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== [[SOCR_News | SOCR News & Events]]: 2023 JMM/AMS Special Session on ''Tensor Representation, Completion, Modeling and Analytics of Complex Data'' ==
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== [[SOCR_News | SOCR News & Events]]: 2026 JMM/AMS Special Session on ''Mathematical Foundation of Machine Learning'' ==
  
[[Image:BigData_AMS_JMM_2014.gif|250px|thumbnail|right| [https://jointmathematicsmeetings.org/meetings/national/jmm2021/2247_program_ss9.html 2023 JMM/AMS Tensor Analytics Session] ]]
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[[Image:BigData_AMS_JMM_2014.gif|250px|thumbnail|right| [https://jointmathematicsmeetings.org/meetings/national/jmm2026/jmm2026-agenda 2026 JMM/AMS Math Foundations of ML/AI] ]]
  
==Overview==
+
==Session Overview==
  
The accelerated rate of increase of data volume and heterogeneity requires novel mathematical foundations for representation, modeling, analysis and interpretation of complex multisource information. This special session will explore the mathematical, physical, and computational aspects of tensors, as one promising direction for data compression, classification, model-based and model-free inference. By bringing together a broad range of experts, the session will provide a platform for open exchange of ideas, reports of recent developments, cross-fertilization of new techniques, translation of mathematical models into data analytic techniques, and the embedding of application-specific constraints into mathematical formulations.  
+
* ''2026 AMS/JMM'': Annual [https://jointmathematicsmeetings.org/jmm 2026 JMM Congress]
  
The session talks will cover new mathematical, computational, and statistical approaches for tensor-based representation, modeling and inference with direct applications to high-dimensional and longitudinal data. Talks will cover coupled tensor-tensor completion strategies, complex time (kime) representation and tensor linear modeling of kimesurfaces, and current advances in tensor computing. Various data science, biomedical health, environmental, climate, and econometrics applications will be showcased.
+
* [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Session/13818 ''Session SS58A'': AMS Special Session on Mathematical Foundation of Machine Learning, I]
  
== Organizers==
+
* ''Date'': Tuesday, January 6, 2026, 8:00-12:00 PM ET
* [https://umich.edu/~dinov Ivo Dinov], [https://www.umich.edu University of Michigan], [https://www.socr.umich.edu SOCR], [https://midas.umich.edu MIDAS].
+
**  Tuesday 01/06/2026, 10:00 - 10:30 AM
* [https://experts.umich.edu/discover/experts_publication?and_facet_profiles_author=5597 Joshua Welch], [https://www.umich.edu University of Michigan], [https://welch-lab.github.io/ WelchLab].
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** 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]
  
==Session Logistics==
+
* ''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
[[Image:JMM_2023_banner_Boston.jpg|300px|thumbnail|right| [https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_program.html 2023 JMM/AMS Tensor Reps & Analytics] ]]
+
 
* '''Date/Time''':  
+
* ''Organizers'': [https://www.isu.edu/math/people/tenure/ Maryam Bagherian & Emanuele Zappala (Idaho State University)]
** [https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_program_wednesday.html#2270:SS19A '''Session 1 (2270:SS19A)''': Wed Jan. 4, 2023, 8:00AM - 12:00PM], [https://www.timeanddate.com/time/zones/et US Eastern time zone (GMT-4)]
+
 
** [https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_program_wednesday.html#2270:SS19B '''Session 2 (2270:SS19B)''': Wed Jan. 4, 2023, 1:00PM - 6:00PM], [https://www.timeanddate.com/time/zones/et US Eastern time zone (GMT-4)]
+
* ''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.
** [https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_program_thursday.html#2270:SS19C '''Session 3 (2270:SS19C)''': Thu Jan. 5, 2023, 8:00AM - 12:00PM], [https://www.timeanddate.com/time/zones/et US Eastern time zone (GMT-4)]
 
* '''Venue''': [https://www.massconvention.com/about-us/contact-us/john-b-hynes-veterans-memorial-convention-center Hynes Convention Center], [https://www.signatureboston.com/hynes/floor-plans-and-specs/space-finder/hynes-meeting-room-206 Room 206].
 
