deploys algorithms and technologies that enable data-driven innovations while preserving privacy, fairness, and robustness. She also designs and performs algorithm and AI audits. Aleksandra is a co-winner of the 2011 PET Award for outstanding research in privacy enhancing technologies for being among the first to identify privacy risks of microtargeted advertising. Her work on RAPPOR, the first commercial deployment of differential privacy, has been recognized by ACM Conference on Computer and Communications Security 2024 Test-of-Time Award. Aleksandra's research on discrimination in ad delivery has received the 2019 CSCW Honorable Mention Award and Recognition of Contribution to Diversity and Inclusion, was a runner-up for the 2021 WWW Best Student Paper Award, and was a winner of the 2025 FAccT Best Paper Award. Aleksandra is a recipient of the Presidential Early Career Award for Scientists and Engineers, a Sloan Research Fellowship and the NSF CAREER Award.

If you would like to attend this virtual talk, please register using the link below:

https://ubc.zoom.us/meeting/register/bTdpB5a2S5SngAX1d1ch6Q

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This talk is one of the Statistics Equity, Diversity and Inclusion Speaker Series. For more information, please visit: https://www.stat.ubc.ca/seminar-series

Score-based diffusion models have emerged as a powerful framework for generative modeling, progressively transforming noise into structured data samples when access to a dataset is available. In this talk, we explore how to extend these ideas to the sampling setting, where the target distribution is only known up to a normalizing constant. After reviewing the fundamentals of diffusion models, we highlight a key observation: the score function central to these methods can be expressed as an expectation with respect to a time-dependent distribution with known unnormalized density. This perspective motivates Stochastic Localization via Iterative Posterior Sampling (SLIPS), an approach that estimates the score function using Monte Carlo methods and leverages it to construct a denoising process. We will examine the theoretical underpinnings of SLIPS, with particular emphasis on its main limitation, the duality of log-concavity, which restricts its practical applicability. Building on this, I will present a new approach to Iterative Posterior Sampling (forthcoming work) that bypasses explicit score estimation altogether, leading to significantly improved scalability. While this method remains affected by the same duality phenomenon, we will see that its impact is mitigated in practice.

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