To join this seminar virtually: Please request Zoom connection details from ea [at] stat.ubc.ca

Presentation 1

Time: 11:00am – 11:30am

Speaker: Xihan (Henry) Qian, UBC Statistics MSc student

Title:  Modeling Diatom Dynamics and Environmental Drivers Using Functional Regression and Rank-Based Model Selection

Abstract: In this project, we analyze the relationship between diatom concentrations and environmental drivers in the Salish Sea using functional data analysis (FDA). Daily measurements of solar radiation, wind speed, air temperature, and diatom levels from 2007 to 2024 are treated as smooth functions over time. To capture the delayed effects of environmental variables on diatom dynamics, we apply a historical functional linear model and estimate a time-varying coefficient surface using finite element basis functions defined over a triangular domain. Smoothness is enforced using directional roughness penalties. Two model selection strategies are compared: one based on the Bayesian Information Criterion (BIC), and another prioritizing predictive performance using mean squared error and rank correlation. We show how the choice of tuning parameters affects predictive accuracy and highlight patterns in the estimated effects of environmental variables on diatom levels.

Presentation 2

Time: 11:30am – 12:00pm

Speaker: Joey Hotz, UBC Statistics MSc student

Title: The Rocky Road Toward Effective Vanilla Bayesian Optimization in High-Dimensional Search Spaces

Abstract: Bayesian optimization (BayesOpt) is a well-established statistical methodology for efficiently finding the true optimum value of a black-box function. A common concern with Bayesian optimization is the "Curse of Dimensionality", as these methods often struggle for input spaces with many parameters unless the algorithm is adjusted accordingly. Despite the prevalence of these challenges, a recently published paper empirically demonstrated that under certain specifications for the surrogate model, the sole adjustment required to make Bayesian optimization effective for higher-dimensional problems is to simply scale the prior distribution for the model based on the dimensionality of the search space. In this presentation, we discuss the background, methodology, and findings of the aforementioned paper. Additionally, we significantly broaden the scope of their simulation study to a wider class of statistical models to evaluate the robustness of their stated result.