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Highly motivated students with a strong passion for developing statistical, machine learning, and AI methodologies are warmly encouraged to join our group.
Statistical Methods for Complex-Oriented Functional Data
Statistical Methods for Complex-Oriented Functional Data
Functional data, where each observation is a curve, surface, or high-dimensional function varying over a continuum, arise in a wide range of scientific disciplines, including neuroscience, environmental science, and medical imaging. In many modern applications, the functional observations are not only high-dimensional but also exhibit complex structure and orientation.
Spatial/Spatiotemporal Data Analysis
Spatial/Spatiotemporal Data Analysis
Spatial and spatiotemporal data analysis refer to the set of statistical and computational techniques used to analyze data that are indexed by spatial (location) or both spatial and temporal (space-time) dimensions. This field is critical in disciplines such as environmental science, epidemiology, geosciences, public health, agriculture, urban planning, and more.
Subgroup Analysis for Functional Data
Subgroup Analysis for Functional Data
We propose a sparse multi-group functional linear regression model to simultaneously estimate multiple coefficient functions and identify groups, such that coefficient functions are identical within groups and distinct across groups. By borrowing information from relevant subgroups of subjects, our method enhances estimation efficiency while preserving heterogeneity in model parameters and coefficient functions. We use an adaptive fused lasso penalty to shrink coefficient estimates to a common value within each group.
Statistical Epidemiology for COVID-19 Prediction
Statistical Epidemiology for COVID-19 Prediction
This project aims to provide a user-friendly tool to visualize, track, and predict real-time infected/death cases of COVID-19 in the U.S., based on our collected data and proposed methods, and thus further illustrate the spatiotemporal dynamics of the disease spread and guide evidence-based decision making.
Application to Spatially Resolved Transcriptomics
Application to Spatially Resolved Transcriptomics
Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. We introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability.
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