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**Seminars will be held In Person unless otherwise noted**
Spring 2025
Seminar One
February 27, 2025
Griffin Floyd Hall, Room 100
Peijun Sang
University of Waterloo
Title:
Functional principal component analysis with informative observation times
Abstract:
Functional principal component analysis has been shown to be invaluable for revealing variation modes of longitudinal outcomes, which serves as important building blocks for forecasting and model building. Decades of research have advanced methods for functional principal component analysis often assuming independence between the observation times and longitudinal outcomes. Yet such assumptions are fragile in real-world settings where observation times may be driven by outcome-related reasons. Rather than ignoring the informative observation time process, we explicitly model the observational times by a general counting process dependent on time-varying prognostic factors. Identification of the mean, covariance function, and functional principal components ensues via inverse intensity weighting. We propose using weighted penalized splines for estimation and establish consistency and convergence rates for the weighted estimators. Simulation studies demonstrate that the proposed estimators are substantially more accurate than the existing ones in the presence of a correlation between the observation time process and the longitudinal outcome process. We further examine the finite-sample performance of the proposed method using the Acute Infection and Early Disease Research Program study.
Seminar Two
March 6, 2025
Griffin-Floyd Hall, Room 100
Alex Peterson
Brigham Young University
Title:
Modeling and Regularized Estimation of the Covariance Operator of Multivariate Functional Data
Abstract:
Functional MRI scans result in a set of regional BOLD signals for each subject, which can be modeled as multivariate functional data. The covariance operator of multivariate functional data is a complex object that can be difficult to estimate, especially if the multivariate dimension is large, making extensions of statistical methods for standard multivariate data to the functional data setting challenging. Compared with multivariate data, a key difficulty is that the covariance operator is compact and thus does not have a bounded inverse. This talk will address covariance modelling for multivariate functional data utilizing nested structures of separability, as well as regularized estimation of the functional precision operators.
Seminar Three
March 13, 2025
Zoom link: https://ufl.zoom.us/j/98708991173
Thomas Bartlett
University College, London
Title:
Using stochastic network theory to inform unsupervised learning from genomic count data.
Abstract:
Important tasks in the study of genomic data include the identification of groups of similar cells (for example by clustering), and visualization of data summaries (for example by dimensional reduction). In this talk, I will present a novel view of these tasks in the context of single-cell genomic data. To do so, I propose modelling the observed count-matrices of genomic data by representing these measurements as a bipartite network with multi-edges. Starting with this first-principles network model of the raw data, I will show improvements in clustering single cells via a suitably-identified d-dimensional Laplacian Eigenspace (LE) using a Gaussian mixture model (GMM-LE), and apply UMAP to non-linearly project the LE to two dimensions for visualization (UMAP-LE). From this first-principles viewpoint, the LE representation of the data-points estimates transformed latent positions (of genes and cells), under a latent position statistical model of nodes in a bipartite stochastic network. By applying this proposed methodology to data from three recent genomics studies in different biological contexts, I will show how clusters of cells independently learned by this proposed methodology are found to correspond to cells expressing specific marker genes that were independently defined by domain experts, with an accuracy that is competitive with the industry-standard for these data. I will then show how this novel view of these data can provide unique insights, leading to the identification of a LE breast-cancer biomarker that significantly predicts long-term patient survival outcome in two independent validation cohorts with data from 1904 and 1091 individuals.
Seminar Four
March 27, 2025
Griffin-Floyd Hall, Room 100
Rajarshi Guhaniyogi
Texas A & M University
Title:
TBD
Abstract:
TBD
Seminar Five
April 3, 2025
Griffin-Floyd Hall, Room 100
Victor Patrangenaru
Florida State University
Title:
TBD
Abstract:
TBD
Seminar Six
April 17, 20215
Griffin-Floyd Hall, Room 100
Abhirup Dutta
Johns Hopkins University
Title:
TBD
Abstract:
TBD