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== [[SOCR_News | SOCR News & Events]]: '''2026 APS Global Physics Summit (GPS)''' ==
 
== [[SOCR_News | SOCR News & Events]]: '''2026 APS Global Physics Summit (GPS)''' ==
  
−
==2025 APS Global Physics Summit==
+
==2026 APS Global Physics Summit==
  
 
[[Image:APS_2025_Summit.png|500px|thumbnail|center| [https://summit.aps.org/ 2026 APS Global Summit] ]]
 
[[Image:APS_2025_Summit.png|500px|thumbnail|center| [https://summit.aps.org/ 2026 APS Global Summit] ]]
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The SOCR group is organizing a Time-Complexity and Inferential Uncertainty (TCIU), Spacekime Analytics, short course, organize sessions, and present invited talks.
 
The SOCR group is organizing a Time-Complexity and Inferential Uncertainty (TCIU), Spacekime Analytics, short course, organize sessions, and present invited talks.
 +
 +
===[https://summit.aps.org/schedule/ APS GPS 2026 Conference Program/Schedule]===
  
 
==Short Course (Tutorial) ==
 
==Short Course (Tutorial) ==
  
−
* Event: Complex-Time (Kime) Representation of Spatiotemporal Processes and Spacekime Analytics
+
===Logistics===
−
* Date/Time: March 15, 2026, Time: 8:00am-12:00pm
+
 
−
* Place: [https://denverconvention.com/ Colorado Convention Center, Room 702]
+
* ''Event'': [https://summit.aps.org/smt/2026/events/MAR-SH03 Complex-Time (Kime) Representation of Spatiotemporal Processes and Spacekime Analytics]
 +
* ''Sponsor'': [https://engage.aps.org/dcomp/home APS Division of Computational Physics (DCOMP)]
 +
* ''Date/Time'': Sunday, March 15, 2026, Time: 8:00am-12:00pm
 +
* ''Place'': [https://denverconvention.com/ Colorado Convention Center, Room 702]
 +
* ''Price'': Students: $10; Postdocs/fellows: $25; Academic faculty: $50; Federal Employees: $50; Industry: $75; International Participants: $30.
 +
 
 +
* ''Who should attend?'' This short course may be appropriate for undergraduate seniors, graduate students, postdocs and fellows, research scientists, and early career faculty interested in mathematical representation, computational modeling, statistical inference, and AI. While all content will be broadly accessible, a background in mathematical physics and a strong interest in advanced statistical learning methods and AI applications will be beneficial. Participants motivated to explore the intersection of statistical physics, computing, and AI will gain the most from learning spacekime analytics and completing this course. Additionally, some knowledge of statistical limit theory, functional analysis, and experience with R/Rmd programming may be helpful. All interested students, fellows, early-career faculty, and members of [https://engage.aps.org/gds/home GDS] and [https://engage.aps.org/dcomp/home DCOMP] are encouraged to attend.
 +
 
 +
* ''Organizer'': [https://socr.umich.edu/people/dinov/ Ivo D. Dinov]
 +
 
 +
===Description===
 +
* ''Description'': Fundamentally new representations of complex systems are critical in advancing scientific discovery, transdisciplinary research, and blended data-driven human-machine intelligence. This PEP course will present the fundamentals of complex-time (kime) representation, which interfaces quantum mechanics, mathematical statistics, and AI applications. Specifically, the course will demonstrate computational inference, scientific visualization, rigorous statistical modeling of large datasets, understanding complex temporally dynamic processes, and spacekime analytics.
 +
 
 +
: The course will provide an active learning environment to build competency in the following areas: methods for transforming time-series to kime-surfaces, which are much richer computational objects; mathematical foundations of spacekime analytics; and AI algorithms with applications. A set of end-to-end R-markdown notebooks will provide reproducible and independently validated protocols for data ingestion, mathematical representation and scientific visualization of longitudinal time-courses mapped as 2D parametric manifolds (kime-surfaces), model-based statistical inference, and model-free AI analytics.
 +
 
 +
: A ''heuristic overview'' of complex-time representation, kime-phase tomography, and spacekime analytics.
 +
:: [https://socr.umich.edu/spacekime/img/Spacekime_TCIU_Podcast1_2025.mp3 A brief, 7-minute, spacekime ''podcast'' (audio)].
 +
:: [https://socr.umich.edu/spacekime/img/ComplexTimeReps_KPT_Spacekime_FieldEquations_video.mp4 A short, 5-minute ''video'' describing complex-time representation, kime-phase tomography (KPT), and spacekime gravitational field equations].
 +
 