* '''Registration''': [https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_reg Meeting Registration is required].
 
* '''Conference''': [https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_program.html 2023 Joint Mathematics Meeting (JMM)], session 2270:SS19 (A, B, C).
 
* '''Session Format''':  Three half-day sessions, each 5-hours with 10 20+5+5 minute talks.
 
* '''Equipment''': Computer projector and screen with HDMI connection. Session room does not include overhead transparency projectors, computers/laptops, blackboards or whiteboards.
 
* '''AMS Special Session Manual''': [http://www.ams.org/meetings/meet-specialsessionmanual AMS/JMM Session Manual].
 
* [https://meetings.ams.org/math/jmm2023/cfp.cgi '''Paper Abstract Submission'''], click on the AMS (American Mathematical Society) “BEGIN A SUBMISSION” button. The '''deadline for submission of invited abstracts has passed''' (September 13, 2022). Interested presenters are encouraged to [https://meetings.ams.org/math/jmm2023/cfp.cgi submit contributed talk abstracts].
 
* [https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_policy JMM'23 policies/procedures]
 
* [https://myumi.ch/DJ75R Session URL]: https://myumi.ch/DJ75R.
 
  
 
== Abstract Submission==
 
== Abstract Submission==
The organizers of this special session on ''Tensor Representation, Completion, Modeling and Analytics of Complex Data'' invite abstract submission for at the upcoming Joint Mathematics Meetings in Boston, MA, January 4-7, 2023 (Wednesday-Saturday). This 3-part session will meet on Wed-Thu January 4-5, 2023. Submission of abstracts for invited talks [20+5+5]-minute are welcome until the deadline, September 13, 2022.
 
The [https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_intro JMM 2023 homepage] contains a wealth of information about the JMM 2023 conference and this special session. Here are some AMS policies concerning special sessions:
 
  
*Each speaker *must* submit an abstract before a talk can be scheduled. [https://meetings.ams.org/math/jmm2023/cfp.cgiAbstracts must be submitted electronically through the AMS portal], then click on the AMS (American Mathematical Society) “BEGIN A SUBMISSION” button.
+
* Abstract Submission: [https://meetings.ams.org/math/jmm2026/cfp.cgi Abstracts must be submitted through the AMS portal].
 +
* Submission Period: July 10, 2025 -- Tuesday, September 9, 2025
 +
 
 +
 
 +
== [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Session/13818  Session Program]==
 +
 
 +
'''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
  
* Please note the deadline of *September 13* for abstract submission. We strongly encourage you to submit your abstract at least a week before that deadline in order to avoid any last-minute problems.  When the "Conclude Submission" button is clicked, an email will be sent to the Presenting Author's email and the Submitter's email confirming receipt of the submission.  Please note your abstract is *NOT* submitted until you click the "Conclude Submission" button.
+
* [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]
* Your talk must be delivered in person, via a computer projection system.  Please note that overhead projectors will not be provided for sessions. Also, the meeting rooms will not have blackboards or whiteboards.
 
* The AMS does not pay any expenses of faculty attending special sessions. However, [http://www.ams.org/student-travel PhD students are encouraged to apply for travel funding from AMS]. The AMS also has a [http://www.ams.org/profession/opportunities/meetings-child-care-grants program of child care grants for all JMM attendees (faculty, students, etc.)]. Note that these programs have their own deadlines and application procedures.
 
* Everyone who attends the meeting is required to pay a registration fee.
 
