SOCR APS GDS VTS SK 2025
Contents
[hide]SOCR News & Events: 2025 APS GDS Virtual Tutorial Series
APS GDS Virtual Tutorial Series
- American Physical Society (APS)
- Group on Data Science (GDS)
- All APS GDS Virtual Tutorial Series Talks are archived and available on YouTube.
April 25, 2025 APS GDS Virtual Tutorial Series Presentation
- Presenter: Ivo Dinov (University of Michigan), co-author Yueyang Shen (Michigan)
- Title: Complex-time (kime) Representation, Statistical Inference, and AI prediction using Repeated Measurement Longitudinal Data
- VTS Chair/Host: Julie Butler, University of Mount Union
- Date/Time/Place: 11:30 AM ET, Friday, April 25, 2025, ZM link TBD
- Abstract:
- Complex-time (kime) representation of repeated measurement longitudinal processes paves the way for advanced spacekime statistical inference and artificial intelligence (AI) applications. Extending time into the complex plane offers a unified framework connecting fundamental quantum mechanics principles, statistical dynamics, and machine learning. Kime representation enhances both model-based statistical inference techniques – utilizing classical probability distributions – and model-free AI prediction and classification algorithms – relying on data and generalized functions. Many open mathematical-physics problems emerge from this formulation, including definition and interpretation of a consistent spacekime-metric tensor and classification of alternative time-series to kime-surfaces transformations. Simulations and observed neuroimaging data demonstrate the utility of complex-time representation and the induced spacekime analytics. These methods enable forward prediction by extrapolating processes beyond their observed timespan and facilitate group comparisons based on corresponding kime surfaces. Additionally, they allow for statistical quantification of differences between experimental groups and conditions, support topological kime surface analysis, and enhance AI prediction for repeated measurement longitudinal data.
- This interactive tutorial will present hands-on the core elements of spacekime analytics. These include
- Importing of repeated measurement longitudinal data,
- Numeric (stitching) and analytic (Laplace) kimesurface reconstruction from time-series data,
- Forward prediction modeling extrapolating the process behavior beyond the observed time-span [0,T],
- Group comparison discrimination between cohorts based on the structure and properties of their corresponding kimesurfaces. For instance, statistically quantify the differences between two or more groups,
- Unsupervised clustering and classification of individuals, traits, and other latent characteristics of cases included in the study,
- Construction of low-dimensional visual representations of large repeated measurement data across multiple individuals as pooled kimesurfaces (parameterized 2D manifolds),
- Statistical comparison, topological quantification, and analytical inference using kimesurface representations of repeated-measurement longitudinal data.
Resources
- Book: Data Science - Time Complexity, Inferential Uncertainty, and Spacekime Analytics and Spacekime/TCIU website
- TCIU Basic Spacekime Predictive Analytics
- for Mapping Repeated Measurement Longitudinal Data (Time-series) to 2D Manifolds (Kimesurfaces)
- The Kime-Phase Problem and Kime-Phase Tomography (KPT)
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