 +
===Course Coverage===
 +
 
 +
* '''Part I (8:00-8:55 AM)''': Course overview with a discussion of the basic mathematical-physics principles underlying the complex-time (''kime'') representation of repeated measurement longitudinal data.
 +
:: [https://socr.umich.edu/docs/uploads/2026/APS_GPS__2026_KPT_long_Dinov_Tutorial_P1.pdf Kime-Phase Tomography (KPT) (slidedeck)],
 +
 
 +
* '''Part II (9:00-9:55 AM)''': Translation of fundamental quantum mechanics principles into statistical inference models of time-varying processes. Specifically, we will demonstrate generalizing the classical 4D spatiotemporal sampling to a 5D spacekime manifold, where the phase of complex-time (an extra degree of freedom) encodes repeated random sampling at fixed spatiotemporal locations.
 +
 
 +
:: [https://www.socr.umich.edu/TCIU/HTMLs/Kepler_KPT_Implementation_V5.html Kime-Phase Tomography: (V.5) KPT Validation with Kepler Stellar Light Curves],
 +
:: [https://www.socr.umich.edu/TCIU/HTMLs/TCIU_SK_Appendix04_Spacekime_Gravity_FieldEquations_V2.html 5D Spacekime Einstein-Hilbert Gravitational Field Equations].
 +
 
 +
* '''Part III (10:00-10:55 AM)''': Alternative strategies for transforming observed time-series into kime-surfaces, which are richer, computationally tractable, objects amenable to tensor-based statistical linear modeling and AI model-free inference.
 +
:: [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_MappingLongitudinalTimeseries_2_Kimesurfaces.html Strategies for Mapping Repeated Measurement Longitudinal Data (Time-series) to 2D Manifolds (Kimesurfaces)].
 +
 
 +
* '''Part IV (11:00-11:55 AM)''': Simulated and real neuroimaging and macroeconomics data to demonstrate applications of spacekime analytics.
 +
:: [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_Basic_SpacekimePredictiveAnalytics.html TCIU/Spacekime Analytics Tutorial: Basic TCIU Protocol for Predictive Spacekime Analytics using Longitudinal Data].
 +
 
 +
: [https://tciu.predictive.space/ Additional theoretical developments, experimental demonstrations, computational algorithms, and data science applications are available on the '''spacekime/TCIU website'''].
 +
 
 +
: [https://socr.umich.edu/DSPA2/DSPA2_notes/01_Introduction.html#2_Foundations_of_R More information about editing, interpreting, knitting/compiling and running ''R''-markdown electronic notebooks (*.Rmd) is available on the SOCR DSPA2 site].
 +
 
 +
===Spacekime analytics themes===
 +
 
 +
* 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
 +
 
 +
* 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,
 +
 
 +
* Constructing 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.
 +
 
 +
===Expected Learning Outcomes===
  
−
== Sessions ===
+
Participants will learn and understand the rationale for, and the duality between, classical representation of repeated-measurement spacetime observations (time-series) and their spacekime analytics counterparts – complex-time representation (parametric manifolds).
  
−
...tbc...
+
== Sessions ==
 +
 
 +
===Session [https://summit.aps.org/smt/2026/events/MAR-J57 MAR-J57: Physical, Statistical and Al modeling of Spatiotemporal Medical Imaging]===
 +
 
 +
* '''Focus Session''': [https://summit.aps.org/smt/2026/events/MAR-J57 Session MAR-J57]
 +
* '''Sponsoring Unit''': [https://engage.aps.org/gmed/home GMED]
 +
* '''Date/Time''': Tuesday, March 17, 2026, 3:30-6:00 PM CT
 +
* '''Room''': [https://denverconvention.com/plan-your-event/event-space/bluebird-ballroom Convention Center Bluebird 2A]
 +
* '''PDF Agenda''': [https://socr.umich.edu/docs/uploads/2026/APS_GPS_Mar-J57_Session_Agenda.pdf MAR-J57 Session Program (PDF)]
 +
* '''Session Chairs''': [https://www.socr.umich.edu/people/dinov/ Ivo Dinov] & [https://radiationoncology.pitt.edu/people/sarah-ashmeg-phd-dabr Sarah Ashmeg]
 +
 
