统计与数据科学系系列学术报告之四百六十九期

时间： 2025年5月15日（周四）16:00-17:00

主持人：复旦大学管理学院统计与数据科学系徐勤丰副教授

地点：史带楼304室

报告人：王亮亮博士Dr. Liangliang Wang

Associate Professor, Simon Fraser University, Canada

题目：Robust and Scalable Bayesian Dimension Reduction

摘要：Dimension reduction plays a central role in modern statistical analysis, particularly when dealing with high-dimensional or complex structured data. In this talk, I present recent advances in Bayesian dimension reduction methods that improve robustness and computational efficiency. First, I introduce a robust Bayesian functional principal component analysis (RB-FPCA) framework for analyzing functional data, even when observations are sparse or contaminated by outliers. By leveraging skew-elliptical distributions, RB-FPCA accurately captures principal modes of variation and offers improved estimation of the covariance structure. Next, I present a generalized Bayesian multidimensional scaling (GBMDS) framework that enables flexible embeddings of multivariate data under non-Gaussian error structures and user-defined dissimilarity measures. A key innovation in both models is the use of annealed Sequential Monte Carlo (ASMC) for posterior inference, which overcomes limitations of traditional MCMC by enhancing posterior exploration and providing accurate marginal likelihood estimates for model comparison. Applications to environmental, biological, and synthetic datasets demonstrate that these methods offer superior performance in robustness, scalability, and uncertainty quantification compared to existing approaches.

个人简介：Liangliang Wang is an Associate Professor in the Department of Statistics and Actuarial Science at Simon Fraser University, where she has been a faculty member since 2013. Dr. Wang completed her Ph.D. in statistics at the University of British Columbia and her master's degree in statistics at McGill University. Her research interests focus on computational statistics and statistical machine learning. Her favourite applications come from important scientific questions raised in genetics, biology, public health, and environmetrics. Dr. Wang is interested in tackling the computational issues in complex statistical models applied to large-scale data. She has published about 55 papers in statistical journals and machine learning conferences. Website: https://www.sfu.ca/~lwa68/