SOCR News APS GPS 2026

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.

Short Course (Tutorial)

• Event: Complex-Time (Kime) Representation of Spatiotemporal Processes and Spacekime Analytics
• Date/Time: Sunday, March 15, 2026, Time: 8:00am-12:00pm
• Place: 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.

• Organizer: Ivo D. Dinov

• 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.

Sessions

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

• Focus Session: Session MAR-J57
• Sponsoring Unit: GMED
• Date/Time: Tuesday, March 17, 2026, 3:30-6:00 PM CT
• Room: Convention Center Bluebird 2A
• Session Chair: Ivo Dinov

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: [Title TBD]	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

...tbc...

Resources

• Dinov's Numerical and Analytical Complex-time Transformations of Longitudinal Processes and Spacekime Analytics slides
• Shen's Probabilistic Symmetry, Variable Exchangeability, and Deep Network Learning Invariance and Equivariance slides

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