 +
{| class="wikitable"
 +
|-
 +
! Time
 +
! Presentation
 +
! Speaker
 +
|-
 +
| Tuesday, March 17, 2026<br />3:30 PM – 4:06 PM
 +
| MAR-J57.00001: AMPERE: A Global Physics Framework for Advancing Medical Physics Research, Education, and Workforce Development
 +
| Stephen M Avery (Invited)
 +
|-
 +
| Tuesday, March 17, 2026<br />4:06 PM – 4:42 PM
 +
| MAR-J57.00002: Complex Time and the Principle of Least Entropic Purpose
 +
| Michael C Parker (Invited)
 +
|-
 +
| Tuesday, March 17, 2026<br />4:42 PM – 5:18 PM
 +
| MAR-J57.00003: Exploring the Relevance of Thermal Spacetime Quantum Mechanics to Data Science and Medical Imaging
 +
| Russell Thompson (Invited)
 +
|-
 +
| Tuesday, March 17, 2026<br />5:18 PM – 5:30 PM
 +
| MAR-J57.00004: Second-order Statistical Image Texture Features from Spectral X-ray Images when using Photon-Counting Spectral Detectors
 +
| Oliver Namuwonge, Diego Andrade, Mini Das
 +
|-
 +
| Tuesday, March 17, 2026<br />5:30 PM – 5:42 PM
 +
| MAR-J57.00005: Fractal Diffusion-Reaction Framework for FLASH-UHDR Tissue Sparing
 +
| Neda Valizadeh Gendeshmin, Robabeh Rahimi, Ramin Abolfazl
 +
|-
 +
| Tuesday, March 17, 2026<br />5:42 PM – 5:54 PM
 +
| MAR-J57.00006: Estimating Uncertainty of a Deep Learning-Based Breast Cancer Risk Prediction Model Using Test-Time Augmentations
 +
| Zan Klanecek, Yao Kuan Wang, Tobias Wagner, Lesley Cockmartin, Nicholas Marshall, Andrej Studen, Katja Jarm, Mateja Krajc, Milos Vrhovec, Hilde Bosmans, Ali Deatsch, Robert Jeraj
 +
|}
  
 
==Talks==
 
==Talks==
  
−
...tbc...
+
* '''Session''': [https://summit.aps.org/smt/2026/events/MAR-J45 MAR-J45], time: 04:54p - 05:06p
 +
* '''Talk Title''': [https://summit.aps.org/events/MAR-J45/6 Kime-Phase Tomography and Manifold Representation of Longitudinal Processes over a Complex-Time Domain]
 +
* '''Presenter''': [https://www.socr.umich.edu/people/dinov/ Ivo D. Dinov] (presenter), Yueyang Shen, Bojko N Bakalov
 +
* '''Slides''': [https://socr.umich.edu/docs/uploads/2026/Dinov_short_ID_APS_GPS__2026_KPT.pdf Dinov's (short_ID_APS_GPS__2026_KPT) Slidedeck (PDF)]
 +
* '''Session Logistics''': Tue. March 17, 03:30p - 06:06p, Convention Center, Meeting Room 605
 +
 
 +
==Other Sessions==
  
 +
* Sunday 3/15 at 6:00pm (location: TBD): GDS (off-site) executive committee meeting
 +
* GDS GPS general meeting: Tue, March 17, 6:45 p.m. Convention Center, Meeting Room 507, EVT-AA58, GDS Unit Business Meeting
  
 
==Resources==
 
==Resources==

Latest revision as of 23:01, 15 March 2026

SOCR News & Events: 2026 APS Global Physics Summit (GPS)

2026 APS Global Physics Summit

2026 APS Global Summit

The annual American Physical Society's March Meetings are scientific research conferences convening over 14,000 physicists and students from around the globe to connect and collaborate across academia, industry, and major labs. The 2026 APS Global Physics Summit will take place at Colorado Convention Center, Denver Colorado. The APS 2026 Summit Program is here.

The SOCR group is organizing a Time-Complexity and Inferential Uncertainty (TCIU), Spacekime Analytics, short course, organize sessions, and present invited talks.

APS GPS 2026 Conference Program/Schedule

Short Course (Tutorial)

Logistics

  • Who should attend? This short course may be appropriate for undergraduate seniors, graduate students, postdocs and fellows, research scientists, and early career faculty interested in mathematical representation, computational modeling, statistical inference, and AI. While all content will be broadly accessible, a background in mathematical physics and a strong interest in advanced statistical learning methods and AI applications will be beneficial. Participants motivated to explore the intersection of statistical physics, computing, and AI will gain the most from learning spacekime analytics and completing this course. Additionally, some knowledge of statistical limit theory, functional analysis, and experience with R/Rmd programming may be helpful. All interested students, fellows, early-career faculty, and members of GDS and DCOMP are encouraged to attend.

