Q1 – 2025 Research Roundup
Title: Collaborative Research: Partial Priors, Regularization, and Valid & Efficient Probabilistic Structure Learning
Amount: $160,000
Funding Agency: National Science Foundation (NSF)
Period: 07/01/2024- 06/30/2027
Abstract: Developing new perspectives and methods for structure learning and, more generally, handling regularization with “partial prior information”. This involves theoretical developments, computational and application-specific investigations, along with educational efforts.
Title: Hypothesis Testing using Phylogenies for the 21st Century
Amount: $75,000
Funding Agency: National Institutes of Health (NIH)
Period: 03/15/2024-02/29/2028
Abstract: This project will support continued work on statistical modeling and methodology aspects of the DNA sequence analysis software package HyPhy that Sergei Kosakovsky Pond and I have developed over the past 25 years. In this round of support we will be addressing the robustness of widely used statistical methods, and also exploring the general behavior of statistical methods as computational advances have allowed us to use methods with ever-increasing complexity. The project will involve the PI (Spencer Muse) and one graduate research assistant.
Title: Projecting Flood Frequency Curves Under a Changing Climate Using Spatial Extreme Value Analysis
Amount: $300,000
Funding Agency: National Science Foundation (NSF)
Period: 06/01/2022-05/31/2025
Abstract: Climate change is often described in terms of the mean, but it will be felt most acutely in terms of extreme events. In particular, the International Panel of Climate Changes recent Sixth Assessment warns of an increase in the likelihood and magnitude of extreme flooding events in upcoming decades. Understanding the spatiotemporal variability of these changes is critical to mitigating their impact. However, current methods for spatial extreme value analysis are limited in their modeling flexibility and computational capabilities, and thus methodological work is required to analyze extreme events across the United States. Therefore, in this proposal, we develop new methodological and computational tools for spatial extreme value analysis and apply them to forecasting flood risk under a changing climate. The analysis combines fifty years of annual maximum streamflow observations at hundreds of gauges provided by the United States Geological Survey with CMIP6 climate model output produced
Title: Comprehensive Tools and Models for Addressing Exposure to Mixtures During Environmental Emergency-Related Contamination Events (The TAMU Superfund Research Program)
Amount: $1,361,904
Funding Agency: National Institute of Environmental Health Sciences (NIEHS)
Period: 09/01/2017 – 06/30/2025
Abstract: The Texas A&M University Superfund Research Center brings together a team of scientists from biomedical, geosciences, data science and engineering disciplines to design comprehensive solutions for complex exposure- and hazard-related challenges. This partnership was formed around a common goal: to develop, apply, and translate a comprehensive set of tools and models that will aid in addressing human health consequences of exposure to mixtures during environmental emergency-related contamination events. Dr. Wright is the lead PI for a subcontract from TAMU, and will act as co-investigator to the Data Sciences Core.
Title: Comprehensive White-matter Microstructure-informed Analytical Methods to Elucidate Neurobiological Mechanisms of Sports-related Concussion (SRC)
Amount: $130,536
Funding Agency: National Institutes of Health (NIH)
Period: 04/15/2020-03/31/2025
Abstract: Dr. Xiao’s expertise is in functional data analysis and high dimensional data analysis. He will assist the PIs (Drs Wu and Harezlak) in methodology developments and in particular he will develop novel functional and high dimensional data methods for combining and extracting signals from MRI data (e.g., streamlines), evaluating their differences in the different groups of subjects (control vs concussed vs exposed), and identifying imaging biomarkers that are predictive of injury and recovery from injury.
Title: Spatial Causal Inference for Wildland Fire Smoke Effects on Air Pollution and Health
Amount: $1,158,927
Funding Agency: National Institutes of Health (NIH)
Period: 04/01/2020-01/31/2025
Abstract: Wildland fire smoke is a major contributor to air pollution in the United States and is associated with a wide range of health risks. The number and intensity of wildland fires are expected to increase with a changing climate; therefore, there is a pressing need to accurately quantify the extent to which wildland fire smoke contributes to air pollution levels and corresponding health burden, and to evaluate the effectiveness of preventative measures to mitigate the health burden. However, this work presents many challenges. Clearly exposure to wildland fires cannot be randomized, so we rely on spatially-correlated observational data. While there is an impressive literature on causal inference for independent data, the methods available for spatial data are limited. Progress in the spatial setting has been slow due to complexities induced by spatial correlations and interference, i.e., the effect of treatment at one location depends on the response at nearby locations.
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