SOCR JMM 2026 - Revision history

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‎Session Program

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‎Session Program

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‎Program

Dinov: /* Talk: Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes */

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‎Talk: Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes

Dinov: /* Talk: Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes */

2025-12-30T22:11:35Z

‎Talk: Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes

Dinov: /* Session Overview */

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‎Session Overview

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‎Session Overview

Dinov: /* Talk: Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes */

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‎Talk: Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes

Dinov: /* Session Overview */

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‎Session Overview

Dinov: /* Session Overview */

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‎Session Overview

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 * 8:00 AM: Riemannian Optimization on Manifolds of Low-Rank Tensors, Maryam Bagherian, Idaho State University, POCATELLO, ID
 * 8:30 AM: Learning on manifolds without manifold learning, Ryan Michael O'Dowd, claremont Graduate University, Erie, CO and Hrushikesh Mhaskar, claremont Graduate University
 * 9:00 AM: Continuous Symmetry Discovery and Enforcement using the Lie Derivative, Benjamin Shaw, Utah State University

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 == [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Session/13818  Session Program]==
-: '''Location''': Room 204C (Level 2, Walter E. Washington Convention Center)
+'''Location''': Room 204C (Level 2, Walter E. Washington Convention Center)
-: '''Session''': [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Session/13818 AMS Special Session on Mathematical Foundation of Machine Learning, I]
+'''Session''': [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Session/13818 AMS Special Session on Mathematical Foundation of Machine Learning, I]
 * 8:00 AM: Riemannian Optimization on Manifolds of Low-Rank Tensors, Maryam Bagherian, Idaho State University, POCATELLO, ID
@@ Line 41: / Line 41: @@
 * 9:30 AM: Uncovering Latent Structure in Neural Networks through Local CorEx, Thomas Jordan Kerby, Brigham Young University and Kevin Moon, Utah State University, Providence, UT
-* '''10:00 AM: Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes, Ivo D. Dinov, University of Michigan, Ann Arbor, MI, Yueyang Shen, University of Michigan and Bojko N Bakalov, North Carolina State University'''
+* [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Paper/51462 10:00 AM: Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes, Ivo D. Dinov, University of Michigan, Ann Arbor, MI, Yueyang Shen, University of Michigan and Bojko N Bakalov, North Carolina State University]
 * 10:30 AM: Tensor denoising, Harm Derksen, Northeastern University, Boston, MA

← Older revision		Revision as of 22:16, 30 December 2025
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−	== Program==	+	== [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Session/13818 Session Program]==
−	~~... pending ...~~

		+	: '''Location''': Room 204C (Level 2, Walter E. Washington Convention Center)
		+
		+	: '''Session''': [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Session/13818 AMS Special Session on Mathematical Foundation of Machine Learning, I]
		+
		+	* 8:00 AM: Riemannian Optimization on Manifolds of Low-Rank Tensors, Maryam Bagherian, Idaho State University, POCATELLO, ID
		+
		+
		+	* 8:30 AM: Learning on manifolds without manifold learning, Ryan Michael O'Dowd, claremont Graduate University, Erie, CO and Hrushikesh Mhaskar, claremont Graduate University
		+
		+
		+	* 9:00 AM: Continuous Symmetry Discovery and Enforcement using the Lie Derivative, Benjamin Shaw, Utah State University
		+
		+	* 9:30 AM: Uncovering Latent Structure in Neural Networks through Local CorEx, Thomas Jordan Kerby, Brigham Young University and Kevin Moon, Utah State University, Providence, UT
		+
		+	* '''10:00 AM: Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes, Ivo D. Dinov, University of Michigan, Ann Arbor, MI, Yueyang Shen, University of Michigan and Bojko N Bakalov, North Carolina State University'''
		+
		+	* 10:30 AM: Tensor denoising, Harm Derksen, Northeastern University, Boston, MA
		+
		+	* 11:00 AM: An adaptive framework for first order gradient methods with momentum, Yunrong Zhu, Idaho State University, Xiaozhe Hu, Tufts University, Sara Pollock, University of Florida, GAINESVILLE, FL and Zhongqin Xue, Tufts University, Medford, MA
		+
		+	* 11:30 AM: Categorical Foundations of Distributed Optimization and Learning, Tyler Evan Hanks, University of Florida, Matthew Klawonn, Air Force Research Lab, Evan Patterson, Topos Institute, Matthew Hale, Georgia Institute of Technology and James P Fairbanks, University of Florida, Gainesville, FL

	==Talk: [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Paper/51462 Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes]==		==Talk: [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Paper/51462 Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes]==

@@ Line 31: / Line 31: @@
 ==Talk: [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Paper/51462 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), [https://socr.umich.edu/docs/uploads/2026/JMM_2026_Slidedeck_SOCR_SKA_KPT_V3.pdf slidedeck]
+* ''Authors'': Ivo D. Dinov (UMich), Yueyang Shen (UMich), and Bojko N Bakalov (NCSU), [https://socr.umich.edu/docs/uploads/2026/JMM_2026_Slidedeck_SOCR_SKA_KPT_V3.pdf slidedeck]
 * ''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.

@@ Line 31: / Line 31: @@
 ==Talk: [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Paper/51462 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)
+* ''Authors'': Ivo D. Dinov (UMich), Yueyang Shen (Umich), and Bojko N Bakalov (NCSU), [https://socr.umich.edu/docs/uploads/2026/JMM_2026_Slidedeck_SOCR_SKA_KPT_V3.pdf slidedeck]
 * ''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.

@@ Line 7: / Line 7: @@
 * ''2026 AMS/JMM'': Annual [https://jointmathematicsmeetings.org/jmm 2026 JMM Congress]
-* ''Session SS58A'': AMS Special Session on Mathematical Foundation of Machine Learning, I
+* [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Session/13818 ''Session SS58A'': AMS Special Session on Mathematical Foundation of Machine Learning, I]
 * ''Date'': Tuesday, January 6, 2026, 8:00-12:00 PM ET

@@ Line 11: / Line 11: @@
 * ''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: ''Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes''
+** 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

← Older revision		Revision as of 20:31, 23 December 2025
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−	==Talk: Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes==	+	==Talk: [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Paper/51462 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)		* ''Authors'': Ivo D. Dinov (UMich), Yueyang Shen (Umich), and Bojko N Bakalov (NCSU)