SOCR News ISS JSM 2025
Contents
SOCR News & Events: 2025 Joint Statistical Meeting, Nashville, TN
The 2025 Joint Statistical Meeting (JSM) will feature an invited special session entitled Statistical Inference and AI Modeling of High-Dimensional Longitudinal Data.
Session Logistics
- Title: Statistical Inference and AI Modeling of High-Dimensional Longitudinal Data
- Organizer: Ivo Dinov (Michigan)
- Chair: Chunming Zhang, University of Wisconsin-Madison
- Speakers: Changbo Zhu, University of Notre Dame, Yi Zhao, Indiana University, and Yueyang Shen (Michigan)
- Discussant: Ivo Dinov (Michigan), Statistics Online Computational Resource (SOCR)
- Date/Time: ...tbd... US ET
- Venue: ...tbd...
- Registration: ...tbd...
- Conference: []
- Format: ...tbd...
Sponsors
- Section on Statistics in Imaging
- International Association for Statistical Computing
- International Statistical Institute
Session Description
In support of the for JSM 2025 theme, "Statistics, Data Science, and Al Enriching Society," this invited special session will bring together a diverse group of academic researchers, each contributing to the cutting-edge intersections of statistical learning methods, Al applications, and high-dimensional data analysis.
- Changbo Zhu from Notre Dame will delve into "Topological Modeling and Analysis of Functional Neuroimaging Data," exploring how topological methods can provide deeper understanding of brain activity and structure through neuroimaging.
- Yi Zhao from Indiana University will present on "Longitudinal and Covariance Regression of High-Dimensional Data," addressing the challenges and methodologies for analyzing data with both temporal and complex covariate structures.
- Yueyang Shen from the University of Michigan will then discuss "Deep Learning Invariance and Statistical Estimation Equivariance with Applications," offering insights into how deep learning models can maintain robustness and consistency across varying conditions, and how this is applied in real-world data scenarios.
In this session, Changbo Zhu, Yi Zhao, and Yueyang Shen will present cutting-edge methodologies that harness the power of statistical learning, topological modeling, and deep learning to advance the analysis of complex, high-dimensional spatiotemporal data in various scientific domains. Speakers will demonstrate topological methods for handling challenges related to neuroimaging data complexity and scale and provide new perspectives on brain connectivity and function. Recent work on longitudinal and covariance regression in high-dimensional data will expose temporal structures involving a large number of covariates. The topics will cover deep learning invariance and equivariance, which characterize the robustness and generalizability of various AI models in real-world applications. All talks will emphasize challenges and opportunities in statistical and Al techniques to extract meaningful unbiased insights from complex data. This session aligns with the overarching conference theme "Statistics, Data Science, and Al Enriching Society" and will highlight applications in neuroscience, longitudinal studies, and AI forecasting, and trustworthy decision-making.
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