报告题目：High-dimensional covariate-augmented overdispersed Poisson factor model
报告人：刘伟 研究员
报告时间：2026年5月12日（周二）下午 15:00-16:00
报告地点：藕舫楼724室，腾讯会议号：520894443
主持人：曹春正 教授
报告人简介：
刘伟，四川大学数学学院特聘研究员，硕士生导师，入选四川大学“双百人才工程”计划。主持教育部海外优秀博士后项目和国自科青年基金等多个项目。2015年在西南大学数学与统计学院获学士学位，2020年在西南财经大学统计学院获博士学位，2021-2024年在新加坡国立大学杜克医学院从事博士后研究。长期从事统计机器学习与生物医学的交叉研究，在单细胞、空间转录组和空间多组学测序数据建模和算法开发等方面开展了系列研究工作。相关成果以第一作者或共同第一作者发表于Journal of the American Statistical Association，Biometrics, Cell, Nature communications等统计学和综合类权威期刊，相关成果开发成多个R/Python软件包累计下载超19万次。担任Nature Communications, Genome Biology, Biometrics, Biostatistics等多个国际期刊的审稿人。
报告简介：
The current Poisson factor models often assume that the factors are unknown, which overlooks the explanatory potential of certain observable covariates. This study focuses on high dimensional settings, where the number of the count response variables and/or covariates can diverge as the sample size increases. A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. A group of identifiability conditions are provided to theoretically guarantee computational identifiability. We incorporate the interdependence of both response variables and covariates by imposing a low-rank constraint on the large coefficient matrix. To address the computation challenges posed by nonlinearity, two high-dimensional latent matrices, and the low-rank constraint, we propose a novel variational estimation scheme that combines Laplace and Taylor approximations. We also develop a criterion based on a singular value ratio to determine the number of factors and the rank of the coefficient matrix. Comprehensive simulation studies demonstrate that the proposed method outperforms the state-of-the-art methods in estimation accuracy and computational efficiency. The practical merit of our method is demonstrated by an application to the CITE-seq dataset. A flexible implementation of our proposed method is available in the R package COAP, available at https://cran.r-project.org/package=COAP.
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数学与统计学院
江苏省应用数学（南京信息工程大学）中心
江苏省系统建模与数据分析国际合作联合实验室
江苏省统计科学研究基地
2026年5月11日