  
== Program==
+
* 10:30 AM: Tensor denoising, Harm Derksen, Northeastern University, Boston, MA
=== Session 1 (SS19A, Wed 1/4/23, 8AM-12PM) ===
 
[https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_program_wednesday.html#2270:SS19A Session 1 (2270:SS19A): AMS Special Session on Tensor Representation, Completion, Modeling and Analytics of Complex Data I; Date: Wednesday, January 4, 2023, Time: 8:00AM – 12:00 PM, Location: Hynes Convention Center - 206]
 
  
<center>
+
* 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
{| class="wikitable"
 
|-
 
! Time [https://www.timeanddate.com/time/zones/et US ET timezone (GMT-5)] || Presenter/Affiliation || Title || Classification - Abstract ID
 
|-
 
| 8:00AM ||Mason A Porter / UCLA || ''Node Centralities in Multilayer Networks'' || 15A99-17029
 
|-
 
| 8:30AM || Alex Townsend / Cornell || ''Why are so many matrices and tensors compressible?'' || 15-02-20266
 
|-
 
| 9:00AM || Joe Kileel / Texas || ''Estimation in Mixture Models Through Implicit Tensor Decomposition'' || 65F99-21426
 
|-
 
| 9:30AM || Luke Oeding / Auburn University || ''Dimensions of Restricted Secant Varieties of Grassmannians'' || 15A69-22223
 
|-
 
| 10:00AM || John Blake Temple / UC-Davis || ''On the regularity implied by the assumptions of geometry'' ||  53B30-22415
 
|-
 
| 10:30 AM || Yizhe Zhu / UC-Irvine || ''Non-backtracking spectra of random hypergraphs and community detection'' || 60C05-18313
 
|-
 
| 11:00 AM || Bruno N. de Oliveira / University of Miami || ''Abundance of symmetric differential tensors, birational geometry of surfaces and hypersurfaces in <math>\mathbb{P}^3</math>'' || 14J60-22507
 
|-
 
| 11:30 AM || Ivo Dinov / Michigan || ''Quantum Physics, Data Science, Tensor Linear Modeling, and Spacekime Analytics'' || 81Q65-14771
 
|}
 
</center>
 
  
=== Session 2 (Wed 1/4/23, 1PM-6PM) ===
+
* 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
[https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_program_wednesday.html#2270:SS19B Session 2 (2270:SS19B)]: AMS Special Session on Tensor Representation, Completion, Modeling and Analytics of Complex Data II   
 
Date: Wednesday, January 4, 2023, Time: 1:00 – 6:00 PM, Location: Hynes Convention Center - 206 
 
 
<center>
 
{| class="wikitable"
 
|-
 
! Time [https://www.timeanddate.com/time/zones/et US ET timezone (GMT-5)] || Presenter/Affiliation || Title || Classification - Abstract ID
 
|-
 
| 1:00PM || Anru Zhang / Duke || ''Tensor Learning in 2020s: Methodology, Theory, and Applications'' || 62H99-17081
 
|-
 
| 1:30PM || Giuseppe Cotardo / Virginia Tech|| ''The Tensor Rank in Coding Theory'' || 03D15-20548
 
|-
 
| 2:00PM || Edinah Koffi Gnang / Johns Hopkins || ''On the complexity of hypermatrix equivalence'' || 15-02-20813
 
|-
 
| 2:30PM || Hirotachi (Hiro) Abo  / Idaho || ''Algebro-geometric approaches to the tensor eigenproblem'' || 15A69-20858
 
|-
 
| 3:00PM || Oscar Fabian Lopez / Florida Atlantic || ''Zero-Truncated Poisson Regression for Sparse Multiway Count Data Corrupted by False Zeros'' ||  15B99-21425
 
|-
 
| 3:30PM ||  Anna Konstorum / Yale || ''Optimizing component recovery in CP decomposition of immunology data'' || 92-08-21503
 
|-
 
| 4:00PM <span style="color:#FFFFFF; background:#ff0000">(Talk Cancelled due to Visa Complications)</span> || Tianyi Shi  / Lawrence Berkeley National Laboratory || ''Tensor equation methods for electron correlation energy computation'' || 65F99-21870
 
