Difference between revisions of "SOCR JMM 2026"
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* ''Date'': Tuesday, January 6, 2026, 8:00-12:00 PM ET | * ''Date'': Tuesday, January 6, 2026, 8:00-12:00 PM ET | ||
** Tuesday 01/06/2026, 10:00 - 10:30 AM | ** Tuesday 01/06/2026, 10:00 - 10:30 AM | ||
| − | ** Reference ID: 51462, Title of Paper: | + | ** Reference ID: 51462, Title of Paper: [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Paper/51462Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes] |
* ''Location'': Room 204C, [https://eventsdc.com/venue/walter-e-washington-convention-center Walter E. Washington Convention Center], 801 Allen Y. Lew Place NW, Washington, DC 20001 | * ''Location'': Room 204C, [https://eventsdc.com/venue/walter-e-washington-convention-center Walter E. Washington Convention Center], 801 Allen Y. Lew Place NW, Washington, DC 20001 | ||
Revision as of 16:31, 23 December 2025
Contents
SOCR News & Events: 2026 JMM/AMS Special Session on Mathematical Foundation of Machine Learning
Session Overview
- 2026 AMS/JMM: Annual 2026 JMM Congress
- Session SS58A: AMS Special Session on Mathematical Foundation of Machine Learning, I
- Date: Tuesday, January 6, 2026, 8:00-12:00 PM ET
- Tuesday 01/06/2026, 10:00 - 10:30 AM
- Reference ID: 51462, Title of Paper: Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes
- Location: Room 204C, Walter E. Washington Convention Center, 801 Allen Y. Lew Place NW, Washington, DC 20001
- Abstract: This special session focuses on the rigorous mathematical foundations underlying modern machine learning. Topics include, but are not limited to, operator theory, functional analysis, optimization, linear/multilinear algebra, metric learning, and approximation theory of neural networks. We welcome contributions that deepen understanding of data-driven algorithms through fundamental mathematical inquiry, emphasizing theoretical rigor in exploring the principles driving machine learning.
Abstract Submission
- Abstract Submission: Abstracts must be submitted through the AMS portal.
- Submission Period: July 10, 2025 -- Tuesday, September 9, 2025
Program
... pending ...
Talk: Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes
- Authors: Ivo D. Dinov (UMich), Yueyang Shen (Umich), and Bojko N Bakalov (NCSU)
- Abstract: This talk will present a complex-time (kime) representation framework for modeling repeated measurement longitudinal processes. The induced kime-phase analytics (KPA) offer a mathematical-statistics foundation for developing advanced machine learning and artificial intelligence models of time-varying functional data. By jointly tracking the classical time dynamics and the intrinsic cross-sectional variability of the underlying process, KPA represents temporal data as rich tensor objects, kime-surfaces. These 2D manifolds are parameterized by a complex variable \(\kappa =t e^{i\theta}\), where the kime magnitude \(t=|\kappa |\in \mathbb{R}^+\) is the longitudinal event order (classical time), and the kime phase \(\theta \sim \Phi_{S^1}\) captures the intrinsic stochastic variation of the longitudinal process. Inspired by quantum tomography and grounded in differential geometry, KPA enables reconstruction of latent phase distributions from observable data. As time permits, we will discuss open problems and show biomedical applications.
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