Difference between revisions of "SOCR News HDDA14 2025"
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== Talk== | == Talk== | ||
| + | * '''Speaker''': [https://www.socr.umich.edu/people/dinov/ Ivo D. Dinov] ([https://www.socr.umich.edu/ SOCR/Umich]) | ||
* '''Title''': ''Spacekime Representation, Statistical Inference, and AI prediction using Repeated Measurement Longitudinal Data'' | * '''Title''': ''Spacekime Representation, Statistical Inference, and AI prediction using Repeated Measurement Longitudinal Data'' | ||
| + | * '''Logistics''': [https://www.cmich.edu/academics/colleges/college-science-engineering/departments-schools/statistics-actuarial-and-data-sciences/HDDA Wed 8/20/25, Speaker 12, 1-1:30PM] | ||
| + | * '''Slides''': [https://wiki.socr.umich.edu/images/1/1c/Dinov_Spacekime_2025_Slidedeck_HDDA_2025.pdf Slidedeck] | ||
* '''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 presentation will cover some of the core elements of spacekime analytics, including | * '''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 presentation will cover some of the core elements of spacekime analytics, including | ||
| − | : Importing of repeated measurement longitudinal data, | + | :: Importing of repeated measurement longitudinal data, |
| − | : Numeric (stitching) and analytic (Laplace) kimesurface reconstruction from time-series 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], | + | :: 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, | + | :: 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, | + | :: 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), | + | :: 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. | + | :: Statistical comparison, topological quantification, and analytical inference using kimesurface representations of repeated-measurement longitudinal data. |
==Resource== | ==Resource== | ||
Latest revision as of 13:00, 7 August 2025
Contents
SOCR News & Events: 14th High Dimensional Data Analysis Workshop (HDDA-XIV) at the CMU Biological Station, Beaver Island, Lake Michigan
The 14th HDDA symposium will be of interest to researchers, students, and faculty engaged in data-driven, model-based statistical and model-free ML/AI analytical projects who are interested to present and/or learn new methods. Details are provided below.
Logistics
- Event: 14th High Dimensional Data Analysis (HDDA-XIV) Symposium
- Dates: Aug. 19-22, 2025
- Place: Central Michigan University Biological Station on Beaver Island, Lake Michigan (see video)
- Foci:
- Highlight and expand the breadth of existing methods in high-dimensional data analysis and their potential for the advance of STEM, physical and bio sciences.
- Identify important challenges and directions for future research in the theory of regularization methods and variational inference, in algorithmic development, and in methodology for different application areas, facilitate collaboration between theoretical and subject-area researchers (e.g., biostatistics, (bio)physics, econometrics, finance, social science).
- Provide networking opportunities to meet and interact with leading researchers in the area.
- Organizers/Presenters: The organizers are still accepting invited and contributed session proposals and presentation abstracts. If you are interested, please contact the Chair of Scientific Committee, Prof. S. Ejaz Ahmed, [email protected].
- Student Support: Some need-based student travel scholarships may be provided.
- Registration: Early registration (before May 1st): Students: 150; Other participants: 200
Talk
- Speaker: Ivo D. Dinov (SOCR/Umich)
- Title: Spacekime Representation, Statistical Inference, and AI prediction using Repeated Measurement Longitudinal Data
- Logistics: Wed 8/20/25, Speaker 12, 1-1:30PM
- Slides: Slidedeck
- 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 presentation will cover some of the core elements of spacekime analytics, including
- 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.
Resource
- Spacekime App
- Test Data
- Test sMRI 3D data (nii.gz)
- Test 4D fMRI data (nii.gz)
- Test PET 3D data (nii.gz)
- SOCR BrainViewer
- Book: Data Science - Time Complexity, Inferential Uncertainty, and Spacekime Analytics and Spacekime/TCIU website
- TCIU Basic Spacekime Predictive Analytics
- Strategies for Mapping Repeated Measurement Longitudinal Data (Time-series) to 2D Manifolds (Kimesurfaces)
- The Kime-Phase Problem and Kime-Phase Tomography (KPT)
![[1]](/index.php/File:ComplexValued_KimeIndexed_Kimesurface.gif)
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