|-
 
| 4:30PM || Jonathan Gryak / CUNY || ''Tensor Denoising via Amplification and Stable Rank Methods'' || 15A72-22096
 
|-
 
| 5:00PM || M. Alex O. Vasilescu / UCLA || ''Kernel Tensor Factor Analysis'' || 15-06-22645
 
|}
 
</center>
 
  
=== Session 3 (Thu 1/5/23, 8AM-11:30AM) ===
+
==Talk: [https://meetings.ams.org/math/jmm2026/meetingapp.cgi/Paper/51462 Kime-Phase Analytics: A Mathematical Framework for Complex-Time Representation of Longitudinal Processes]==
[https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_program_thursday.html#2270:SS19C Session 3 (2270:SS19C)]: AMS Special Session on Tensor Representation, Completion, Modeling and Analytics of Complex Data III   
 
Date: Thursday, January 5, 2023, Time: 8:00 – 11:30 AM, Location: Hynes Convention Center - 206 
 
 
<center>
 
{| class="wikitable"
 
|-
 
! Time [https://www.timeanddate.com/time/zones/et US ET timezone (GMT-5)] || Presenter/Affiliation || Title || Classification - Abstract ID
 
|-
 
| 8:00AM || Elina Robeva / UBC || ''High-order Cumulants for Learning Linear Non-Gaussian Causal Models'' ||  62H22-18002
 
|-
 
| 8:30AM ||  Zhen Dai / Chicago || ''From tensor rank to the inversion of a complex matrix'' ||  65F05-19986
 
|-
 
| 9:00AM ||  Eric Evert / KU Leuven || ''Best low rank approximations of positive definite tensors'' || 15-02-20965
 
|-
 
| 9:30AM || Hajer Bouzaouache / University Tunis, El Manar || ''On the Importance of Tensor representation in the stability analysis of Nonlinear systems'' || 93D05-22326
 
|-
 
| 10:00AM ||  Rachel Minster / Wake Forest University || ''Randomized Parallel Algorithms for Tucker Decompositions'' || 65F99-21497 
 
|-
 
| 10:30AM || Harm Derksen / Northeastern || ''Tensor Denoising via Amplification and Stable Rank Methods'' || 15-02-20663
 
|-
 
| 11:00AM || Maryam Bagherian / Michigan || ''Tensor Recovery Under Metric Learning Constraints'' || 68U99-18227
 
|}
 
</center>
 
  
==Speakers, Titles, and Abstracts==
+
* ''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]
  
* [https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_program_wednesday.html#2270:SS19A Session 1 (2270:SS19A): AMS Special Session on Tensor Representation, Completion, Modeling and Analytics of Complex Data I; Date: Wednesday, January 4, 2023, Time: 8:00AM – 12:00 PM, Location: Hynes Convention Center - 206].
+
* ''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.
* [https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_program_wednesday.html#2270:SS19A Session 1 (2270:SS19A): AMS Special Session on Tensor Representation, Completion, Modeling and Analytics of Complex Data I; Date: Wednesday, January 4, 2023, Time: 8:00AM – 12:00 PM, Location: Hynes Convention Center - 206].
 
* [https://www.jointmathematicsmeetings.org/meetings/national/jmm2023/2270_program_thursday.html#2270:SS19C Session 3 (2270:SS19C): AMS Special Session on Tensor Representation, Completion, Modeling and Analytics of Complex Data III; Date: Thursday, January 5, 2023, Time: 8:00 – 11:30 AM, Location: Hynes Convention Center - 206].
 
  
==Resources==
 
* [https://wiki.socr.umich.edu/images/5/53/Tensor_InvitedSpecialSession_JMM_2023_Boston_Flier.pdf Session Flier]
 
* Slides/papers ...
 