Description

  • Description: Fundamentally new representations of complex systems are critical in advancing scientific discovery, transdisciplinary research, and blended data-driven human-machine intelligence. This PEP course will present the fundamentals of complex-time (kime) representation, which interfaces quantum mechanics, mathematical statistics, and AI applications. Specifically, the course will demonstrate computational inference, scientific visualization, rigorous statistical modeling of large datasets, understanding complex temporally dynamic processes, and spacekime analytics.
The course will provide an active learning environment to build competency in the following areas: methods for transforming time-series to kime-surfaces, which are much richer computational objects; mathematical foundations of spacekime analytics; and AI algorithms with applications. A set of end-to-end R-markdown notebooks will provide reproducible and independently validated protocols for data ingestion, mathematical representation and scientific visualization of longitudinal time-courses mapped as 2D parametric manifolds (kime-surfaces), model-based statistical inference, and model-free AI analytics.
A heuristic overview of complex-time representation, kime-phase tomography, and spacekime analytics.
A brief, 7-minute, spacekime podcast (audio).
A short, 5-minute video describing complex-time representation, kime-phase tomography (KPT), and spacekime gravitational field equations.

Course Coverage

  • Part I (8:00-8:55 AM): Course overview with a discussion of the basic mathematical-physics principles underlying the complex-time (kime) representation of repeated measurement longitudinal data.
Kime-Phase Tomography (KPT) (slidedeck),
  • Part II (9:00-9:55 AM): Translation of fundamental quantum mechanics principles into statistical inference models of time-varying processes. Specifically, we will demonstrate generalizing the classical 4D spatiotemporal sampling to a 5D spacekime manifold, where the phase of complex-time (an extra degree of freedom) encodes repeated random sampling at fixed spatiotemporal locations.
Kime-Phase Tomography: (V.5) KPT Validation with Kepler Stellar Light Curves,
5D Spacekime Einstein-Hilbert Gravitational Field Equations.
  • Part III (10:00-10:55 AM): Alternative strategies for transforming observed time-series into kime-surfaces, which are richer, computationally tractable, objects amenable to tensor-based statistical linear modeling and AI model-free inference.
Strategies for Mapping Repeated Measurement Longitudinal Data (Time-series) to 2D Manifolds (Kimesurfaces).
  • Part IV (11:00-11:55 AM): Simulated and real neuroimaging and macroeconomics data to demonstrate applications of spacekime analytics.
TCIU/Spacekime Analytics Tutorial: Basic TCIU Protocol for Predictive Spacekime Analytics using Longitudinal Data.
Additional theoretical developments, experimental demonstrations, computational algorithms, and data science applications are available on the spacekime/TCIU website.
More information about editing, interpreting, knitting/compiling and running R-markdown electronic notebooks (*.Rmd) is available on the SOCR DSPA2 site.

Spacekime analytics themes

  • 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
  • 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,
  • Constructing 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.

Expected Learning Outcomes

Participants will learn and understand the rationale for, and the duality between, classical representation of repeated-measurement spacetime observations (time-series) and their spacekime analytics counterparts – complex-time representation (parametric manifolds).

Sessions

Session MAR-J57: Physical, Statistical and Al modeling of Spatiotemporal Medical Imaging

Time Presentation Speaker
Tuesday, March 17, 2026
3:30 PM – 4:06 PM 		MAR-J57.00001: AMPERE: A Global Physics Framework for Advancing Medical Physics Research, Education, and Workforce Development Stephen M Avery (Invited)
Tuesday, March 17, 2026
4:06 PM – 4:42 PM 		MAR-J57.00002: Complex Time and the Principle of Least Entropic Purpose Michael C Parker (Invited)
Tuesday, March 17, 2026
4:42 PM – 5:18 PM 		MAR-J57.00003: Exploring the Relevance of Thermal Spacetime Quantum Mechanics to Data Science and Medical Imaging Russell Thompson (Invited)
Tuesday, March 17, 2026
5:18 PM – 5:30 PM 		MAR-J57.00004: Second-order Statistical Image Texture Features from Spectral X-ray Images when using Photon-Counting Spectral Detectors Oliver Namuwonge, Diego Andrade, Mini Das
Tuesday, March 17, 2026
5:30 PM – 5:42 PM 		MAR-J57.00005: Fractal Diffusion-Reaction Framework for FLASH-UHDR Tissue Sparing Neda Valizadeh Gendeshmin, Robabeh Rahimi, Ramin Abolfazl
Tuesday, March 17, 2026
5:42 PM – 5:54 PM 		MAR-J57.00006: Estimating Uncertainty of a Deep Learning-Based Breast Cancer Risk Prediction Model Using Test-Time Augmentations Zan Klanecek, Yao Kuan Wang, Tobias Wagner, Lesley Cockmartin, Nicholas Marshall, Andrej Studen, Katja Jarm, Mateja Krajc, Milos Vrhovec, Hilde Bosmans, Ali Deatsch, Robert Jeraj

Talks

Other Sessions

  • Sunday 3/15 at 6:00pm (location: TBD): GDS (off-site) executive committee meeting
  • GDS GPS general meeting: Tue, March 17, 6:45 p.m. Convention Center, Meeting Room 507, EVT-AA58, GDS Unit Business Meeting

Resources

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