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.

The van Eeden seminar is a yearly event in which graduate students vote for their favorite statisticians. The winner is contacted by the organizing committee and invited to give a talk in the department’s seminar. The speaker spends one or two days on-campus, and graduate students have the opportunity to have lunch and dinner with them.

THIS YEAR'S SPEAKER

The Constance van Eeden Speaker for 2026 is Dr. Ryan Tibshirani, Professor in the Department of Statistics at the University of California, Berkeley, and Principal Investigator in the Delphi Research Group. Before joining Berkeley, Dr. Tibshirani served as a faculty member in the Departments of Statistics and Machine Learning at Carnegie Mellon University from 2011 to 2022. He earned his Ph.D. in Statistics from Stanford University in 2011 under the supervision of Professor Jonathan Taylor, and his B.S. in Mathematics from Stanford University in 2007. 

Seminar Title: Online Conformal Prediction, Multi-Level Quantile Tracking, and Gradient Equilibrium

Event Date: Thursday, April 2nd, 2026. 10:30-12:00.
Location: ESB 5104, University of British Columbia*

Event registration: https://ubc.zoom.us/meeting/register/htGYWnrFSCWhHIs8-d4wqw

Abstract:

This talk is about uncertainty quantification for time series prediction.

The overarching goal is to provide easy-to-use algorithms with formal guarantees. The algorithms we present build upon ideas from conformal prediction and control theory, are able to prospectively model conformal scores in an online setting, and adapt to the presence of systematic errors due to seasonality, trends, and general distribution shifts. We will then discuss an extension of these ideas to the setting of probabilistic forecasting, which is essentially a generalization of the framework to handle vector-valued predictions, i.e., predictions which take the form of a set of ordered quantile forecasts at different probability levels. Finally, we will generalize this even further to discuss an abstract property in online learning called gradient equilibrium, which encapsulates these settings, and more.

Dr. Ryan Tibshirani has been invited to be this year’s van Eeden speaker by the graduate students in the Department of Statistics at the University of British Columbia. A van Eeden speaker is a prominent statistician who is chosen each year to give a lecture, supported by the UBC Constance van Eeden Fund. The 2024 seminar is additionally sponsored by the Canadian Statistical Sciences Institute (CANSSI), the Pacific Institute for the Mathematical Sciences (PIMS), and the Walter H. Gage Memorial Fund.

*The room location may change.

Weighting methods are essential tools for estimating causal effects in observational studies, with the goal of balancing pre-treatment covariates across treatment groups. Traditional approaches pursue this objective indirectly, for example, via inverse propensity score weighting or by matching a finite number of covariate moments, and therefore do not guarantee balance of the full joint covariate distributions. Recently, distributional balancing methods have emerged as robust, nonparametric alternatives that directly target alignment of entire covariate distributions, but they lack a unified framework, formal theoretical guarantees, and valid inferential procedures. We introduce a unified framework for nonparametric distributional balancing based on the characteristic function distance (CFD) and show that widely used discrepancy measures, including the maximum mean discrepancy and energy distance, arise as special cases. Our theoretical analysis establishes conditions under which the resulting CFD-based weighting estimator achieves root-N consistency. Since the standard bootstrap may fail for this estimator, we propose subsampling as a valid alternative for inference. We further extend our approach to an instrumental variable setting to address potential unmeasured confounding. Finally, we evaluate the performance of our method through simulation studies and a real-world application, where the proposed estimator performs well and exhibits results consistent with our theoretical predictions.

The paper is available at https://arxiv.org/abs/2601.15449

Bio:

Dr. Chan Park is an assistant professor at the University of Illinois Urbana-Champaign. His research focuses on causal inference in complex settings, including dependence among units and omitted variables. He specializes in applying nonparametric methods and semiparametric theory to address these challenges.

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

In practical regression applications, multiple covariates are often measured, but not all may be associated with the response variable. Identifying and including only the relevant covariates in the model is crucial for improving prediction accuracy. In this work, we develop a variational inference approach for estimation and variable selection in scalar-on-function regression, involving only functional covariates, and in partially functional regression models that also include scalar covariates. Specifically, we develop a variational expectation–maximization algorithm, with a variational Bayes procedure implemented in the E-step to obtain approximate marginal posterior distributions for most model parameters, except for the regularization parameters, which are updated in the M-step. Our method accurately identifies relevant covariates while maintaining strong predictive performance, as demonstrated through extensive simulation studies across diverse scenarios. Compared with alternative approaches, including BGLSS (Bayesian Group Lasso with Spike-and-Slab priors), grLASSO (group Least Absolute Shrinkage and Selection Operator), grMCP (group Minimax Concave Penalty), and grSCAD (group Smoothly Clipped Absolute Deviation), our approach achieves a superior balance between goodness-of-fit and sparsity in most scenarios. We further illustrate its practical utility through real-data applications involving spectral analysis of sugar samples and weather measurements from Japan.
 

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

Machine learning methods are now increasingly studied and used in National Statistical Offices, in particular to handle item nonresponse, where some survey respondents answer certain questions but leave others missing. In most surveys, item nonresponse affects key study variables, and imputation is routinely used to handle the resulting missing data. Standard parametric imputation methods can support rigorous inference when their modeling assumptions are approximately correct. However, when the imputation model is misspecified, the resulting inferences may be potentially misleading. Machine learning offers a flexible alternative by learning complex relationships between variables from the data, which can reduce the risk of misspecification. At the same time, this flexibility introduces new challenges for survey inference, since modern learning algorithms may converge more slowly than classical parametric models and may not automatically deliver valid uncertainty quantification. In this talk, I will present a survey sampling extension of the double/debiased machine learning framework of Chernozhukov et al. (2018). The proposed approach combines machine learning-based imputation with design-based survey weighting and an orthogonalized estimating strategy, leading to root-$n$ consistent and asymptotically normal estimation of population means under realistic conditions. We also develop a consistent variance estimator, yielding asymptotically valid confidence intervals while allowing the use of a wide range of machine learning algorithms. I will briefly discuss aggregation procedures and conclude with simulation results illustrating the performance of the proposed methodology.
 

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.

In this talk, we propose a state space model for functional time series data, which extends many time series models to the realm of functional data. Most notably, we introduce the Functional ARMAX process (FARMAX), which is developed in the fully functional setting, i.e. without relying on projection onto a finite number of basis functions. These models are fit via our fully functional variant of the Kalman filter and smoother methods. The theoretical soundness of this approach is proven using tools from the theory of Gaussian measures in locally convex spaces. As an application, we consider signals data collected from small wearable medical tri-axial accelerometers affixed to a patient's wrists or ankles. Each device collects three time series (x, y, z directions) at 100Hz and can continuously collect data for 14 days.

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