** [https://wiki.socr.umich.edu/images/0/0b/Dinov_Spacekime_JMM_2023_Boston.pdf ''Quantum Physics, Data Science, Tensor Linear Modeling, and Spacekime Analytics'' (Ivo Dinov)]
 
** [https://wiki.socr.umich.edu/images/e/e5/MaryamBagherian_JMM_2023_Slides.pdf ''Tensor recovery under metric learning constraints'' (Maryam Bagherian)]
 
** [https://wiki.socr.umich.edu/images/3/3a/Kileel_JMM_2023_Tensor_Slides.pdf ''Estimation in Mixture Models through Implicit Tensor Decompositions (Joe Kileel)]
 
** [https://wiki.socr.umich.edu/images/d/dc/Bidleman-Oeding_JMM_talk.pdf ''Dimensions of Restricted Secant Varieties of Grassmannians'' (Luke Oeding)]
 
** [https://wiki.socr.umich.edu/images/a/a9/Yizhe_Zhu_JMM2023.pdf ''Non-backtracking spectra of random hypergraphs and community detection'' (Yizhe Zhu)]
 
** [https://wiki.socr.umich.edu/images/a/ac/JMM_Giuseppe_Cotardo.pdf ''The Tensor Rank in Coding Theory'' (Giuseppe Cotardo)]
 
** [https://wiki.socr.umich.edu/images/4/4a/Mason_Porter_Multilayer-centrality-JMM-Jan2023.pdf ''Node Centralities in Multilayer Networks'' (Mason Porter)]
 
** [https://wiki.socr.umich.edu/images/d/d4/Oscar_Lopez_ZTP_JMM_2023.pdf ''Zero-Truncated Poisson Regression for Sparse Multiway Count Data Corrupted by False Zeros'' (Oscar Lopez)]
 
** [https://wiki.socr.umich.edu/images/1/16/Hiro_Abo_BostonJMM_2023.pdf ''Algebro-geometric approaches to the tensor eigenproblem'' (Hiro Abo)]
 
** [https://wiki.socr.umich.edu/images/3/3f/Gryak_JMM23_Slides.pdf ''Tensor Denoising via Amplification and Stable Rank Methods'' (Jonathan Gryak)]
 
** [https://arxiv.org/abs/2211.13028 ''Parallel Randomized Tucker Decomposition Algorithms'' (Rachel Minster's arXiv paper)]
 
** [https://doi.org/10.1137/19M1272494 ''Algebraic Methods for Tensor Data'' (Harm Derksen's SIAM 2021 paper)]
 
** [https://wiki.socr.umich.edu/images/5/5e/Minster_JMM23.pdf ''Randomized Algorithms for Tensor Decompositions in the Tucker Format'' (Rackel Misnter)]
 
** [https://wiki.socr.umich.edu/images/6/68/Temple_TalkAMS_BostonJan2023.pdf ''On the Regularity Implied by the Hypotheses of Geometry'' (Blake Temple)]
 
** [https://wiki.socr.umich.edu/images/7/7c/Anna_Konstorum_JMM23_Slides.pdf ''Optimizing component recovery in CP decomposition of immunology data'' (Anna Konstorum)]
 
** [https://wiki.socr.umich.edu/images/e/e6/Zhen_Dai_TensorRankInversion_JMM_2023.pdf ''From Tensor Rank to the Inversion of a Complex Matrix'' (Zhen Dai)]
 
** ...
 
  
  
 
<hr>
 
<hr>
 
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{{translate|pageName=https://wiki.socr.umich.edu/index.php?title=SOCR_JMM_2026}}

Latest revision as of 18:33, 30 December 2025

SOCR News & Events: 2026 JMM/AMS Special Session on Mathematical Foundation of Machine Learning

Session Overview

  • 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


Session Program

Location: Room 204C (Level 2, Walter E. Washington Convention Center)

Session: 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
  • 10:30 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: 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), 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.





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