line graphs displayed across a digital screen


Dr. Anton Westveld

Dr. Anton Westveld, Australian National University presents Bayesian Melding: Deterministic to the Stochastic Case

Date: Thursday, November 9, 2023

Mathematical models are widely employed across scientific disciplines, especially when field data are sparse. Where field data are available, they are often used informally as “ground truth” for model calibration. Formal incorporation of theoretical relationships with data via a likelihood can be formulated as, say, a statespace model that gives rise to a well-defined likelihood, or a black-box model that renders the likelihood intractable. In contrast, Bayesian melding (Poole Raftery, 2000 in J

Dr. Hongxiao Zhu

Dr. Hongxiao Zhu, Associate Professor, Virginia Tech presents Region Detection on Functional Data

Date: Thursday, November 2, 2023

High-dimensional functional data, such as brain images, spectral curves, and engineering signals, often contain a wealth of information. However, the abundance of information does not automatically lead to a clearer understanding of the underlying mechanisms. In fact, a large portion of the information carried by functional data may be noise, unrelated to the issues of interest. This presentation focuses on the challenge of identifying important local regions within functional data. I will intro

Dr. Suman Majumder

Dr. Suman Majumder, Assistant Professor, Department of Statistics, University of Missouri-Columbia presents Multivariate Cluster Point Process Model: Parent Location Improves Inference for Complex Biofilm Image Data

Date: Thursday, October 19, 2023

A common challenge in spatial statistics is to quantify the spatial distributions of clusters of objects. Clusters of similar or dissimilar objects are encountered in many fields, including field ecology, astronomy, and biomedical imaging. Frequently used approaches treat each cluster’s central object as latent, but it is often the case that cells of one or more types cluster around cells of another type. Such arrangements are common, for example, in microbial biofilm, in which close inter-speci

Ed Boone

Dr. Edward Boone, Professor, Department of Statistical Sciences and Operations Research, Virginia Commonwealth University presents Statistical Inference on Fractional Partial Differential Equations

Date: Thursday, October 12, 2023

Researchers are often confronted with connecting theoretical models with real world data. This is very common in the petroleum resources industry. Specifically, they wish to understand the pressure of gas in a porous substance. A wealth of mathematical models have been developed based on partial differential equations and have been studied extensively. These modeling efforts have been extended to using Fractional Partial Differential Equations (FPDE) to better capture various attributes of the p

Grace Chiu

Dr. Grace S. Chiu, Professor, Virginia Institute of Marine Science presents LaCSH: A Spatial and Causal Framework for Modeling Socio-Economic Health

Date: Thursday, October 5, 2023

We develop a model-based Latent Causal Socioeconomic Health (LaCSH) index, with uncertainty bounds, at the national level. Extending the latent health factor index (LHFI) modeling approach to assess ecosystem health, LaCSH integratively models the hierarchical relationship among the nation’s societal health or well-being (latent / intangible), socio-economic metrics (e.g., GDP), the covariates that drive the notion of well-being (e.g., natural resources), and a continuous variable that reflects

Dr. Shan Yu

Dr. Shan Yu, Department of Statistics, University of Virginia presents Big Spatial Data Learning: A Parallel Solution

Date: Thursday, September 21, 2023

Nowadays, we are living in the era of “Big Data.” A significant portion of big data is big spatial data captured through advanced technologies or largescale simulations. Explosive growth in spatial and spatiotemporal data emphasizes the need for developing new and computationally efficient methods and credible theoretical support tailored for analyzing such large-scale data. Parallel statistical computing has proved to be a handy tool when dealing with big data. In general, it uses multiple proc

Dr. Annie Qu

Dr. Annie Qu, Chancellor's Professor, Department of Statistics, University of California, Irvine presents De-confounding Causal Inference Using Latent Multiple-Mediator Pathways

Date: Friday, September 15, 2023

Causal effect estimation from observational data is one of the essential problems in causal inference. However, most estimation methods rely on the strong assumption that all confounders are observed, which is impractical and untestable in the real world. We develop a mediation analysis framework inferring the latent confounder for debiasing both direct and indirect causal effects. Specifically, we introduce generalized structural equation modeling that incorporates structured latent factors to

Check out our past events.